Sample records for relative prediction errors
-
NASA Astrophysics Data System (ADS)
Duan, Wansuo; Zhao, Peng
2017-04-01
Within the Zebiak-Cane model, the nonlinear forcing singular vector (NFSV) approach is used to investigate the role of model errors in the "Spring Predictability Barrier" (SPB) phenomenon within ENSO predictions. NFSV-related errors have the largest negative effect on the uncertainties of El Niño predictions. NFSV errors can be classified into two types: the first is characterized by a zonal dipolar pattern of SST anomalies (SSTA), with the western poles centered in the equatorial central-western Pacific exhibiting positive anomalies and the eastern poles in the equatorial eastern Pacific exhibiting negative anomalies; and the second is characterized by a pattern almost opposite the first type. The first type of error tends to have the worst effects on El Niño growth-phase predictions, whereas the latter often yields the largest negative effects on decaying-phase predictions. The evolution of prediction errors caused by NFSV-related errors exhibits prominent seasonality, with the fastest error growth in the spring and/or summer seasons; hence, these errors result in a significant SPB related to El Niño events. The linear counterpart of NFSVs, the (linear) forcing singular vector (FSV), induces a less significant SPB because it contains smaller prediction errors. Random errors cannot generate a SPB for El Niño events. These results show that the occurrence of an SPB is related to the spatial patterns of tendency errors. The NFSV tendency errors cause the most significant SPB for El Niño events. In addition, NFSVs often concentrate these large value errors in a few areas within the equatorial eastern and central-western Pacific, which likely represent those areas sensitive to El Niño predictions associated with model errors. Meanwhile, these areas are also exactly consistent with the sensitive areas related to initial errors determined by previous studies. This implies that additional observations in the sensitive areas would not only improve the accuracy of the initial field but also promote the reduction of model errors to greatly improve ENSO forecasts.
-
Schiffer, Anne-Marike; Ahlheim, Christiane; Wurm, Moritz F.; Schubotz, Ricarda I.
2012-01-01
Influential concepts in neuroscientific research cast the brain a predictive machine that revises its predictions when they are violated by sensory input. This relates to the predictive coding account of perception, but also to learning. Learning from prediction errors has been suggested for take place in the hippocampal memory system as well as in the basal ganglia. The present fMRI study used an action-observation paradigm to investigate the contributions of the hippocampus, caudate nucleus and midbrain dopaminergic system to different types of learning: learning in the absence of prediction errors, learning from prediction errors, and responding to the accumulation of prediction errors in unpredictable stimulus configurations. We conducted analyses of the regions of interests' BOLD response towards these different types of learning, implementing a bootstrapping procedure to correct for false positives. We found both, caudate nucleus and the hippocampus to be activated by perceptual prediction errors. The hippocampal responses seemed to relate to the associative mismatch between a stored representation and current sensory input. Moreover, its response was significantly influenced by the average information, or Shannon entropy of the stimulus material. In accordance with earlier results, the habenula was activated by perceptual prediction errors. Lastly, we found that the substantia nigra was activated by the novelty of sensory input. In sum, we established that the midbrain dopaminergic system, the hippocampus, and the caudate nucleus were to different degrees significantly involved in the three different types of learning: acquisition of new information, learning from prediction errors and responding to unpredictable stimulus developments. We relate learning from perceptual prediction errors to the concept of predictive coding and related information theoretic accounts. PMID:22570715
-
When is an error not a prediction error? An electrophysiological investigation.
Holroyd, Clay B; Krigolson, Olave E; Baker, Robert; Lee, Seung; Gibson, Jessica
2009-03-01
A recent theory holds that the anterior cingulate cortex (ACC) uses reinforcement learning signals conveyed by the midbrain dopamine system to facilitate flexible action selection. According to this position, the impact of reward prediction error signals on ACC modulates the amplitude of a component of the event-related brain potential called the error-related negativity (ERN). The theory predicts that ERN amplitude is monotonically related to the expectedness of the event: It is larger for unexpected outcomes than for expected outcomes. However, a recent failure to confirm this prediction has called the theory into question. In the present article, we investigated this discrepancy in three trial-and-error learning experiments. All three experiments provided support for the theory, but the effect sizes were largest when an optimal response strategy could actually be learned. This observation suggests that ACC utilizes dopamine reward prediction error signals for adaptive decision making when the optimal behavior is, in fact, learnable.
-
Mulej Bratec, Satja; Xie, Xiyao; Schmid, Gabriele; Doll, Anselm; Schilbach, Leonhard; Zimmer, Claus; Wohlschläger, Afra; Riedl, Valentin; Sorg, Christian
2015-12-01
Cognitive emotion regulation is a powerful way of modulating emotional responses. However, despite the vital role of emotions in learning, it is unknown whether the effect of cognitive emotion regulation also extends to the modulation of learning. Computational models indicate prediction error activity, typically observed in the striatum and ventral tegmental area, as a critical neural mechanism involved in associative learning. We used model-based fMRI during aversive conditioning with and without cognitive emotion regulation to test the hypothesis that emotion regulation would affect prediction error-related neural activity in the striatum and ventral tegmental area, reflecting an emotion regulation-related modulation of learning. Our results show that cognitive emotion regulation reduced emotion-related brain activity, but increased prediction error-related activity in a network involving ventral tegmental area, hippocampus, insula and ventral striatum. While the reduction of response activity was related to behavioral measures of emotion regulation success, the enhancement of prediction error-related neural activity was related to learning performance. Furthermore, functional connectivity between the ventral tegmental area and ventrolateral prefrontal cortex, an area involved in regulation, was specifically increased during emotion regulation and likewise related to learning performance. Our data, therefore, provide first-time evidence that beyond reducing emotional responses, cognitive emotion regulation affects learning by enhancing prediction error-related activity, potentially via tegmental dopaminergic pathways. Copyright © 2015 Elsevier Inc. All rights reserved.
-
Error-related negativities elicited by monetary loss and cues that predict loss.
Dunning, Jonathan P; Hajcak, Greg
2007-11-19
Event-related potential studies have reported error-related negativity following both error commission and feedback indicating errors or monetary loss. The present study examined whether error-related negativities could be elicited by a predictive cue presented prior to both the decision and subsequent feedback in a gambling task. Participants were presented with a cue that indicated the probability of reward on the upcoming trial (0, 50, and 100%). Results showed a negative deflection in the event-related potential in response to loss cues compared with win cues; this waveform shared a similar latency and morphology with the traditional feedback error-related negativity.
-
Disrupted prediction-error signal in psychosis: evidence for an associative account of delusions
Corlett, P. R.; Murray, G. K.; Honey, G. D.; Aitken, M. R. F.; Shanks, D. R.; Robbins, T.W.; Bullmore, E.T.; Dickinson, A.; Fletcher, P. C.
2012-01-01
Delusions are maladaptive beliefs about the world. Based upon experimental evidence that prediction error—a mismatch between expectancy and outcome—drives belief formation, this study examined the possibility that delusions form because of disrupted prediction-error processing. We used fMRI to determine prediction-error-related brain responses in 12 healthy subjects and 12 individuals (7 males) with delusional beliefs. Frontal cortex responses in the patient group were suggestive of disrupted prediction-error processing. Furthermore, across subjects, the extent of disruption was significantly related to an individual’s propensity to delusion formation. Our results support a neurobiological theory of delusion formation that implicates aberrant prediction-error signalling, disrupted attentional allocation and associative learning in the formation of delusional beliefs. PMID:17690132
-
Model parameter-related optimal perturbations and their contributions to El Niño prediction errors
NASA Astrophysics Data System (ADS)
Tao, Ling-Jiang; Gao, Chuan; Zhang, Rong-Hua
2018-04-01
Errors in initial conditions and model parameters (MPs) are the main sources that limit the accuracy of ENSO predictions. In addition to exploring the initial error-induced prediction errors, model errors are equally important in determining prediction performance. In this paper, the MP-related optimal errors that can cause prominent error growth in ENSO predictions are investigated using an intermediate coupled model (ICM) and a conditional nonlinear optimal perturbation (CNOP) approach. Two MPs related to the Bjerknes feedback are considered in the CNOP analysis: one involves the SST-surface wind coupling ({α _τ } ), and the other involves the thermocline effect on the SST ({α _{Te}} ). The MP-related optimal perturbations (denoted as CNOP-P) are found uniformly positive and restrained in a small region: the {α _τ } component is mainly concentrated in the central equatorial Pacific, and the {α _{Te}} component is mainly located in the eastern cold tongue region. This kind of CNOP-P enhances the strength of the Bjerknes feedback and induces an El Niño- or La Niña-like error evolution, resulting in an El Niño-like systematic bias in this model. The CNOP-P is also found to play a role in the spring predictability barrier (SPB) for ENSO predictions. Evidently, such error growth is primarily attributed to MP errors in small areas based on the localized distribution of CNOP-P. Further sensitivity experiments firmly indicate that ENSO simulations are sensitive to the representation of SST-surface wind coupling in the central Pacific and to the thermocline effect in the eastern Pacific in the ICM. These results provide guidance and theoretical support for the future improvement in numerical models to reduce the systematic bias and SPB phenomenon in ENSO predictions.
-
Hester, Robert; Murphy, Kevin; Brown, Felicity L; Skilleter, Ashley J
2010-11-17
Punishing an error to shape subsequent performance is a major tenet of individual and societal level behavioral interventions. Recent work examining error-related neural activity has identified that the magnitude of activity in the posterior medial frontal cortex (pMFC) is predictive of learning from an error, whereby greater activity in this region predicts adaptive changes in future cognitive performance. It remains unclear how punishment influences error-related neural mechanisms to effect behavior change, particularly in key regions such as pMFC, which previous work has demonstrated to be insensitive to punishment. Using an associative learning task that provided monetary reward and punishment for recall performance, we observed that when recall errors were categorized by subsequent performance--whether the failure to accurately recall a number-location association was corrected at the next presentation of the same trial--the magnitude of error-related pMFC activity predicted future correction. However, the pMFC region was insensitive to the magnitude of punishment an error received and it was the left insula cortex that predicted learning from the most aversive outcomes. These findings add further evidence to the hypothesis that error-related pMFC activity may reflect more than a prediction error in representing the value of an outcome. The novel role identified here for the insular cortex in learning from punishment appears particularly compelling for our understanding of psychiatric and neurologic conditions that feature both insular cortex dysfunction and a diminished capacity for learning from negative feedback or punishment.
-
Frontal Theta Links Prediction Errors to Behavioral Adaptation in Reinforcement Learning
Cavanagh, James F.; Frank, Michael J.; Klein, Theresa J.; Allen, John J.B.
2009-01-01
Investigations into action monitoring have consistently detailed a fronto-central voltage deflection in the Event-Related Potential (ERP) following the presentation of negatively valenced feedback, sometimes termed the Feedback Related Negativity (FRN). The FRN has been proposed to reflect a neural response to prediction errors during reinforcement learning, yet the single trial relationship between neural activity and the quanta of expectation violation remains untested. Although ERP methods are not well suited to single trial analyses, the FRN has been associated with theta band oscillatory perturbations in the medial prefrontal cortex. Medio-frontal theta oscillations have been previously associated with expectation violation and behavioral adaptation and are well suited to single trial analysis. Here, we recorded EEG activity during a probabilistic reinforcement learning task and fit the performance data to an abstract computational model (Q-learning) for calculation of single-trial reward prediction errors. Single-trial theta oscillatory activities following feedback were investigated within the context of expectation (prediction error) and adaptation (subsequent reaction time change). Results indicate that interactive medial and lateral frontal theta activities reflect the degree of negative and positive reward prediction error in the service of behavioral adaptation. These different brain areas use prediction error calculations for different behavioral adaptations: with medial frontal theta reflecting the utilization of prediction errors for reaction time slowing (specifically following errors), but lateral frontal theta reflecting prediction errors leading to working memory-related reaction time speeding for the correct choice. PMID:19969093
-
Krigolson, Olav E; Hassall, Cameron D; Handy, Todd C
2014-03-01
Our ability to make decisions is predicated upon our knowledge of the outcomes of the actions available to us. Reinforcement learning theory posits that actions followed by a reward or punishment acquire value through the computation of prediction errors-discrepancies between the predicted and the actual reward. A multitude of neuroimaging studies have demonstrated that rewards and punishments evoke neural responses that appear to reflect reinforcement learning prediction errors [e.g., Krigolson, O. E., Pierce, L. J., Holroyd, C. B., & Tanaka, J. W. Learning to become an expert: Reinforcement learning and the acquisition of perceptual expertise. Journal of Cognitive Neuroscience, 21, 1833-1840, 2009; Bayer, H. M., & Glimcher, P. W. Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47, 129-141, 2005; O'Doherty, J. P. Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology, 14, 769-776, 2004; Holroyd, C. B., & Coles, M. G. H. The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679-709, 2002]. Here, we used the brain ERP technique to demonstrate that not only do rewards elicit a neural response akin to a prediction error but also that this signal rapidly diminished and propagated to the time of choice presentation with learning. Specifically, in a simple, learnable gambling task, we show that novel rewards elicited a feedback error-related negativity that rapidly decreased in amplitude with learning. Furthermore, we demonstrate the existence of a reward positivity at choice presentation, a previously unreported ERP component that has a similar timing and topography as the feedback error-related negativity that increased in amplitude with learning. The pattern of results we observed mirrored the output of a computational model that we implemented to compute reward prediction errors and the changes in amplitude of these prediction errors at the time of choice presentation and reward delivery. Our results provide further support that the computations that underlie human learning and decision-making follow reinforcement learning principles.
-
Error-related brain activity predicts cocaine use after treatment at 3-month follow-up.
Marhe, Reshmi; van de Wetering, Ben J M; Franken, Ingmar H A
2013-04-15
Relapse after treatment is one of the most important problems in drug dependency. Several studies suggest that lack of cognitive control is one of the causes of relapse. In this study, a relative new electrophysiologic index of cognitive control, the error-related negativity, is investigated to examine its suitability as a predictor of relapse. The error-related negativity was measured in 57 cocaine-dependent patients during their first week in detoxification treatment. Data from 49 participants were used to predict cocaine use at 3-month follow-up. Cocaine use at follow-up was measured by means of self-reported days of cocaine use in the last month verified by urine screening. A multiple hierarchical regression model was used to examine the predictive value of the error-related negativity while controlling for addiction severity and self-reported craving in the week before treatment. The error-related negativity was the only significant predictor in the model and added 7.4% of explained variance to the control variables, resulting in a total of 33.4% explained variance in the prediction of days of cocaine use at follow-up. A reduced error-related negativity measured during the first week of treatment was associated with more days of cocaine use at 3-month follow-up. Moreover, the error-related negativity was a stronger predictor of recent cocaine use than addiction severity and craving. These results suggest that underactive error-related brain activity might help to identify patients who are at risk of relapse as early as in the first week of detoxification treatment. Copyright © 2013 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.
-
Reinhart, Robert M G; Zhu, Julia; Park, Sohee; Woodman, Geoffrey F
2015-09-02
Posterror learning, associated with medial-frontal cortical recruitment in healthy subjects, is compromised in neuropsychiatric disorders. Here we report novel evidence for the mechanisms underlying learning dysfunctions in schizophrenia. We show that, by noninvasively passing direct current through human medial-frontal cortex, we could enhance the event-related potential related to learning from mistakes (i.e., the error-related negativity), a putative index of prediction error signaling in the brain. Following this causal manipulation of brain activity, the patients learned a new task at a rate that was indistinguishable from healthy individuals. Moreover, the severity of delusions interacted with the efficacy of the stimulation to improve learning. Our results demonstrate a causal link between disrupted prediction error signaling and inefficient learning in schizophrenia. These findings also demonstrate the feasibility of nonpharmacological interventions to address cognitive deficits in neuropsychiatric disorders. When there is a difference between what we expect to happen and what we actually experience, our brains generate a prediction error signal, so that we can map stimuli to responses and predict outcomes accurately. Theories of schizophrenia implicate abnormal prediction error signaling in the cognitive deficits of the disorder. Here, we combine noninvasive brain stimulation with large-scale electrophysiological recordings to establish a causal link between faulty prediction error signaling and learning deficits in schizophrenia. We show that it is possible to improve learning rate, as well as the neural signature of prediction error signaling, in patients to a level quantitatively indistinguishable from that of healthy subjects. The results provide mechanistic insight into schizophrenia pathophysiology and suggest a future therapy for this condition. Copyright © 2015 the authors 0270-6474/15/3512232-09$15.00/0.
-
Disambiguating ventral striatum fMRI-related bold signal during reward prediction in schizophrenia
Morris, R W; Vercammen, A; Lenroot, R; Moore, L; Langton, J M; Short, B; Kulkarni, J; Curtis, J; O'Donnell, M; Weickert, C S; Weickert, T W
2012-01-01
Reward detection, surprise detection and prediction-error signaling have all been proposed as roles for the ventral striatum (vStr). Previous neuroimaging studies of striatal function in schizophrenia have found attenuated neural responses to reward-related prediction errors; however, as prediction errors represent a discrepancy in mesolimbic neural activity between expected and actual events, it is critical to examine responses to both expected and unexpected rewards (URs) in conjunction with expected and UR omissions in order to clarify the nature of ventral striatal dysfunction in schizophrenia. In the present study, healthy adults and people with schizophrenia were tested with a reward-related prediction-error task during functional magnetic resonance imaging to determine whether schizophrenia is associated with altered neural responses in the vStr to rewards, surprise prediction errors or all three factors. In healthy adults, we found neural responses in the vStr were correlated more specifically with prediction errors than to surprising events or reward stimuli alone. People with schizophrenia did not display the normal differential activation between expected and URs, which was partially due to exaggerated ventral striatal responses to expected rewards (right vStr) but also included blunted responses to unexpected outcomes (left vStr). This finding shows that neural responses, which typically are elicited by surprise, can also occur to well-predicted events in schizophrenia and identifies aberrant activity in the vStr as a key node of dysfunction in the neural circuitry used to differentiate expected and unexpected feedback in schizophrenia. PMID:21709684
-
Comparison of structural and least-squares lines for estimating geologic relations
Williams, G.P.; Troutman, B.M.
1990-01-01
Two different goals in fitting straight lines to data are to estimate a "true" linear relation (physical law) and to predict values of the dependent variable with the smallest possible error. Regarding the first goal, a Monte Carlo study indicated that the structural-analysis (SA) method of fitting straight lines to data is superior to the ordinary least-squares (OLS) method for estimating "true" straight-line relations. Number of data points, slope and intercept of the true relation, and variances of the errors associated with the independent (X) and dependent (Y) variables influence the degree of agreement. For example, differences between the two line-fitting methods decrease as error in X becomes small relative to error in Y. Regarding the second goal-predicting the dependent variable-OLS is better than SA. Again, the difference diminishes as X takes on less error relative to Y. With respect to estimation of slope and intercept and prediction of Y, agreement between Monte Carlo results and large-sample theory was very good for sample sizes of 100, and fair to good for sample sizes of 20. The procedures and error measures are illustrated with two geologic examples. ?? 1990 International Association for Mathematical Geology.
-
Response Monitoring and Adjustment: Differential Relations with Psychopathic Traits
Bresin, Konrad; Finy, M. Sima; Sprague, Jenessa; Verona, Edelyn
2014-01-01
Studies on the relation between psychopathy and cognitive functioning often show mixed results, partially because different factors of psychopathy have not been considered fully. Based on previous research, we predicted divergent results based on a two-factor model of psychopathy (interpersonal-affective traits and impulsive-antisocial traits). Specifically, we predicted that the unique variance of interpersonal-affective traits would be related to increased monitoring (i.e., error-related negativity) and adjusting to errors (i.e., post-error slowing), whereas impulsive-antisocial traits would be related to reductions in these processes. Three studies using a diverse selection of assessment tools, samples, and methods are presented to identify response monitoring correlates of the two main factors of psychopathy. In Studies 1 (undergraduates), 2 (adolescents), and 3 (offenders), interpersonal-affective traits were related to increased adjustment following errors and, in Study 3, to enhanced monitoring of errors. Impulsive-antisocial traits were not consistently related to error adjustment across the studies, although these traits were related to a deficient monitoring of errors in Study 3. The results may help explain previous mixed findings and advance implications for etiological models of psychopathy. PMID:24933282
-
Cao, Hui; Stetson, Peter; Hripcsak, George
2003-01-01
Many types of medical errors occur in and outside of hospitals, some of which have very serious consequences and increase cost. Identifying errors is a critical step for managing and preventing them. In this study, we assessed the explicit reporting of medical errors in the electronic record. We used five search terms "mistake," "error," "incorrect," "inadvertent," and "iatrogenic" to survey several sets of narrative reports including discharge summaries, sign-out notes, and outpatient notes from 1991 to 2000. We manually reviewed all the positive cases and identified them based on the reporting of physicians. We identified 222 explicitly reported medical errors. The positive predictive value varied with different keywords. In general, the positive predictive value for each keyword was low, ranging from 3.4 to 24.4%. Therapeutic-related errors were the most common reported errors and these reported therapeutic-related errors were mainly medication errors. Keyword searches combined with manual review indicated some medical errors that were reported in medical records. It had a low sensitivity and a moderate positive predictive value, which varied by search term. Physicians were most likely to record errors in the Hospital Course and History of Present Illness sections of discharge summaries. The reported errors in medical records covered a broad range and were related to several types of care providers as well as non-health care professionals.
-
Early math and reading achievement are associated with the error positivity.
Kim, Matthew H; Grammer, Jennie K; Marulis, Loren M; Carrasco, Melisa; Morrison, Frederick J; Gehring, William J
2016-12-01
Executive functioning (EF) and motivation are associated with academic achievement and error-related ERPs. The present study explores whether early academic skills predict variability in the error-related negativity (ERN) and error positivity (Pe). Data from 113 three- to seven-year-old children in a Go/No-Go task revealed that stronger early reading and math skills predicted a larger Pe. Closer examination revealed that this relation was quadratic and significant for children performing at or near grade level, but not significant for above-average achievers. Early academics did not predict the ERN. These findings suggest that the Pe - which reflects individual differences in motivational processes as well as attention - may be associated with early academic achievement. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.
-
Bissonette, Gregory B; Roesch, Matthew R
2016-01-01
Many brain areas are activated by the possibility and receipt of reward. Are all of these brain areas reporting the same information about reward? Or are these signals related to other functions that accompany reward-guided learning and decision-making? Through carefully controlled behavioral studies, it has been shown that reward-related activity can represent reward expectations related to future outcomes, errors in those expectations, motivation, and signals related to goal- and habit-driven behaviors. These dissociations have been accomplished by manipulating the predictability of positively and negatively valued events. Here, we review single neuron recordings in behaving animals that have addressed this issue. We describe data showing that several brain areas, including orbitofrontal cortex, anterior cingulate, and basolateral amygdala signal reward prediction. In addition, anterior cingulate, basolateral amygdala, and dopamine neurons also signal errors in reward prediction, but in different ways. For these areas, we will describe how unexpected manipulations of positive and negative value can dissociate signed from unsigned reward prediction errors. All of these signals feed into striatum to modify signals that motivate behavior in ventral striatum and guide responding via associative encoding in dorsolateral striatum.
-
Roesch, Matthew R.
2017-01-01
Many brain areas are activated by the possibility and receipt of reward. Are all of these brain areas reporting the same information about reward? Or are these signals related to other functions that accompany reward-guided learning and decision-making? Through carefully controlled behavioral studies, it has been shown that reward-related activity can represent reward expectations related to future outcomes, errors in those expectations, motivation, and signals related to goal- and habit-driven behaviors. These dissociations have been accomplished by manipulating the predictability of positively and negatively valued events. Here, we review single neuron recordings in behaving animals that have addressed this issue. We describe data showing that several brain areas, including orbitofrontal cortex, anterior cingulate, and basolateral amygdala signal reward prediction. In addition, anterior cingulate, basolateral amygdala, and dopamine neurons also signal errors in reward prediction, but in different ways. For these areas, we will describe how unexpected manipulations of positive and negative value can dissociate signed from unsigned reward prediction errors. All of these signals feed into striatum to modify signals that motivate behavior in ventral striatum and guide responding via associative encoding in dorsolateral striatum. PMID:26276036
-
Ruiz, María Herrojo; Strübing, Felix; Jabusch, Hans-Christian; Altenmüller, Eckart
2011-04-15
Skilled performance requires the ability to monitor ongoing behavior, detect errors in advance and modify the performance accordingly. The acquisition of fast predictive mechanisms might be possible due to the extensive training characterizing expertise performance. Recent EEG studies on piano performance reported a negative event-related potential (ERP) triggered in the ACC 70 ms before performance errors (pitch errors due to incorrect keypress). This ERP component, termed pre-error related negativity (pre-ERN), was assumed to reflect processes of error detection in advance. However, some questions remained to be addressed: (i) Does the electrophysiological marker prior to errors reflect an error signal itself or is it related instead to the implementation of control mechanisms? (ii) Does the posterior frontomedial cortex (pFMC, including ACC) interact with other brain regions to implement control adjustments following motor prediction of an upcoming error? (iii) Can we gain insight into the electrophysiological correlates of error prediction and control by assessing the local neuronal synchronization and phase interaction among neuronal populations? (iv) Finally, are error detection and control mechanisms defective in pianists with musician's dystonia (MD), a focal task-specific dystonia resulting from dysfunction of the basal ganglia-thalamic-frontal circuits? Consequently, we investigated the EEG oscillatory and phase synchronization correlates of error detection and control during piano performances in healthy pianists and in a group of pianists with MD. In healthy pianists, the main outcomes were increased pre-error theta and beta band oscillations over the pFMC and 13-15 Hz phase synchronization, between the pFMC and the right lateral prefrontal cortex, which predicted corrective mechanisms. In MD patients, the pattern of phase synchronization appeared in a different frequency band (6-8 Hz) and correlated with the severity of the disorder. The present findings shed new light on the neural mechanisms, which might implement motor prediction by means of forward control processes, as they function in healthy pianists and in their altered form in patients with MD. Copyright © 2010 Elsevier Inc. All rights reserved.
-
Error-related brain activity and error awareness in an error classification paradigm.
Di Gregorio, Francesco; Steinhauser, Marco; Maier, Martin E
2016-10-01
Error-related brain activity has been linked to error detection enabling adaptive behavioral adjustments. However, it is still unclear which role error awareness plays in this process. Here, we show that the error-related negativity (Ne/ERN), an event-related potential reflecting early error monitoring, is dissociable from the degree of error awareness. Participants responded to a target while ignoring two different incongruent distractors. After responding, they indicated whether they had committed an error, and if so, whether they had responded to one or to the other distractor. This error classification paradigm allowed distinguishing partially aware errors, (i.e., errors that were noticed but misclassified) and fully aware errors (i.e., errors that were correctly classified). The Ne/ERN was larger for partially aware errors than for fully aware errors. Whereas this speaks against the idea that the Ne/ERN foreshadows the degree of error awareness, it confirms the prediction of a computational model, which relates the Ne/ERN to post-response conflict. This model predicts that stronger distractor processing - a prerequisite of error classification in our paradigm - leads to lower post-response conflict and thus a smaller Ne/ERN. This implies that the relationship between Ne/ERN and error awareness depends on how error awareness is related to response conflict in a specific task. Our results further indicate that the Ne/ERN but not the degree of error awareness determines adaptive performance adjustments. Taken together, we conclude that the Ne/ERN is dissociable from error awareness and foreshadows adaptive performance adjustments. Our results suggest that the relationship between the Ne/ERN and error awareness is correlative and mediated by response conflict. Copyright © 2016 Elsevier Inc. All rights reserved.
-
New dimension analyses with error analysis for quaking aspen and black spruce
NASA Technical Reports Server (NTRS)
Woods, K. D.; Botkin, D. B.; Feiveson, A. H.
1987-01-01
Dimension analysis for black spruce in wetland stands and trembling aspen are reported, including new approaches in error analysis. Biomass estimates for sacrificed trees have standard errors of 1 to 3%; standard errors for leaf areas are 10 to 20%. Bole biomass estimation accounts for most of the error for biomass, while estimation of branch characteristics and area/weight ratios accounts for the leaf area error. Error analysis provides insight for cost effective design of future analyses. Predictive equations for biomass and leaf area, with empirically derived estimators of prediction error, are given. Systematic prediction errors for small aspen trees and for leaf area of spruce from different site-types suggest a need for different predictive models within species. Predictive equations are compared with published equations; significant differences may be due to species responses to regional or site differences. Proportional contributions of component biomass in aspen change in ways related to tree size and stand development. Spruce maintains comparatively constant proportions with size, but shows changes corresponding to site. This suggests greater morphological plasticity of aspen and significance for spruce of nutrient conditions.
-
Modulation of the error-related negativity by response conflict.
Danielmeier, Claudia; Wessel, Jan R; Steinhauser, Marco; Ullsperger, Markus
2009-11-01
An arrow version of the Eriksen flanker task was employed to investigate the influence of conflict on the error-related negativity (ERN). The degree of conflict was modulated by varying the distance between flankers and the target arrow (CLOSE and FAR conditions). Error rates and reaction time data from a behavioral experiment were used to adapt a connectionist model of this task. This model was based on the conflict monitoring theory and simulated behavioral and event-related potential data. The computational model predicted an increased ERN amplitude in FAR incompatible (the low-conflict condition) compared to CLOSE incompatible errors (the high-conflict condition). A subsequent ERP experiment confirmed the model predictions. The computational model explains this finding with larger post-response conflict in far trials. In addition, data and model predictions of the N2 and the LRP support the conflict interpretation of the ERN.
-
Evaluation of Acoustic Doppler Current Profiler measurements of river discharge
Morlock, S.E.
1996-01-01
The standard deviations of the ADCP measurements ranged from approximately 1 to 6 percent and were generally higher than the measurement errors predicted by error-propagation analysis of ADCP instrument performance. These error-prediction methods assume that the largest component of ADCP discharge measurement error is instrument related. The larger standard deviations indicate that substantial portions of measurement error may be attributable to sources unrelated to ADCP electronics or signal processing and are functions of the field environment.
-
Attention in the predictive mind.
Ransom, Madeleine; Fazelpour, Sina; Mole, Christopher
2017-01-01
It has recently become popular to suggest that cognition can be explained as a process of Bayesian prediction error minimization. Some advocates of this view propose that attention should be understood as the optimization of expected precisions in the prediction-error signal (Clark, 2013, 2016; Feldman & Friston, 2010; Hohwy, 2012, 2013). This proposal successfully accounts for several attention-related phenomena. We claim that it cannot account for all of them, since there are certain forms of voluntary attention that it cannot accommodate. We therefore suggest that, although the theory of Bayesian prediction error minimization introduces some powerful tools for the explanation of mental phenomena, its advocates have been wrong to claim that Bayesian prediction error minimization is 'all the brain ever does'. Copyright © 2016 Elsevier Inc. All rights reserved.
-
Greenberg, Tsafrir; Chase, Henry W.; Almeida, Jorge R.; Stiffler, Richelle; Zevallos, Carlos R.; Aslam, Haris A.; Deckersbach, Thilo; Weyandt, Sarah; Cooper, Crystal; Toups, Marisa; Carmody, Thomas; Kurian, Benji; Peltier, Scott; Adams, Phillip; McInnis, Melvin G.; Oquendo, Maria A.; McGrath, Patrick J.; Fava, Maurizio; Weissman, Myrna; Parsey, Ramin; Trivedi, Madhukar H.; Phillips, Mary L.
2016-01-01
Objective Anhedonia, disrupted reward processing, is a core symptom of major depressive disorder. Recent findings demonstrate altered reward-related ventral striatal reactivity in depressed individuals, but the extent to which this is specific to anhedonia remains poorly understood. The authors examined the effect of anhedonia on reward expectancy (expected outcome value) and prediction error-(discrepancy between expected and actual outcome) related ventral striatal reactivity, as well as the relationship between these measures. Method A total of 148 unmedicated individuals with major depressive disorder and 31 healthy comparison individuals recruited for the multisite EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care) study underwent functional MRI during a well-validated reward task. Region of interest and whole-brain data were examined in the first- (N=78) and second- (N=70) recruited cohorts, as well as the total sample, of depressed individuals, and in healthy individuals. Results Healthy, but not depressed, individuals showed a significant inverse relationship between reward expectancy and prediction error-related right ventral striatal reactivity. Across all participants, and in depressed individuals only, greater anhedonia severity was associated with a reduced reward expectancy-prediction error inverse relationship, even after controlling for other symptoms. Conclusions The normal reward expectancy and prediction error-related ventral striatal reactivity inverse relationship concords with conditioning models, predicting a shift in ventral striatal responding from reward outcomes to reward cues. This study shows, for the first time, an absence of this relationship in two cohorts of unmedicated depressed individuals and a moderation of this relationship by anhedonia, suggesting reduced reward-contingency learning with greater anhedonia. These findings help elucidate neural mechanisms of anhedonia, as a step toward identifying potential biosignatures of treatment response. PMID:26183698
-
Greenberg, Tsafrir; Chase, Henry W; Almeida, Jorge R; Stiffler, Richelle; Zevallos, Carlos R; Aslam, Haris A; Deckersbach, Thilo; Weyandt, Sarah; Cooper, Crystal; Toups, Marisa; Carmody, Thomas; Kurian, Benji; Peltier, Scott; Adams, Phillip; McInnis, Melvin G; Oquendo, Maria A; McGrath, Patrick J; Fava, Maurizio; Weissman, Myrna; Parsey, Ramin; Trivedi, Madhukar H; Phillips, Mary L
2015-09-01
Anhedonia, disrupted reward processing, is a core symptom of major depressive disorder. Recent findings demonstrate altered reward-related ventral striatal reactivity in depressed individuals, but the extent to which this is specific to anhedonia remains poorly understood. The authors examined the effect of anhedonia on reward expectancy (expected outcome value) and prediction error- (discrepancy between expected and actual outcome) related ventral striatal reactivity, as well as the relationship between these measures. A total of 148 unmedicated individuals with major depressive disorder and 31 healthy comparison individuals recruited for the multisite EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care) study underwent functional MRI during a well-validated reward task. Region of interest and whole-brain data were examined in the first- (N=78) and second- (N=70) recruited cohorts, as well as the total sample, of depressed individuals, and in healthy individuals. Healthy, but not depressed, individuals showed a significant inverse relationship between reward expectancy and prediction error-related right ventral striatal reactivity. Across all participants, and in depressed individuals only, greater anhedonia severity was associated with a reduced reward expectancy-prediction error inverse relationship, even after controlling for other symptoms. The normal reward expectancy and prediction error-related ventral striatal reactivity inverse relationship concords with conditioning models, predicting a shift in ventral striatal responding from reward outcomes to reward cues. This study shows, for the first time, an absence of this relationship in two cohorts of unmedicated depressed individuals and a moderation of this relationship by anhedonia, suggesting reduced reward-contingency learning with greater anhedonia. These findings help elucidate neural mechanisms of anhedonia, as a step toward identifying potential biosignatures of treatment response.
-
Reward positivity: Reward prediction error or salience prediction error?
Heydari, Sepideh; Holroyd, Clay B
2016-08-01
The reward positivity is a component of the human ERP elicited by feedback stimuli in trial-and-error learning and guessing tasks. A prominent theory holds that the reward positivity reflects a reward prediction error signal that is sensitive to outcome valence, being larger for unexpected positive events relative to unexpected negative events (Holroyd & Coles, 2002). Although the theory has found substantial empirical support, most of these studies have utilized either monetary or performance feedback to test the hypothesis. However, in apparent contradiction to the theory, a recent study found that unexpected physical punishments also elicit the reward positivity (Talmi, Atkinson, & El-Deredy, 2013). The authors of this report argued that the reward positivity reflects a salience prediction error rather than a reward prediction error. To investigate this finding further, in the present study participants navigated a virtual T maze and received feedback on each trial under two conditions. In a reward condition, the feedback indicated that they would either receive a monetary reward or not and in a punishment condition the feedback indicated that they would receive a small shock or not. We found that the feedback stimuli elicited a typical reward positivity in the reward condition and an apparently delayed reward positivity in the punishment condition. Importantly, this signal was more positive to the stimuli that predicted the omission of a possible punishment relative to stimuli that predicted a forthcoming punishment, which is inconsistent with the salience hypothesis. © 2016 Society for Psychophysiological Research.
-
Visuomotor adaptation needs a validation of prediction error by feedback error
Gaveau, Valérie; Prablanc, Claude; Laurent, Damien; Rossetti, Yves; Priot, Anne-Emmanuelle
2014-01-01
The processes underlying short-term plasticity induced by visuomotor adaptation to a shifted visual field are still debated. Two main sources of error can induce motor adaptation: reaching feedback errors, which correspond to visually perceived discrepancies between hand and target positions, and errors between predicted and actual visual reafferences of the moving hand. These two sources of error are closely intertwined and difficult to disentangle, as both the target and the reaching limb are simultaneously visible. Accordingly, the goal of the present study was to clarify the relative contributions of these two types of errors during a pointing task under prism-displaced vision. In “terminal feedback error” condition, viewing of their hand by subjects was allowed only at movement end, simultaneously with viewing of the target. In “movement prediction error” condition, viewing of the hand was limited to movement duration, in the absence of any visual target, and error signals arose solely from comparisons between predicted and actual reafferences of the hand. In order to prevent intentional corrections of errors, a subthreshold, progressive stepwise increase in prism deviation was used, so that subjects remained unaware of the visual deviation applied in both conditions. An adaptive aftereffect was observed in the “terminal feedback error” condition only. As far as subjects remained unaware of the optical deviation and self-assigned pointing errors, prediction error alone was insufficient to induce adaptation. These results indicate a critical role of hand-to-target feedback error signals in visuomotor adaptation; consistent with recent neurophysiological findings, they suggest that a combination of feedback and prediction error signals is necessary for eliciting aftereffects. They also suggest that feedback error updates the prediction of reafferences when a visual perturbation is introduced gradually and cognitive factors are eliminated or strongly attenuated. PMID:25408644
-
Evidence for aversive withdrawal response to own errors.
Hochman, Eldad Yitzhak; Milman, Valery; Tal, Liron
2017-10-01
Recent model suggests that error detection gives rise to defensive motivation prompting protective behavior. Models of active avoidance behavior predict it should grow larger with threat imminence and avoidance. We hypothesized that in a task requiring left or right key strikes, error detection would drive an avoidance reflex manifested by rapid withdrawal of an erring finger growing larger with threat imminence and avoidance. In experiment 1, three groups differing by error-related threat imminence and avoidance performed a flanker task requiring left or right force sensitive-key strikes. As predicted, errors were followed by rapid force release growing faster with threat imminence and opportunity to evade threat. In experiment 2, we established a link between error key release time (KRT) and the subjective sense of inner-threat. In a simultaneous, multiple regression analysis of three error-related compensatory mechanisms (error KRT, flanker effect, error correction RT), only error KRT was significantly associated with increased compulsive checking tendencies. We propose that error response withdrawal reflects an error-withdrawal reflex. Copyright © 2017 Elsevier B.V. All rights reserved.
-
DOE Office of Scientific and Technical Information (OSTI.GOV)
Vedam, S.; Docef, A.; Fix, M.
2005-06-15
The synchronization of dynamic multileaf collimator (DMLC) response with respiratory motion is critical to ensure the accuracy of DMLC-based four dimensional (4D) radiation delivery. In practice, however, a finite time delay (response time) between the acquisition of tumor position and multileaf collimator response necessitates predictive models of respiratory tumor motion to synchronize radiation delivery. Predicting a complex process such as respiratory motion introduces geometric errors, which have been reported in several publications. However, the dosimetric effect of such errors on 4D radiation delivery has not yet been investigated. Thus, our aim in this work was to quantify the dosimetric effectsmore » of geometric error due to prediction under several different conditions. Conformal and intensity modulated radiation therapy (IMRT) plans for a lung patient were generated for anterior-posterior/posterior-anterior (AP/PA) beam arrangements at 6 and 18 MV energies to provide planned dose distributions. Respiratory motion data was obtained from 60 diaphragm-motion fluoroscopy recordings from five patients. A linear adaptive filter was employed to predict the tumor position. The geometric error of prediction was defined as the absolute difference between predicted and actual positions at each diaphragm position. Distributions of geometric error of prediction were obtained for all of the respiratory motion data. Planned dose distributions were then convolved with distributions for the geometric error of prediction to obtain convolved dose distributions. The dosimetric effect of such geometric errors was determined as a function of several variables: response time (0-0.6 s), beam energy (6/18 MV), treatment delivery (3D/4D), treatment type (conformal/IMRT), beam direction (AP/PA), and breathing training type (free breathing/audio instruction/visual feedback). Dose difference and distance-to-agreement analysis was employed to quantify results. Based on our data, the dosimetric impact of prediction (a) increased with response time, (b) was larger for 3D radiation therapy as compared with 4D radiation therapy, (c) was relatively insensitive to change in beam energy and beam direction, (d) was greater for IMRT distributions as compared with conformal distributions, (e) was smaller than the dosimetric impact of latency, and (f) was greatest for respiration motion with audio instructions, followed by visual feedback and free breathing. Geometric errors of prediction that occur during 4D radiation delivery introduce dosimetric errors that are dependent on several factors, such as response time, treatment-delivery type, and beam energy. Even for relatively small response times of 0.6 s into the future, dosimetric errors due to prediction could approach delivery errors when respiratory motion is not accounted for at all. To reduce the dosimetric impact, better predictive models and/or shorter response times are required.« less
-
Prospect theory does not describe the feedback-related negativity value function.
Sambrook, Thomas D; Roser, Matthew; Goslin, Jeremy
2012-12-01
Humans handle uncertainty poorly. Prospect theory accounts for this with a value function in which possible losses are overweighted compared to possible gains, and the marginal utility of rewards decreases with size. fMRI studies have explored the neural basis of this value function. A separate body of research claims that prediction errors are calculated by midbrain dopamine neurons. We investigated whether the prospect theoretic effects shown in behavioral and fMRI studies were present in midbrain prediction error coding by using the feedback-related negativity, an ERP component believed to reflect midbrain prediction errors. Participants' stated satisfaction with outcomes followed prospect theory but their feedback-related negativity did not, instead showing no effect of marginal utility and greater sensitivity to potential gains than losses. Copyright © 2012 Society for Psychophysiological Research.
-
Association between split selection instability and predictive error in survival trees.
Radespiel-Tröger, M; Gefeller, O; Rabenstein, T; Hothorn, T
2006-01-01
To evaluate split selection instability in six survival tree algorithms and its relationship with predictive error by means of a bootstrap study. We study the following algorithms: logrank statistic with multivariate p-value adjustment without pruning (LR), Kaplan-Meier distance of survival curves (KM), martingale residuals (MR), Poisson regression for censored data (PR), within-node impurity (WI), and exponential log-likelihood loss (XL). With the exception of LR, initial trees are pruned by using split-complexity, and final trees are selected by means of cross-validation. We employ a real dataset from a clinical study of patients with gallbladder stones. The predictive error is evaluated using the integrated Brier score for censored data. The relationship between split selection instability and predictive error is evaluated by means of box-percentile plots, covariate and cutpoint selection entropy, and cutpoint selection coefficients of variation, respectively, in the root node. We found a positive association between covariate selection instability and predictive error in the root node. LR yields the lowest predictive error, while KM and MR yield the highest predictive error. The predictive error of survival trees is related to split selection instability. Based on the low predictive error of LR, we recommend the use of this algorithm for the construction of survival trees. Unpruned survival trees with multivariate p-value adjustment can perform equally well compared to pruned trees. The analysis of split selection instability can be used to communicate the results of tree-based analyses to clinicians and to support the application of survival trees.
-
Model-free and model-based reward prediction errors in EEG.
Sambrook, Thomas D; Hardwick, Ben; Wills, Andy J; Goslin, Jeremy
2018-05-24
Learning theorists posit two reinforcement learning systems: model-free and model-based. Model-based learning incorporates knowledge about structure and contingencies in the world to assign candidate actions with an expected value. Model-free learning is ignorant of the world's structure; instead, actions hold a value based on prior reinforcement, with this value updated by expectancy violation in the form of a reward prediction error. Because they use such different learning mechanisms, it has been previously assumed that model-based and model-free learning are computationally dissociated in the brain. However, recent fMRI evidence suggests that the brain may compute reward prediction errors to both model-free and model-based estimates of value, signalling the possibility that these systems interact. Because of its poor temporal resolution, fMRI risks confounding reward prediction errors with other feedback-related neural activity. In the present study, EEG was used to show the presence of both model-based and model-free reward prediction errors and their place in a temporal sequence of events including state prediction errors and action value updates. This demonstration of model-based prediction errors questions a long-held assumption that model-free and model-based learning are dissociated in the brain. Copyright © 2018 Elsevier Inc. All rights reserved.
-
DOE Office of Scientific and Technical Information (OSTI.GOV)
Daly, Don S.; Anderson, Kevin K.; White, Amanda M.
Background: A microarray of enzyme-linked immunosorbent assays, or ELISA microarray, predicts simultaneously the concentrations of numerous proteins in a small sample. These predictions, however, are uncertain due to processing error and biological variability. Making sound biological inferences as well as improving the ELISA microarray process require require both concentration predictions and creditable estimates of their errors. Methods: We present a statistical method based on monotonic spline statistical models, penalized constrained least squares fitting (PCLS) and Monte Carlo simulation (MC) to predict concentrations and estimate prediction errors in ELISA microarray. PCLS restrains the flexible spline to a fit of assay intensitymore » that is a monotone function of protein concentration. With MC, both modeling and measurement errors are combined to estimate prediction error. The spline/PCLS/MC method is compared to a common method using simulated and real ELISA microarray data sets. Results: In contrast to the rigid logistic model, the flexible spline model gave credible fits in almost all test cases including troublesome cases with left and/or right censoring, or other asymmetries. For the real data sets, 61% of the spline predictions were more accurate than their comparable logistic predictions; especially the spline predictions at the extremes of the prediction curve. The relative errors of 50% of comparable spline and logistic predictions differed by less than 20%. Monte Carlo simulation rendered acceptable asymmetric prediction intervals for both spline and logistic models while propagation of error produced symmetric intervals that diverged unrealistically as the standard curves approached horizontal asymptotes. Conclusions: The spline/PCLS/MC method is a flexible, robust alternative to a logistic/NLS/propagation-of-error method to reliably predict protein concentrations and estimate their errors. The spline method simplifies model selection and fitting, and reliably estimates believable prediction errors. For the 50% of the real data sets fit well by both methods, spline and logistic predictions are practically indistinguishable, varying in accuracy by less than 15%. The spline method may be useful when automated prediction across simultaneous assays of numerous proteins must be applied routinely with minimal user intervention.« less
-
Preston, Jonathan L; Hull, Margaret; Edwards, Mary Louise
2013-05-01
To determine if speech error patterns in preschoolers with speech sound disorders (SSDs) predict articulation and phonological awareness (PA) outcomes almost 4 years later. Twenty-five children with histories of preschool SSDs (and normal receptive language) were tested at an average age of 4;6 (years;months) and were followed up at age 8;3. The frequency of occurrence of preschool distortion errors, typical substitution and syllable structure errors, and atypical substitution and syllable structure errors was used to predict later speech sound production, PA, and literacy outcomes. Group averages revealed below-average school-age articulation scores and low-average PA but age-appropriate reading and spelling. Preschool speech error patterns were related to school-age outcomes. Children for whom >10% of their speech sound errors were atypical had lower PA and literacy scores at school age than children who produced <10% atypical errors. Preschoolers who produced more distortion errors were likely to have lower school-age articulation scores than preschoolers who produced fewer distortion errors. Different preschool speech error patterns predict different school-age clinical outcomes. Many atypical speech sound errors in preschoolers may be indicative of weak phonological representations, leading to long-term PA weaknesses. Preschoolers' distortions may be resistant to change over time, leading to persisting speech sound production problems.
-
Neural mechanisms of reinforcement learning in unmedicated patients with major depressive disorder.
Rothkirch, Marcus; Tonn, Jonas; Köhler, Stephan; Sterzer, Philipp
2017-04-01
According to current concepts, major depressive disorder is strongly related to dysfunctional neural processing of motivational information, entailing impairments in reinforcement learning. While computational modelling can reveal the precise nature of neural learning signals, it has not been used to study learning-related neural dysfunctions in unmedicated patients with major depressive disorder so far. We thus aimed at comparing the neural coding of reward and punishment prediction errors, representing indicators of neural learning-related processes, between unmedicated patients with major depressive disorder and healthy participants. To this end, a group of unmedicated patients with major depressive disorder (n = 28) and a group of age- and sex-matched healthy control participants (n = 30) completed an instrumental learning task involving monetary gains and losses during functional magnetic resonance imaging. The two groups did not differ in their learning performance. Patients and control participants showed the same level of prediction error-related activity in the ventral striatum and the anterior insula. In contrast, neural coding of reward prediction errors in the medial orbitofrontal cortex was reduced in patients. Moreover, neural reward prediction error signals in the medial orbitofrontal cortex and ventral striatum showed negative correlations with anhedonia severity. Using a standard instrumental learning paradigm we found no evidence for an overall impairment of reinforcement learning in medication-free patients with major depressive disorder. Importantly, however, the attenuated neural coding of reward in the medial orbitofrontal cortex and the relation between anhedonia and reduced reward prediction error-signalling in the medial orbitofrontal cortex and ventral striatum likely reflect an impairment in experiencing pleasure from rewarding events as a key mechanism of anhedonia in major depressive disorder. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
-
Joch, Michael; Hegele, Mathias; Maurer, Heiko; Müller, Hermann; Maurer, Lisa Katharina
2017-07-01
The error (related) negativity (Ne/ERN) is an event-related potential in the electroencephalogram (EEG) correlating with error processing. Its conditions of appearance before terminal external error information suggest that the Ne/ERN is indicative of predictive processes in the evaluation of errors. The aim of the present study was to specifically examine the Ne/ERN in a complex motor task and to particularly rule out other explaining sources of the Ne/ERN aside from error prediction processes. To this end, we focused on the dependency of the Ne/ERN on visual monitoring about the action outcome after movement termination but before result feedback (action effect monitoring). Participants performed a semi-virtual throwing task by using a manipulandum to throw a virtual ball displayed on a computer screen to hit a target object. Visual feedback about the ball flying to the target was masked to prevent action effect monitoring. Participants received a static feedback about the action outcome (850 ms) after each trial. We found a significant negative deflection in the average EEG curves of the error trials peaking at ~250 ms after ball release, i.e., before error feedback. Furthermore, this Ne/ERN signal did not depend on visual ball-flight monitoring after release. We conclude that the Ne/ERN has the potential to indicate error prediction in motor tasks and that it exists even in the absence of action effect monitoring. NEW & NOTEWORTHY In this study, we are separating different kinds of possible contributors to an electroencephalogram (EEG) error correlate (Ne/ERN) in a throwing task. We tested the influence of action effect monitoring on the Ne/ERN amplitude in the EEG. We used a task that allows us to restrict movement correction and action effect monitoring and to control the onset of result feedback. We ascribe the Ne/ERN to predictive error processing where a conscious feeling of failure is not a prerequisite. Copyright © 2017 the American Physiological Society.
-
Confirmation bias in human reinforcement learning: Evidence from counterfactual feedback processing
Lefebvre, Germain; Blakemore, Sarah-Jayne
2017-01-01
Previous studies suggest that factual learning, that is, learning from obtained outcomes, is biased, such that participants preferentially take into account positive, as compared to negative, prediction errors. However, whether or not the prediction error valence also affects counterfactual learning, that is, learning from forgone outcomes, is unknown. To address this question, we analysed the performance of two groups of participants on reinforcement learning tasks using a computational model that was adapted to test if prediction error valence influences learning. We carried out two experiments: in the factual learning experiment, participants learned from partial feedback (i.e., the outcome of the chosen option only); in the counterfactual learning experiment, participants learned from complete feedback information (i.e., the outcomes of both the chosen and unchosen option were displayed). In the factual learning experiment, we replicated previous findings of a valence-induced bias, whereby participants learned preferentially from positive, relative to negative, prediction errors. In contrast, for counterfactual learning, we found the opposite valence-induced bias: negative prediction errors were preferentially taken into account, relative to positive ones. When considering valence-induced bias in the context of both factual and counterfactual learning, it appears that people tend to preferentially take into account information that confirms their current choice. PMID:28800597
-
Confirmation bias in human reinforcement learning: Evidence from counterfactual feedback processing.
Palminteri, Stefano; Lefebvre, Germain; Kilford, Emma J; Blakemore, Sarah-Jayne
2017-08-01
Previous studies suggest that factual learning, that is, learning from obtained outcomes, is biased, such that participants preferentially take into account positive, as compared to negative, prediction errors. However, whether or not the prediction error valence also affects counterfactual learning, that is, learning from forgone outcomes, is unknown. To address this question, we analysed the performance of two groups of participants on reinforcement learning tasks using a computational model that was adapted to test if prediction error valence influences learning. We carried out two experiments: in the factual learning experiment, participants learned from partial feedback (i.e., the outcome of the chosen option only); in the counterfactual learning experiment, participants learned from complete feedback information (i.e., the outcomes of both the chosen and unchosen option were displayed). In the factual learning experiment, we replicated previous findings of a valence-induced bias, whereby participants learned preferentially from positive, relative to negative, prediction errors. In contrast, for counterfactual learning, we found the opposite valence-induced bias: negative prediction errors were preferentially taken into account, relative to positive ones. When considering valence-induced bias in the context of both factual and counterfactual learning, it appears that people tend to preferentially take into account information that confirms their current choice.
-
Preston, Jonathan L.; Hull, Margaret; Edwards, Mary Louise
2012-01-01
Purpose To determine if speech error patterns in preschoolers with speech sound disorders (SSDs) predict articulation and phonological awareness (PA) outcomes almost four years later. Method Twenty-five children with histories of preschool SSDs (and normal receptive language) were tested at an average age of 4;6 and followed up at 8;3. The frequency of occurrence of preschool distortion errors, typical substitution and syllable structure errors, and atypical substitution and syllable structure errors were used to predict later speech sound production, PA, and literacy outcomes. Results Group averages revealed below-average school-age articulation scores and low-average PA, but age-appropriate reading and spelling. Preschool speech error patterns were related to school-age outcomes. Children for whom more than 10% of their speech sound errors were atypical had lower PA and literacy scores at school-age than children who produced fewer than 10% atypical errors. Preschoolers who produced more distortion errors were likely to have lower school-age articulation scores. Conclusions Different preschool speech error patterns predict different school-age clinical outcomes. Many atypical speech sound errors in preschool may be indicative of weak phonological representations, leading to long-term PA weaknesses. Preschool distortions may be resistant to change over time, leading to persisting speech sound production problems. PMID:23184137
-
Wald Sequential Probability Ratio Test for Analysis of Orbital Conjunction Data
NASA Technical Reports Server (NTRS)
Carpenter, J. Russell; Markley, F. Landis; Gold, Dara
2013-01-01
We propose a Wald Sequential Probability Ratio Test for analysis of commonly available predictions associated with spacecraft conjunctions. Such predictions generally consist of a relative state and relative state error covariance at the time of closest approach, under the assumption that prediction errors are Gaussian. We show that under these circumstances, the likelihood ratio of the Wald test reduces to an especially simple form, involving the current best estimate of collision probability, and a similar estimate of collision probability that is based on prior assumptions about the likelihood of collision.
-
The influence of orbit selection on the accuracy of the Stanford Relativity gyroscope experiment
NASA Technical Reports Server (NTRS)
Vassar, R.; Everitt, C. W. F.; Vanpatten, R. A.; Breakwell, J. V.
1980-01-01
This paper discusses an error analysis for the Stanford Relativity experiment, designed to measure the precession of a gyroscope's spin-axis predicted by general relativity. Measurements will be made of the spin-axis orientations of 4 superconducting spherical gyroscopes carried by an earth-satellite. Two relativistic precessions are predicted: a 'geodetic' precession associated with the satellite's orbital motion and a 'motional' precession due to the earth's rotation. Using a Kalman filter covariance analysis with a realistic error model we have computed the error in determining the relativistic precession rates. Studies show that a slightly off-polar orbit is better than a polar orbit for determining the 'motional' drift.
-
Anxiety and Error Monitoring: Increased Error Sensitivity or Altered Expectations?
ERIC Educational Resources Information Center
Compton, Rebecca J.; Carp, Joshua; Chaddock, Laura; Fineman, Stephanie L.; Quandt, Lorna C.; Ratliff, Jeffrey B.
2007-01-01
This study tested the prediction that the error-related negativity (ERN), a physiological measure of error monitoring, would be enhanced in anxious individuals, particularly in conditions with threatening cues. Participants made gender judgments about faces whose expressions were either happy, angry, or neutral. Replicating prior studies, midline…
-
Methods for estimating flood frequency in Montana based on data through water year 1998
Parrett, Charles; Johnson, Dave R.
2004-01-01
Annual peak discharges having recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years (T-year floods) were determined for 660 gaged sites in Montana and in adjacent areas of Idaho, Wyoming, and Canada, based on data through water year 1998. The updated flood-frequency information was subsequently used in regression analyses, either ordinary or generalized least squares, to develop equations relating T-year floods to various basin and climatic characteristics, equations relating T-year floods to active-channel width, and equations relating T-year floods to bankfull width. The equations can be used to estimate flood frequency at ungaged sites. Montana was divided into eight regions, within which flood characteristics were considered to be reasonably homogeneous, and the three sets of regression equations were developed for each region. A measure of the overall reliability of the regression equations is the average standard error of prediction. The average standard errors of prediction for the equations based on basin and climatic characteristics ranged from 37.4 percent to 134.1 percent. Average standard errors of prediction for the equations based on active-channel width ranged from 57.2 percent to 141.3 percent. Average standard errors of prediction for the equations based on bankfull width ranged from 63.1 percent to 155.5 percent. In most regions, the equations based on basin and climatic characteristics generally had smaller average standard errors of prediction than equations based on active-channel or bankfull width. An exception was the Southeast Plains Region, where all equations based on active-channel width had smaller average standard errors of prediction than equations based on basin and climatic characteristics or bankfull width. Methods for weighting estimates derived from the basin- and climatic-characteristic equations and the channel-width equations also were developed. The weights were based on the cross correlation of residuals from the different methods and the average standard errors of prediction. When all three methods were combined, the average standard errors of prediction ranged from 37.4 percent to 120.2 percent. Weighting of estimates reduced the standard errors of prediction for all T-year flood estimates in four regions, reduced the standard errors of prediction for some T-year flood estimates in two regions, and provided no reduction in average standard error of prediction in two regions. A computer program for solving the regression equations, weighting estimates, and determining reliability of individual estimates was developed and placed on the USGS Montana District World Wide Web page. A new regression method, termed Region of Influence regression, also was tested. Test results indicated that the Region of Influence method was not as reliable as the regional equations based on generalized least squares regression. Two additional methods for estimating flood frequency at ungaged sites located on the same streams as gaged sites also are described. The first method, based on a drainage-area-ratio adjustment, is intended for use on streams where the ungaged site of interest is located near a gaged site. The second method, based on interpolation between gaged sites, is intended for use on streams that have two or more streamflow-gaging stations.
-
Spindle Thermal Error Optimization Modeling of a Five-axis Machine Tool
NASA Astrophysics Data System (ADS)
Guo, Qianjian; Fan, Shuo; Xu, Rufeng; Cheng, Xiang; Zhao, Guoyong; Yang, Jianguo
2017-05-01
Aiming at the problem of low machining accuracy and uncontrollable thermal errors of NC machine tools, spindle thermal error measurement, modeling and compensation of a two turntable five-axis machine tool are researched. Measurement experiment of heat sources and thermal errors are carried out, and GRA(grey relational analysis) method is introduced into the selection of temperature variables used for thermal error modeling. In order to analyze the influence of different heat sources on spindle thermal errors, an ANN (artificial neural network) model is presented, and ABC(artificial bee colony) algorithm is introduced to train the link weights of ANN, a new ABC-NN(Artificial bee colony-based neural network) modeling method is proposed and used in the prediction of spindle thermal errors. In order to test the prediction performance of ABC-NN model, an experiment system is developed, the prediction results of LSR (least squares regression), ANN and ABC-NN are compared with the measurement results of spindle thermal errors. Experiment results show that the prediction accuracy of ABC-NN model is higher than LSR and ANN, and the residual error is smaller than 3 μm, the new modeling method is feasible. The proposed research provides instruction to compensate thermal errors and improve machining accuracy of NC machine tools.
-
A predictability study of Lorenz's 28-variable model as a dynamical system
NASA Technical Reports Server (NTRS)
Krishnamurthy, V.
1993-01-01
The dynamics of error growth in a two-layer nonlinear quasi-geostrophic model has been studied to gain an understanding of the mathematical theory of atmospheric predictability. The growth of random errors of varying initial magnitudes has been studied, and the relation between this classical approach and the concepts of the nonlinear dynamical systems theory has been explored. The local and global growths of random errors have been expressed partly in terms of the properties of an error ellipsoid and the Liapunov exponents determined by linear error dynamics. The local growth of small errors is initially governed by several modes of the evolving error ellipsoid but soon becomes dominated by the longest axis. The average global growth of small errors is exponential with a growth rate consistent with the largest Liapunov exponent. The duration of the exponential growth phase depends on the initial magnitude of the errors. The subsequent large errors undergo a nonlinear growth with a steadily decreasing growth rate and attain saturation that defines the limit of predictability. The degree of chaos and the largest Liapunov exponent show considerable variation with change in the forcing, which implies that the time variation in the external forcing can introduce variable character to the predictability.
-
Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do.
Zhao, Linlin; Wang, Wenyi; Sedykh, Alexander; Zhu, Hao
2017-06-30
Numerous chemical data sets have become available for quantitative structure-activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting.
-
Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do
2017-01-01
Numerous chemical data sets have become available for quantitative structure–activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting. PMID:28691113
-
Mesoscale Predictability and Error Growth in Short Range Ensemble Forecasts
NASA Astrophysics Data System (ADS)
Gingrich, Mark
Although it was originally suggested that small-scale, unresolved errors corrupt forecasts at all scales through an inverse error cascade, some authors have proposed that those mesoscale circulations resulting from stationary forcing on the larger scale may inherit the predictability of the large-scale motions. Further, the relative contributions of large- and small-scale uncertainties in producing error growth in the mesoscales remain largely unknown. Here, 100 member ensemble forecasts are initialized from an ensemble Kalman filter (EnKF) to simulate two winter storms impacting the East Coast of the United States in 2010. Four verification metrics are considered: the local snow water equivalence, total liquid water, and 850 hPa temperatures representing mesoscale features; and the sea level pressure field representing a synoptic feature. It is found that while the predictability of the mesoscale features can be tied to the synoptic forecast, significant uncertainty existed on the synoptic scale at lead times as short as 18 hours. Therefore, mesoscale details remained uncertain in both storms due to uncertainties at the large scale. Additionally, the ensemble perturbation kinetic energy did not show an appreciable upscale propagation of error for either case. Instead, the initial condition perturbations from the cycling EnKF were maximized at large scales and immediately amplified at all scales without requiring initial upscale propagation. This suggests that relatively small errors in the synoptic-scale initialization may have more importance in limiting predictability than errors in the unresolved, small-scale initial conditions.
-
Uncertainty in predicting soil hydraulic properties at the hillslope scale with indirect methods
NASA Astrophysics Data System (ADS)
Chirico, G. B.; Medina, H.; Romano, N.
2007-02-01
SummarySeveral hydrological applications require the characterisation of the soil hydraulic properties at large spatial scales. Pedotransfer functions (PTFs) are being developed as simplified methods to estimate soil hydraulic properties as an alternative to direct measurements, which are unfeasible for most practical circumstances. The objective of this study is to quantify the uncertainty in PTFs spatial predictions at the hillslope scale as related to the sampling density, due to: (i) the error in estimated soil physico-chemical properties and (ii) PTF model error. The analysis is carried out on a 2-km-long experimental hillslope in South Italy. The method adopted is based on a stochastic generation of patterns of soil variables using sequential Gaussian simulation, conditioned to the observed sample data. The following PTFs are applied: Vereecken's PTF [Vereecken, H., Diels, J., van Orshoven, J., Feyen, J., Bouma, J., 1992. Functional evaluation of pedotransfer functions for the estimation of soil hydraulic properties. Soil Sci. Soc. Am. J. 56, 1371-1378] and HYPRES PTF [Wösten, J.H.M., Lilly, A., Nemes, A., Le Bas, C., 1999. Development and use of a database of hydraulic properties of European soils. Geoderma 90, 169-185]. The two PTFs estimate reliably the soil water retention characteristic even for a relatively coarse sampling resolution, with prediction uncertainties comparable to the uncertainties in direct laboratory or field measurements. The uncertainty of soil water retention prediction due to the model error is as much as or more significant than the uncertainty associated with the estimated input, even for a relatively coarse sampling resolution. Prediction uncertainties are much more important when PTF are applied to estimate the saturated hydraulic conductivity. In this case model error dominates the overall prediction uncertainties, making negligible the effect of the input error.
-
Olson, Andrew; Halloran, Elizabeth; Romani, Cristina
2015-12-01
We present three jargonaphasic patients who made phonological errors in naming, repetition and reading. We analyse target/response overlap using statistical models to answer three questions: 1) Is there a single phonological source for errors or two sources, one for target-related errors and a separate source for abstruse errors? 2) Can correct responses be predicted by the same distribution used to predict errors or do they show a completion boost (CB)? 3) Is non-lexical and lexical information summed during reading and repetition? The answers were clear. 1) Abstruse errors did not require a separate distribution created by failure to access word forms. Abstruse and target-related errors were the endpoints of a single overlap distribution. 2) Correct responses required a special factor, e.g., a CB or lexical/phonological feedback, to preserve their integrity. 3) Reading and repetition required separate lexical and non-lexical contributions that were combined at output. Copyright © 2015 Elsevier Ltd. All rights reserved.
-
Effect of correlated observation error on parameters, predictions, and uncertainty
Tiedeman, Claire; Green, Christopher T.
2013-01-01
Correlations among observation errors are typically omitted when calculating observation weights for model calibration by inverse methods. We explore the effects of omitting these correlations on estimates of parameters, predictions, and uncertainties. First, we develop a new analytical expression for the difference in parameter variance estimated with and without error correlations for a simple one-parameter two-observation inverse model. Results indicate that omitting error correlations from both the weight matrix and the variance calculation can either increase or decrease the parameter variance, depending on the values of error correlation (ρ) and the ratio of dimensionless scaled sensitivities (rdss). For small ρ, the difference in variance is always small, but for large ρ, the difference varies widely depending on the sign and magnitude of rdss. Next, we consider a groundwater reactive transport model of denitrification with four parameters and correlated geochemical observation errors that are computed by an error-propagation approach that is new for hydrogeologic studies. We compare parameter estimates, predictions, and uncertainties obtained with and without the error correlations. Omitting the correlations modestly to substantially changes parameter estimates, and causes both increases and decreases of parameter variances, consistent with the analytical expression. Differences in predictions for the models calibrated with and without error correlations can be greater than parameter differences when both are considered relative to their respective confidence intervals. These results indicate that including observation error correlations in weighting for nonlinear regression can have important effects on parameter estimates, predictions, and their respective uncertainties.
-
NASA Astrophysics Data System (ADS)
Wang, Qianxin; Hu, Chao; Xu, Tianhe; Chang, Guobin; Hernández Moraleda, Alberto
2017-12-01
Analysis centers (ACs) for global navigation satellite systems (GNSSs) cannot accurately obtain real-time Earth rotation parameters (ERPs). Thus, the prediction of ultra-rapid orbits in the international terrestrial reference system (ITRS) has to utilize the predicted ERPs issued by the International Earth Rotation and Reference Systems Service (IERS) or the International GNSS Service (IGS). In this study, the accuracy of ERPs predicted by IERS and IGS is analyzed. The error of the ERPs predicted for one day can reach 0.15 mas and 0.053 ms in polar motion and UT1-UTC direction, respectively. Then, the impact of ERP errors on ultra-rapid orbit prediction by GNSS is studied. The methods for orbit integration and frame transformation in orbit prediction with introduced ERP errors dominate the accuracy of the predicted orbit. Experimental results show that the transformation from the geocentric celestial references system (GCRS) to ITRS exerts the strongest effect on the accuracy of the predicted ultra-rapid orbit. To obtain the most accurate predicted ultra-rapid orbit, a corresponding real-time orbit correction method is developed. First, orbits without ERP-related errors are predicted on the basis of ITRS observed part of ultra-rapid orbit for use as reference. Then, the corresponding predicted orbit is transformed from GCRS to ITRS to adjust for the predicted ERPs. Finally, the corrected ERPs with error slopes are re-introduced to correct the predicted orbit in ITRS. To validate the proposed method, three experimental schemes are designed: function extrapolation, simulation experiments, and experiments with predicted ultra-rapid orbits and international GNSS Monitoring and Assessment System (iGMAS) products. Experimental results show that using the proposed correction method with IERS products considerably improved the accuracy of ultra-rapid orbit prediction (except the geosynchronous BeiDou orbits). The accuracy of orbit prediction is enhanced by at least 50% (error related to ERP) when a highly accurate observed orbit is used with the correction method. For iGMAS-predicted orbits, the accuracy improvement ranges from 8.5% for the inclined BeiDou orbits to 17.99% for the GPS orbits. This demonstrates that the correction method proposed by this study can optimize the ultra-rapid orbit prediction.
-
Gregorini, P; Galli, J; Romera, A J; Levy, G; Macdonald, K A; Fernandez, H H; Beukes, P C
2014-07-01
The DairyNZ whole-farm model (WFM; DairyNZ, Hamilton, New Zealand) consists of a framework that links component models for animal, pastures, crops, and soils. The model was developed to assist with analysis and design of pasture-based farm systems. New (this work) and revised (e.g., cow, pasture, crops) component models can be added to the WFM, keeping the model flexible and up to date. Nevertheless, the WFM does not account for plant-animal relationships determining herbage-depletion dynamics. The user has to preset the maximum allowable level of herbage depletion [i.e., postgrazing herbage mass (residuals)] throughout the year. Because residuals have a direct effect on herbage regrowth, the WFM in its current form does not dynamically simulate the effect of grazing pressure on herbage depletion and consequent effect on herbage regrowth. The management of grazing pressure is a key component of pasture-based dairy systems. Thus, the main objective of the present work was to develop a new version of the WFM able to predict residuals, and thereby simulate related effects of grazing pressure dynamically at the farm scale. This objective was accomplished by incorporating a new component model into the WFM. This model represents plant-animal relationships, for example sward structure and herbage intake rate, and resulting level of herbage depletion. The sensitivity of the new version of the WFM was evaluated and then the new WFM was tested against an experimental data set previously used to evaluate the WFM and to illustrate the adequacy and improvement of the model development. Key outputs variables of the new version pertinent to this work (milk production, herbage dry matter intake, intake rate, harvesting efficiency, and residuals) responded acceptably to a range of input variables. The relative prediction errors for monthly and mean annual residual predictions were 20 and 5%, respectively. Monthly predictions of residuals had a line bias (1.5%), with a proportion of square root of mean square prediction error (RMSPE) due to random error of 97.5%. Predicted monthly herbage growth rates had a line bias of 2%, a proportion of RMSPE due to random error of 96%, and a concordance correlation coefficient of 0.87. Annual herbage production was predicted with an RMSPE of 531 (kg of herbage dry matter/ha per year), a line bias of 11%, a proportion of RMSPE due to random error of 80%, and relative prediction errors of 2%. Annual herbage dry matter intake per cow and hectare, both per year, were predicted with RMSPE, relative prediction error, and concordance correlation coefficient of 169 and 692kg of dry matter, 3 and 4%, and 0.91 and 0.87, respectively. These results indicate that predictions of the new WFM are relatively accurate and precise, with a conclusion that incorporating a plant-animal relationship model into the WFM allows for dynamic predictions of residuals and more realistic simulations of the effect of grazing pressure on herbage production and intake at the farm level without the intervention from the user. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
-
Lahat, Ayelet; Lamm, Connie; Chronis-Tuscano, Andrea; Pine, Daniel S.; Henderson, Heather A.; Fox, Nathan A.
2014-01-01
Objective Behavioral inhibition (BI) is an early childhood temperament characterized by fearful responses to novelty and avoidance of social interactions. During adolescence, a subset of children with stable childhood BI develop social anxiety disorder and concurrently exhibit increased error monitoring. The current study examines whether increased error monitoring in seven-year-old behaviorally inhibited children prospectively predicts risk for symptoms of social phobia at age 9. Method Two hundred and ninety one children were characterized on BI at 24 and 36 months of age. Children were seen again at 7 years of age, where they performed a Flanker task, and event-related-potential (ERP) indices of response monitoring were generated. At age 9, self- and maternal-report of social phobia symptoms were obtained. Results Children high in BI, compared to those low in BI, displayed increased error monitoring at age 7, as indexed by larger (i.e., more negative) error-related negativity (ERN) amplitudes. Additionally, early BI was related to later childhood social phobia symptoms at age 9 among children with a large difference in amplitude between ERN and correct-response negativity (CRN) at age 7. Conclusions Heightened error monitoring predicts risk for later social phobia symptoms in children with high BI. Research assessing response monitoring in children with BI may refine our understanding of the mechanisms underlying risk for later anxiety disorders and inform prevention efforts. PMID:24655654
-
How good are the Garvey-Kelson predictions of nuclear masses?
NASA Astrophysics Data System (ADS)
Morales, Irving O.; López Vieyra, J. C.; Hirsch, J. G.; Frank, A.
2009-09-01
The Garvey-Kelson relations are used in an iterative process to predict nuclear masses in the neighborhood of nuclei with measured masses. Average errors in the predicted masses for the first three iteration shells are smaller than those obtained with the best nuclear mass models. Their quality is comparable with the Audi-Wapstra extrapolations, offering a simple and reproducible procedure for short range mass predictions. A systematic study of the way the error grows as a function of the iteration and the distance to the known masses region, shows that a correlation exists between the error and the residual neutron-proton interaction, produced mainly by the implicit assumption that V varies smoothly along the nuclear landscape.
-
Kumar, K Vasanth; Porkodi, K; Rocha, F
2008-01-15
A comparison of linear and non-linear regression method in selecting the optimum isotherm was made to the experimental equilibrium data of basic red 9 sorption by activated carbon. The r(2) was used to select the best fit linear theoretical isotherm. In the case of non-linear regression method, six error functions namely coefficient of determination (r(2)), hybrid fractional error function (HYBRID), Marquardt's percent standard deviation (MPSD), the average relative error (ARE), sum of the errors squared (ERRSQ) and sum of the absolute errors (EABS) were used to predict the parameters involved in the two and three parameter isotherms and also to predict the optimum isotherm. Non-linear regression was found to be a better way to obtain the parameters involved in the isotherms and also the optimum isotherm. For two parameter isotherm, MPSD was found to be the best error function in minimizing the error distribution between the experimental equilibrium data and predicted isotherms. In the case of three parameter isotherm, r(2) was found to be the best error function to minimize the error distribution structure between experimental equilibrium data and theoretical isotherms. The present study showed that the size of the error function alone is not a deciding factor to choose the optimum isotherm. In addition to the size of error function, the theory behind the predicted isotherm should be verified with the help of experimental data while selecting the optimum isotherm. A coefficient of non-determination, K(2) was explained and was found to be very useful in identifying the best error function while selecting the optimum isotherm.
-
Flight Evaluation of Center-TRACON Automation System Trajectory Prediction Process
NASA Technical Reports Server (NTRS)
Williams, David H.; Green, Steven M.
1998-01-01
Two flight experiments (Phase 1 in October 1992 and Phase 2 in September 1994) were conducted to evaluate the accuracy of the Center-TRACON Automation System (CTAS) trajectory prediction process. The Transport Systems Research Vehicle (TSRV) Boeing 737 based at Langley Research Center flew 57 arrival trajectories that included cruise and descent segments; at the same time, descent clearance advisories from CTAS were followed. Actual trajectories of the airplane were compared with the trajectories predicted by the CTAS trajectory synthesis algorithms and airplane Flight Management System (FMS). Trajectory prediction accuracy was evaluated over several levels of cockpit automation that ranged from a conventional cockpit to performance-based FMS vertical navigation (VNAV). Error sources and their magnitudes were identified and measured from the flight data. The major source of error during these tests was found to be the predicted winds aloft used by CTAS. The most significant effect related to flight guidance was the cross-track and turn-overshoot errors associated with conventional VOR guidance. FMS lateral navigation (LNAV) guidance significantly reduced both the cross-track and turn-overshoot error. Pilot procedures and VNAV guidance were found to significantly reduce the vertical profile errors associated with atmospheric and airplane performance model errors.
-
An MEG signature corresponding to an axiomatic model of reward prediction error.
Talmi, Deborah; Fuentemilla, Lluis; Litvak, Vladimir; Duzel, Emrah; Dolan, Raymond J
2012-01-02
Optimal decision-making is guided by evaluating the outcomes of previous decisions. Prediction errors are theoretical teaching signals which integrate two features of an outcome: its inherent value and prior expectation of its occurrence. To uncover the magnetic signature of prediction errors in the human brain we acquired magnetoencephalographic (MEG) data while participants performed a gambling task. Our primary objective was to use formal criteria, based upon an axiomatic model (Caplin and Dean, 2008a), to determine the presence and timing profile of MEG signals that express prediction errors. We report analyses at the sensor level, implemented in SPM8, time locked to outcome onset. We identified, for the first time, a MEG signature of prediction error, which emerged approximately 320 ms after an outcome and expressed as an interaction between outcome valence and probability. This signal followed earlier, separate signals for outcome valence and probability, which emerged approximately 200 ms after an outcome. Strikingly, the time course of the prediction error signal, as well as the early valence signal, resembled the Feedback-Related Negativity (FRN). In simultaneously acquired EEG data we obtained a robust FRN, but the win and loss signals that comprised this difference wave did not comply with the axiomatic model. Our findings motivate an explicit examination of the critical issue of timing embodied in computational models of prediction errors as seen in human electrophysiological data. Copyright © 2011 Elsevier Inc. All rights reserved.
-
Biased interpretation and memory in children with varying levels of spider fear.
Klein, Anke M; Titulaer, Geraldine; Simons, Carlijn; Allart, Esther; de Gier, Erwin; Bögels, Susan M; Becker, Eni S; Rinck, Mike
2014-01-01
This study investigated multiple cognitive biases in children simultaneously, to investigate whether spider-fearful children display an interpretation bias, a recall bias, and source monitoring errors, and whether these biases are specific for spider-related materials. Furthermore, the independent ability of these biases to predict spider fear was investigated. A total of 121 children filled out the Spider Anxiety and Disgust Screening for Children (SADS-C), and they performed an interpretation task, a memory task, and a Behavioural Assessment Test (BAT). As expected, a specific interpretation bias was found: Spider-fearful children showed more negative interpretations of ambiguous spider-related scenarios, but not of other scenarios. We also found specific source monitoring errors: Spider-fearful children made more fear-related source monitoring errors for the spider-related scenarios, but not for the other scenarios. Only limited support was found for a recall bias. Finally, interpretation bias, recall bias, and source monitoring errors predicted unique variance components of spider fear.
-
Qu, Conghui; Schuetz, Johanna M.; Min, Jeong Eun; Leach, Stephen; Daley, Denise; Spinelli, John J.; Brooks-Wilson, Angela; Graham, Jinko
2011-01-01
We describe a statistical approach to predict gender-labeling errors in candidate-gene association studies, when Y-chromosome markers have not been included in the genotyping set. The approach adds value to methods that consider only the heterozygosity of X-chromosome SNPs, by incorporating available information about the intensity of X-chromosome SNPs in candidate genes relative to autosomal SNPs from the same individual. To our knowledge, no published methods formalize a framework in which heterozygosity and relative intensity are simultaneously taken into account. Our method offers the advantage that, in the genotyping set, no additional space is required beyond that already assigned to X-chromosome SNPs in the candidate genes. We also show how the predictions can be used in a two-phase sampling design to estimate the gender-labeling error rates for an entire study, at a fraction of the cost of a conventional design. PMID:22303327
-
DeGuzman, Marisa; Shott, Megan E; Yang, Tony T; Riederer, Justin; Frank, Guido K W
2017-06-01
Anorexia nervosa is a psychiatric disorder of unknown etiology. Understanding associations between behavior and neurobiology is important in treatment development. Using a novel monetary reward task during functional magnetic resonance brain imaging, the authors tested how brain reward learning in adolescent anorexia nervosa changes with weight restoration. Female adolescents with anorexia nervosa (N=21; mean age, 16.4 years [SD=1.9]) underwent functional MRI (fMRI) before and after treatment; similarly, healthy female control adolescents (N=21; mean age, 15.2 years [SD=2.4]) underwent fMRI on two occasions. Brain function was tested using the reward prediction error construct, a computational model for reward receipt and omission related to motivation and neural dopamine responsiveness. Compared with the control group, the anorexia nervosa group exhibited greater brain response 1) for prediction error regression within the caudate, ventral caudate/nucleus accumbens, and anterior and posterior insula, 2) to unexpected reward receipt in the anterior and posterior insula, and 3) to unexpected reward omission in the caudate body. Prediction error and unexpected reward omission response tended to normalize with treatment, while unexpected reward receipt response remained significantly elevated. Greater caudate prediction error response when underweight was associated with lower weight gain during treatment. Punishment sensitivity correlated positively with ventral caudate prediction error response. Reward system responsiveness is elevated in adolescent anorexia nervosa when underweight and after weight restoration. Heightened prediction error activity in brain reward regions may represent a phenotype of adolescent anorexia nervosa that does not respond well to treatment. Prediction error response could be a neurobiological marker of illness severity that can indicate individual treatment needs.
-
DeGuzman, Marisa; Shott, Megan E.; Yang, Tony T.; Riederer, Justin; Frank, Guido K.W.
2017-01-01
Objective Anorexia nervosa is a psychiatric disorder of unknown etiology. Understanding associations between behavior and neurobiology is important in treatment development. Using a novel monetary reward task during functional magnetic resonance brain imaging, the authors tested how brain reward learning in adolescent anorexia nervosa changes with weight restoration. Method Female adolescents with anorexia nervosa (N=21; mean age, 15.2 years [SD=2.4]) underwent functional MRI (fMRI) before and after treatment; similarly, healthy female control adolescents (N=21; mean age, 16.4 years [SD=1.9]) underwent fMRI on two occasions. Brain function was tested using the reward prediction error construct, a computational model for reward receipt and omission related to motivation and neural dopamine responsiveness. Results Compared with the control group, the anorexia nervosa group exhibited greater brain response 1) for prediction error regression within the caudate, ventral caudate/nucleus accumbens, and anterior and posterior insula, 2) to unexpected reward receipt in the anterior and posterior insula, and 3) to unexpected reward omission in the caudate body. Prediction error and unexpected reward omission response tended to normalize with treatment, while unexpected reward receipt response remained significantly elevated. Greater caudate prediction error response when underweight was associated with lower weight gain during treatment. Punishment sensitivity correlated positively with ventral caudate prediction error response. Conclusions Reward system responsiveness is elevated in adolescent anorexia nervosa when underweight and after weight restoration. Heightened prediction error activity in brain reward regions may represent a phenotype of adolescent anorexia nervosa that does not respond well to treatment. Prediction error response could be a neurobiological marker of illness severity that can indicate individual treatment needs. PMID:28231717
-
Prediction of human errors by maladaptive changes in event-related brain networks.
Eichele, Tom; Debener, Stefan; Calhoun, Vince D; Specht, Karsten; Engel, Andreas K; Hugdahl, Kenneth; von Cramon, D Yves; Ullsperger, Markus
2008-04-22
Humans engaged in monotonous tasks are susceptible to occasional errors that may lead to serious consequences, but little is known about brain activity patterns preceding errors. Using functional MRI and applying independent component analysis followed by deconvolution of hemodynamic responses, we studied error preceding brain activity on a trial-by-trial basis. We found a set of brain regions in which the temporal evolution of activation predicted performance errors. These maladaptive brain activity changes started to evolve approximately 30 sec before the error. In particular, a coincident decrease of deactivation in default mode regions of the brain, together with a decline of activation in regions associated with maintaining task effort, raised the probability of future errors. Our findings provide insights into the brain network dynamics preceding human performance errors and suggest that monitoring of the identified precursor states may help in avoiding human errors in critical real-world situations.
-
Prediction of human errors by maladaptive changes in event-related brain networks
Eichele, Tom; Debener, Stefan; Calhoun, Vince D.; Specht, Karsten; Engel, Andreas K.; Hugdahl, Kenneth; von Cramon, D. Yves; Ullsperger, Markus
2008-01-01
Humans engaged in monotonous tasks are susceptible to occasional errors that may lead to serious consequences, but little is known about brain activity patterns preceding errors. Using functional MRI and applying independent component analysis followed by deconvolution of hemodynamic responses, we studied error preceding brain activity on a trial-by-trial basis. We found a set of brain regions in which the temporal evolution of activation predicted performance errors. These maladaptive brain activity changes started to evolve ≈30 sec before the error. In particular, a coincident decrease of deactivation in default mode regions of the brain, together with a decline of activation in regions associated with maintaining task effort, raised the probability of future errors. Our findings provide insights into the brain network dynamics preceding human performance errors and suggest that monitoring of the identified precursor states may help in avoiding human errors in critical real-world situations. PMID:18427123
-
Applying Intelligent Algorithms to Automate the Identification of Error Factors.
Jin, Haizhe; Qu, Qingxing; Munechika, Masahiko; Sano, Masataka; Kajihara, Chisato; Duffy, Vincent G; Chen, Han
2018-05-03
Medical errors are the manifestation of the defects occurring in medical processes. Extracting and identifying defects as medical error factors from these processes are an effective approach to prevent medical errors. However, it is a difficult and time-consuming task and requires an analyst with a professional medical background. The issues of identifying a method to extract medical error factors and reduce the extraction difficulty need to be resolved. In this research, a systematic methodology to extract and identify error factors in the medical administration process was proposed. The design of the error report, extraction of the error factors, and identification of the error factors were analyzed. Based on 624 medical error cases across four medical institutes in both Japan and China, 19 error-related items and their levels were extracted. After which, they were closely related to 12 error factors. The relational model between the error-related items and error factors was established based on a genetic algorithm (GA)-back-propagation neural network (BPNN) model. Additionally, compared to GA-BPNN, BPNN, partial least squares regression and support vector regression, GA-BPNN exhibited a higher overall prediction accuracy, being able to promptly identify the error factors from the error-related items. The combination of "error-related items, their different levels, and the GA-BPNN model" was proposed as an error-factor identification technology, which could automatically identify medical error factors.
-
Popa, Laurentiu S.; Hewitt, Angela L.; Ebner, Timothy J.
2012-01-01
The cerebellum has been implicated in processing motor errors required for online control of movement and motor learning. The dominant view is that Purkinje cell complex spike discharge signals motor errors. This study investigated whether errors are encoded in the simple spike discharge of Purkinje cells in monkeys trained to manually track a pseudo-randomly moving target. Four task error signals were evaluated based on cursor movement relative to target movement. Linear regression analyses based on firing residuals ensured that the modulation with a specific error parameter was independent of the other error parameters and kinematics. The results demonstrate that simple spike firing in lobules IV–VI is significantly correlated with position, distance and directional errors. Independent of the error signals, the same Purkinje cells encode kinematics. The strongest error modulation occurs at feedback timing. However, in 72% of cells at least one of the R2 temporal profiles resulting from regressing firing with individual errors exhibit two peak R2 values. For these bimodal profiles, the first peak is at a negative τ (lead) and a second peak at a positive τ (lag), implying that Purkinje cells encode both prediction and feedback about an error. For the majority of the bimodal profiles, the signs of the regression coefficients or preferred directions reverse at the times of the peaks. The sign reversal results in opposing simple spike modulation for the predictive and feedback components. Dual error representations may provide the signals needed to generate sensory prediction errors used to update a forward internal model. PMID:23115173
-
A confirmation of the general relativistic prediction of the Lense-Thirring effect.
Ciufolini, I; Pavlis, E C
2004-10-21
An important early prediction of Einstein's general relativity was the advance of the perihelion of Mercury's orbit, whose measurement provided one of the classical tests of Einstein's theory. The advance of the orbital point-of-closest-approach also applies to a binary pulsar system and to an Earth-orbiting satellite. General relativity also predicts that the rotation of a body like Earth will drag the local inertial frames of reference around it, which will affect the orbit of a satellite. This Lense-Thirring effect has hitherto not been detected with high accuracy, but its detection with an error of about 1 per cent is the main goal of Gravity Probe B--an ongoing space mission using orbiting gyroscopes. Here we report a measurement of the Lense-Thirring effect on two Earth satellites: it is 99 +/- 5 per cent of the value predicted by general relativity; the uncertainty of this measurement includes all known random and systematic errors, but we allow for a total +/- 10 per cent uncertainty to include underestimated and unknown sources of error.
-
Schaefer, Alexandre; Buratto, Luciano G; Goto, Nobuhiko; Brotherhood, Emilie V
A large body of evidence shows that buying behaviour is strongly determined by consumers' price expectations and the extent to which real prices violate these expectations. Despite the importance of this phenomenon, little is known regarding its neural mechanisms. Here we show that two patterns of electrical brain activity known to index prediction errors-the Feedback-Related Negativity (FRN) and the feedback-related P300 -were sensitive to price offers that were cheaper than participants' expectations. In addition, we also found that FRN amplitude time-locked to price offers predicted whether a product would be subsequently purchased or not, and further analyses suggest that this result was driven by the sensitivity of the FRN to positive price expectation violations. This finding strongly suggests that ensembles of neurons coding positive prediction errors play a critical role in real-life consumer behaviour. Further, these findings indicate that theoretical models based on the notion of prediction error, such as the Reinforcement Learning Theory, can provide a neurobiologically grounded account of consumer behavior.
-
Hoos, Anne B.; Patel, Anant R.
1996-01-01
Model-adjustment procedures were applied to the combined data bases of storm-runoff quality for Chattanooga, Knoxville, and Nashville, Tennessee, to improve predictive accuracy for storm-runoff quality for urban watersheds in these three cities and throughout Middle and East Tennessee. Data for 45 storms at 15 different sites (five sites in each city) constitute the data base. Comparison of observed values of storm-runoff load and event-mean concentration to the predicted values from the regional regression models for 10 constituents shows prediction errors, as large as 806,000 percent. Model-adjustment procedures, which combine the regional model predictions with local data, are applied to improve predictive accuracy. Standard error of estimate after model adjustment ranges from 67 to 322 percent. Calibration results may be biased due to sampling error in the Tennessee data base. The relatively large values of standard error of estimate for some of the constituent models, although representing significant reduction (at least 50 percent) in prediction error compared to estimation with unadjusted regional models, may be unacceptable for some applications. The user may wish to collect additional local data for these constituents and repeat the analysis, or calibrate an independent local regression model.
-
NASA Astrophysics Data System (ADS)
Lehner, Flavio; Wood, Andrew W.; Llewellyn, Dagmar; Blatchford, Douglas B.; Goodbody, Angus G.; Pappenberger, Florian
2017-12-01
Seasonal streamflow predictions provide a critical management tool for water managers in the American Southwest. In recent decades, persistent prediction errors for spring and summer runoff volumes have been observed in a number of watersheds in the American Southwest. While mostly driven by decadal precipitation trends, these errors also relate to the influence of increasing temperature on streamflow in these basins. Here we show that incorporating seasonal temperature forecasts from operational global climate prediction models into streamflow forecasting models adds prediction skill for watersheds in the headwaters of the Colorado and Rio Grande River basins. Current dynamical seasonal temperature forecasts now show sufficient skill to reduce streamflow forecast errors in snowmelt-driven regions. Such predictions can increase the resilience of streamflow forecasting and water management systems in the face of continuing warming as well as decadal-scale temperature variability and thus help to mitigate the impacts of climate nonstationarity on streamflow predictability.
-
Flight Test Results: CTAS Cruise/Descent Trajectory Prediction Accuracy for En route ATC Advisories
NASA Technical Reports Server (NTRS)
Green, S.; Grace, M.; Williams, D.
1999-01-01
The Center/TRACON Automation System (CTAS), under development at NASA Ames Research Center, is designed to assist controllers with the management and control of air traffic transitioning to/from congested airspace. This paper focuses on the transition from the en route environment, to high-density terminal airspace, under a time-based arrival-metering constraint. Two flight tests were conducted at the Denver Air Route Traffic Control Center (ARTCC) to study trajectory-prediction accuracy, the key to accurate Decision Support Tool advisories such as conflict detection/resolution and fuel-efficient metering conformance. In collaboration with NASA Langley Research Center, these test were part of an overall effort to research systems and procedures for the integration of CTAS and flight management systems (FMS). The Langley Transport Systems Research Vehicle Boeing 737 airplane flew a combined total of 58 cruise-arrival trajectory runs while following CTAS clearance advisories. Actual trajectories of the airplane were compared to CTAS and FMS predictions to measure trajectory-prediction accuracy and identify the primary sources of error for both. The research airplane was used to evaluate several levels of cockpit automation ranging from conventional avionics to a performance-based vertical navigation (VNAV) FMS. Trajectory prediction accuracy was analyzed with respect to both ARTCC radar tracking and GPS-based aircraft measurements. This paper presents detailed results describing the trajectory accuracy and error sources. Although differences were found in both accuracy and error sources, CTAS accuracy was comparable to the FMS in terms of both meter-fix arrival-time performance (in support of metering) and 4D-trajectory prediction (key to conflict prediction). Overall arrival time errors (mean plus standard deviation) were measured to be approximately 24 seconds during the first flight test (23 runs) and 15 seconds during the second flight test (25 runs). The major source of error during these tests was found to be the predicted winds aloft used by CTAS. Position and velocity estimates of the airplane provided to CTAS by the ATC Host radar tracker were found to be a relatively insignificant error source for the trajectory conditions evaluated. Airplane performance modeling errors within CTAS were found to not significantly affect arrival time errors when the constrained descent procedures were used. The most significant effect related to the flight guidance was observed to be the cross-track and turn-overshoot errors associated with conventional VOR guidance. Lateral navigation (LNAV) guidance significantly reduced both the cross-track and turn-overshoot error. Pilot procedures and VNAV guidance were found to significantly reduce the vertical profile errors associated with atmospheric and aircraft performance model errors.
-
Resolving Mixed Algal Species in Hyperspectral Images
Mehrubeoglu, Mehrube; Teng, Ming Y.; Zimba, Paul V.
2014-01-01
We investigated a lab-based hyperspectral imaging system's response from pure (single) and mixed (two) algal cultures containing known algae types and volumetric combinations to characterize the system's performance. The spectral response to volumetric changes in single and combinations of algal mixtures with known ratios were tested. Constrained linear spectral unmixing was applied to extract the algal content of the mixtures based on abundances that produced the lowest root mean square error. Percent prediction error was computed as the difference between actual percent volumetric content and abundances at minimum RMS error. Best prediction errors were computed as 0.4%, 0.4% and 6.3% for the mixed spectra from three independent experiments. The worst prediction errors were found as 5.6%, 5.4% and 13.4% for the same order of experiments. Additionally, Beer-Lambert's law was utilized to relate transmittance to different volumes of pure algal suspensions demonstrating linear logarithmic trends for optical property measurements. PMID:24451451
-
Luo, Ke; Hong, Sung-Sam; Wang, Jun; Chung, Mi-Ja; Deog-Hwan, Oh
2015-05-01
This study was conducted to develop a predictive model to estimate the growth of Listeria monocytogenes on fresh pork during storage at constant temperatures (5, 10, 15, 20, 25, 30, and 35°C). The Baranyi model was fitted to growth data (log CFU per gram) to calculate the specific growth rate (SGR) and lag time (LT) with a high coefficient of determination (R(2) > 0.98). As expected, SGR increased with a decline in LT with rising temperatures in all samples. Secondary models were then developed to describe the variation of SGR and LT as a function of temperature. Subsequently, the developed models were validated with additional independent growth data collected at 7, 17, 27, and 37°C and from published reports using proportion of relative errors and proportion of standard error of prediction. The proportion of relative errors of the SGR and LT models developed herein were 0.79 and 0.18, respectively. In addition, the standard error of prediction values of the SGR and LT of L. monocytogenes ranged from 25.7 to 33.1% and from 44.92 to 58.44%, respectively. These results suggest that the model developed in this study was capable of predicting the growth of L. monocytogenes under various isothermal conditions.
-
High capacity reversible watermarking for audio by histogram shifting and predicted error expansion.
Wang, Fei; Xie, Zhaoxin; Chen, Zuo
2014-01-01
Being reversible, the watermarking information embedded in audio signals can be extracted while the original audio data can achieve lossless recovery. Currently, the few reversible audio watermarking algorithms are confronted with following problems: relatively low SNR (signal-to-noise) of embedded audio; a large amount of auxiliary embedded location information; and the absence of accurate capacity control capability. In this paper, we present a novel reversible audio watermarking scheme based on improved prediction error expansion and histogram shifting. First, we use differential evolution algorithm to optimize prediction coefficients and then apply prediction error expansion to output stego data. Second, in order to reduce location map bits length, we introduced histogram shifting scheme. Meanwhile, the prediction error modification threshold according to a given embedding capacity can be computed by our proposed scheme. Experiments show that this algorithm improves the SNR of embedded audio signals and embedding capacity, drastically reduces location map bits length, and enhances capacity control capability.
-
Errors in Multi-Digit Arithmetic and Behavioral Inattention in Children With Math Difficulties
Raghubar, Kimberly; Cirino, Paul; Barnes, Marcia; Ewing-Cobbs, Linda; Fletcher, Jack; Fuchs, Lynn
2009-01-01
Errors in written multi-digit computation were investigated in children with math difficulties. Third-and fourth-grade children (n = 291) with coexisting math and reading difficulties, math difficulties, reading difficulties, or no learning difficulties were compared. A second analysis compared those with severe math learning difficulties, low average achievement in math, and no learning difficulties. Math fact errors were related to the severity of the math difficulties, not to reading status. Contrary to predictions, children with poorer reading, regardless of math achievement, committed more visually based errors. Operation switch errors were not systematically related to group membership. Teacher ratings of behavioral inattention were related to accuracy, math fact errors, and procedural bugs. The findings are discussed with respect to hypotheses about the cognitive origins of arithmetic errors and in relation to current discussions about how to conceptualize math disabilities. PMID:19380494
-
Choose and choose again: appearance-reality errors, pragmatics and logical ability.
Deák, Gedeon O; Enright, Brian
2006-05-01
In the Appearance/Reality (AR) task some 3- and 4-year-old children make perseverative errors: they choose the same word for the appearance and the function of a deceptive object. Are these errors specific to the AR task, or signs of a general question-answering problem? Preschoolers completed five tasks: AR; simple successive forced-choice question pairs (QP); flexible naming of objects (FN); working memory (WM) span; and indeterminacy detection (ID). AR errors correlated with QP errors. Insensitivity to indeterminacy predicted perseveration in both tasks. Neither WM span nor flexible naming predicted other measures. Age predicted sensitivity to indeterminacy. These findings suggest that AR tests measure a pragmatic understanding; specifically, different questions about a topic usually call for different answers. This understanding is related to the ability to detect indeterminacy of each question in a series. AR errors are unrelated to the ability to represent an object as belonging to multiple categories, to working memory span, or to inhibiting previously activated words.
-
Wiese, Steffen; Teutenberg, Thorsten; Schmidt, Torsten C
2011-09-28
In the present work it is shown that the linear elution strength (LES) model which was adapted from temperature-programming gas chromatography (GC) can also be employed to predict retention times for segmented-temperature gradients based on temperature-gradient input data in liquid chromatography (LC) with high accuracy. The LES model assumes that retention times for isothermal separations can be predicted based on two temperature gradients and is employed to calculate the retention factor of an analyte when changing the start temperature of the temperature gradient. In this study it was investigated whether this approach can also be employed in LC. It was shown that this approximation cannot be transferred to temperature-programmed LC where a temperature range from 60°C up to 180°C is investigated. Major relative errors up to 169.6% were observed for isothermal retention factor predictions. In order to predict retention times for temperature gradients with different start temperatures in LC, another relationship is required to describe the influence of temperature on retention. Therefore, retention times for isothermal separations based on isothermal input runs were predicted using a plot of the natural logarithm of the retention factor vs. the inverse temperature and a plot of the natural logarithm of the retention factor vs. temperature. It could be shown that a plot of lnk vs. T yields more reliable isothermal/isocratic retention time predictions than a plot of lnk vs. 1/T which is usually employed. Hence, in order to predict retention times for temperature-gradients with different start temperatures in LC, two temperature gradient and two isothermal measurements have been employed. In this case, retention times can be predicted with a maximal relative error of 5.5% (average relative error: 2.9%). In comparison, if the start temperature of the simulated temperature gradient is equal to the start temperature of the input data, only two temperature-gradient measurements are required. Under these conditions, retention times can be predicted with a maximal relative error of 4.3% (average relative error: 2.2%). As an example, the systematic method development for an isothermal as well as a temperature gradient separation of selected sulfonamides by means of the adapted LES model is demonstrated using a pure water mobile phase. Both methods are compared and it is shown that the temperature-gradient separation provides some advantages over the isothermal separation in terms of limits of detection and analysis time. Copyright © 2011 Elsevier B.V. All rights reserved.
-
Green, Christopher T.; Zhang, Yong; Jurgens, Bryant C.; Starn, J. Jeffrey; Landon, Matthew K.
2014-01-01
Analytical models of the travel time distribution (TTD) from a source area to a sample location are often used to estimate groundwater ages and solute concentration trends. The accuracies of these models are not well known for geologically complex aquifers. In this study, synthetic datasets were used to quantify the accuracy of four analytical TTD models as affected by TTD complexity, observation errors, model selection, and tracer selection. Synthetic TTDs and tracer data were generated from existing numerical models with complex hydrofacies distributions for one public-supply well and 14 monitoring wells in the Central Valley, California. Analytical TTD models were calibrated to synthetic tracer data, and prediction errors were determined for estimates of TTDs and conservative tracer (NO3−) concentrations. Analytical models included a new, scale-dependent dispersivity model (SDM) for two-dimensional transport from the watertable to a well, and three other established analytical models. The relative influence of the error sources (TTD complexity, observation error, model selection, and tracer selection) depended on the type of prediction. Geological complexity gave rise to complex TTDs in monitoring wells that strongly affected errors of the estimated TTDs. However, prediction errors for NO3− and median age depended more on tracer concentration errors. The SDM tended to give the most accurate estimates of the vertical velocity and other predictions, although TTD model selection had minor effects overall. Adding tracers improved predictions if the new tracers had different input histories. Studies using TTD models should focus on the factors that most strongly affect the desired predictions.
-
Lahat, Ayelet; Lamm, Connie; Chronis-Tuscano, Andrea; Pine, Daniel S; Henderson, Heather A; Fox, Nathan A
2014-04-01
Behavioral inhibition (BI) is an early childhood temperament characterized by fearful responses to novelty and avoidance of social interactions. During adolescence, a subset of children with stable childhood BI develop social anxiety disorder and concurrently exhibit increased error monitoring. The current study examines whether increased error monitoring in 7-year-old, behaviorally inhibited children prospectively predicts risk for symptoms of social phobia at age 9 years. A total of 291 children were characterized on BI at 24 and 36 months of age. Children were seen again at 7 years of age, when they performed a Flanker task, and event-related potential (ERP) indices of response monitoring were generated. At age 9, self- and maternal-report of social phobia symptoms were obtained. Children high in BI, compared to those low in BI, displayed increased error monitoring at age 7, as indexed by larger (i.e., more negative) error-related negativity (ERN) amplitudes. In addition, early BI was related to later childhood social phobia symptoms at age 9 among children with a large difference in amplitude between ERN and correct-response negativity (CRN) at age 7. Heightened error monitoring predicts risk for later social phobia symptoms in children with high BI. Research assessing response monitoring in children with BI may refine our understanding of the mechanisms underlying risk for later anxiety disorders and inform prevention efforts. Copyright © 2014 American Academy of Child and Adolescent Psychiatry. All rights reserved.
-
Zhang, Ji-Li; Liu, Bo-Fei; Di, Xue-Ying; Chu, Teng-Fei; Jin, Sen
2012-11-01
Taking fuel moisture content, fuel loading, and fuel bed depth as controlling factors, the fuel beds of Mongolian oak leaves in Maoershan region of Northeast China in field were simulated, and a total of one hundred experimental burnings under no-wind and zero-slope conditions were conducted in laboratory, with the effects of the fuel moisture content, fuel loading, and fuel bed depth on the flame length and its residence time analyzed and the multivariate linear prediction models constructed. The results indicated that fuel moisture content had a significant negative liner correlation with flame length, but less correlation with flame residence time. Both the fuel loading and the fuel bed depth were significantly positively correlated with flame length and its residence time. The interactions of fuel bed depth with fuel moisture content and fuel loading had significant effects on the flame length, while the interactions of fuel moisture content with fuel loading and fuel bed depth affected the flame residence time significantly. The prediction model of flame length had better prediction effect, which could explain 83.3% of variance, with a mean absolute error of 7.8 cm and a mean relative error of 16.2%, while the prediction model of flame residence time was not good enough, which could only explain 54% of variance, with a mean absolute error of 9.2 s and a mean relative error of 18.6%.
-
Cook, Sarah F; Roberts, Jessica K; Samiee-Zafarghandy, Samira; Stockmann, Chris; King, Amber D; Deutsch, Nina; Williams, Elaine F; Allegaert, Karel; Wilkins, Diana G; Sherwin, Catherine M T; van den Anker, John N
2016-01-01
The aims of this study were to develop a population pharmacokinetic model for intravenous paracetamol in preterm and term neonates and to assess the generalizability of the model by testing its predictive performance in an external dataset. Nonlinear mixed-effects models were constructed from paracetamol concentration-time data in NONMEM 7.2. Potential covariates included body weight, gestational age, postnatal age, postmenstrual age, sex, race, total bilirubin, and estimated glomerular filtration rate. An external dataset was used to test the predictive performance of the model through calculation of bias, precision, and normalized prediction distribution errors. The model-building dataset included 260 observations from 35 neonates with a mean gestational age of 33.6 weeks [standard deviation (SD) 6.6]. Data were well-described by a one-compartment model with first-order elimination. Weight predicted paracetamol clearance and volume of distribution, which were estimated as 0.348 L/h (5.5 % relative standard error; 30.8 % coefficient of variation) and 2.46 L (3.5 % relative standard error; 14.3 % coefficient of variation), respectively, at the mean subject weight of 2.30 kg. An external evaluation was performed on an independent dataset that included 436 observations from 60 neonates with a mean gestational age of 35.6 weeks (SD 4.3). The median prediction error was 10.1 % [95 % confidence interval (CI) 6.1-14.3] and the median absolute prediction error was 25.3 % (95 % CI 23.1-28.1). Weight predicted intravenous paracetamol pharmacokinetics in neonates ranging from extreme preterm to full-term gestational status. External evaluation suggested that these findings should be generalizable to other similar patient populations.
-
Cook, Sarah F.; Roberts, Jessica K.; Samiee-Zafarghandy, Samira; Stockmann, Chris; King, Amber D.; Deutsch, Nina; Williams, Elaine F.; Allegaert, Karel; Sherwin, Catherine M. T.; van den Anker, John N.
2017-01-01
Objectives The aims of this study were to develop a population pharmacokinetic model for intravenous paracetamol in preterm and term neonates and to assess the generalizability of the model by testing its predictive performance in an external dataset. Methods Nonlinear mixed-effects models were constructed from paracetamol concentration–time data in NONMEM 7.2. Potential covariates included body weight, gestational age, postnatal age, postmenstrual age, sex, race, total bilirubin, and estimated glomerular filtration rate. An external dataset was used to test the predictive performance of the model through calculation of bias, precision, and normalized prediction distribution errors. Results The model-building dataset included 260 observations from 35 neonates with a mean gestational age of 33.6 weeks [standard deviation (SD) 6.6]. Data were well-described by a one-compartment model with first-order elimination. Weight predicted paracetamol clearance and volume of distribution, which were estimated as 0.348 L/h (5.5 % relative standard error; 30.8 % coefficient of variation) and 2.46 L (3.5 % relative standard error; 14.3 % coefficient of variation), respectively, at the mean subject weight of 2.30 kg. An external evaluation was performed on an independent dataset that included 436 observations from 60 neonates with a mean gestational age of 35.6 weeks (SD 4.3). The median prediction error was 10.1 % [95 % confidence interval (CI) 6.1–14.3] and the median absolute prediction error was 25.3 % (95 % CI 23.1–28.1). Conclusions Weight predicted intravenous paracetamol pharmacokinetics in neonates ranging from extreme preterm to full-term gestational status. External evaluation suggested that these findings should be generalizable to other similar patient populations. PMID:26201306
-
Impact of SST Anomaly Events over the Kuroshio-Oyashio Extension on the "Summer Prediction Barrier"
NASA Astrophysics Data System (ADS)
Wu, Yujie; Duan, Wansuo
2018-04-01
The "summer prediction barrier" (SPB) of SST anomalies (SSTA) over the Kuroshio-Oyashio Extension (KOE) refers to the phenomenon that prediction errors of KOE-SSTA tend to increase rapidly during boreal summer, resulting in large prediction uncertainties. The fast error growth associated with the SPB occurs in the mature-to-decaying transition phase, which is usually during the August-September-October (ASO) season, of the KOE-SSTA events to be predicted. Thus, the role of KOE-SSTA evolutionary characteristics in the transition phase in inducing the SPB is explored by performing perfect model predictability experiments in a coupled model, indicating that the SSTA events with larger mature-to-decaying transition rates (Category-1) favor a greater possibility of yielding a more significant SPB than those events with smaller transition rates (Category-2). The KOE-SSTA events in Category-1 tend to have more significant anomalous Ekman pumping in their transition phase, resulting in larger prediction errors of vertical oceanic temperature advection associated with the SSTA events. Consequently, Category-1 events possess faster error growth and larger prediction errors. In addition, the anomalous Ekman upwelling (downwelling) in the ASO season also causes SSTA cooling (warming), accelerating the transition rates of warm (cold) KOE-SSTA events. Therefore, the SSTA transition rate and error growth rate are both related with the anomalous Ekman pumping of the SSTA events to be predicted in their transition phase. This may explain why the SSTA events transferring more rapidly from the mature to decaying phase tend to have a greater possibility of yielding a more significant SPB.
-
Neural dynamics of reward probability coding: a Magnetoencephalographic study in humans
Thomas, Julie; Vanni-Mercier, Giovanna; Dreher, Jean-Claude
2013-01-01
Prediction of future rewards and discrepancy between actual and expected outcomes (prediction error) are crucial signals for adaptive behavior. In humans, a number of fMRI studies demonstrated that reward probability modulates these two signals in a large brain network. Yet, the spatio-temporal dynamics underlying the neural coding of reward probability remains unknown. Here, using magnetoencephalography, we investigated the neural dynamics of prediction and reward prediction error computations while subjects learned to associate cues of slot machines with monetary rewards with different probabilities. We showed that event-related magnetic fields (ERFs) arising from the visual cortex coded the expected reward value 155 ms after the cue, demonstrating that reward value signals emerge early in the visual stream. Moreover, a prediction error was reflected in ERF peaking 300 ms after the rewarded outcome and showing decreasing amplitude with higher reward probability. This prediction error signal was generated in a network including the anterior and posterior cingulate cortex. These findings pinpoint the spatio-temporal characteristics underlying reward probability coding. Together, our results provide insights into the neural dynamics underlying the ability to learn probabilistic stimuli-reward contingencies. PMID:24302894
-
Gurdak, Jason J.; Qi, Sharon L.; Geisler, Michael L.
2009-01-01
The U.S. Geological Survey Raster Error Propagation Tool (REPTool) is a custom tool for use with the Environmental System Research Institute (ESRI) ArcGIS Desktop application to estimate error propagation and prediction uncertainty in raster processing operations and geospatial modeling. REPTool is designed to introduce concepts of error and uncertainty in geospatial data and modeling and provide users of ArcGIS Desktop a geoprocessing tool and methodology to consider how error affects geospatial model output. Similar to other geoprocessing tools available in ArcGIS Desktop, REPTool can be run from a dialog window, from the ArcMap command line, or from a Python script. REPTool consists of public-domain, Python-based packages that implement Latin Hypercube Sampling within a probabilistic framework to track error propagation in geospatial models and quantitatively estimate the uncertainty of the model output. Users may specify error for each input raster or model coefficient represented in the geospatial model. The error for the input rasters may be specified as either spatially invariant or spatially variable across the spatial domain. Users may specify model output as a distribution of uncertainty for each raster cell. REPTool uses the Relative Variance Contribution method to quantify the relative error contribution from the two primary components in the geospatial model - errors in the model input data and coefficients of the model variables. REPTool is appropriate for many types of geospatial processing operations, modeling applications, and related research questions, including applications that consider spatially invariant or spatially variable error in geospatial data.
-
Determination of nutritional parameters of yoghurts by FT Raman spectroscopy
NASA Astrophysics Data System (ADS)
Czaja, Tomasz; Baranowska, Maria; Mazurek, Sylwester; Szostak, Roman
2018-05-01
FT-Raman quantitative analysis of nutritional parameters of yoghurts was performed with the help of partial least squares models. The relative standard errors of prediction for fat, lactose and protein determination in the quantified commercial samples equalled to 3.9, 3.2 and 3.6%, respectively. Models based on attenuated total reflectance spectra of the liquid yoghurt samples and of dried yoghurt films collected with the single reflection diamond accessory showed relative standard errors of prediction values of 1.6-5.0% and 2.7-5.2%, respectively, for the analysed components. Despite a relatively low signal-to-noise ratio in the obtained spectra, Raman spectroscopy, combined with chemometrics, constitutes a fast and powerful tool for macronutrients quantification in yoghurts. Errors received for attenuated total reflectance method were found to be relatively higher than those for Raman spectroscopy due to inhomogeneity of the analysed samples.
-
Driving Errors in Parkinson’s Disease: Moving Closer to Predicting On-Road Outcomes
Brumback, Babette; Monahan, Miriam; Malaty, Irene I.; Rodriguez, Ramon L.; Okun, Michael S.; McFarland, Nikolaus R.
2014-01-01
Age-related medical conditions such as Parkinson’s disease (PD) compromise driver fitness. Results from studies are unclear on the specific driving errors that underlie passing or failing an on-road assessment. In this study, we determined the between-group differences and quantified the on-road driving errors that predicted pass or fail on-road outcomes in 101 drivers with PD (mean age = 69.38 ± 7.43) and 138 healthy control (HC) drivers (mean age = 71.76 ± 5.08). Participants with PD had minor differences in demographics and driving habits and history but made more and different driving errors than HC participants. Drivers with PD failed the on-road test to a greater extent than HC drivers (41% vs. 9%), χ2(1) = 35.54, HC N = 138, PD N = 99, p < .001. The driving errors predicting on-road pass or fail outcomes (95% confidence interval, Nagelkerke R2 =.771) were made in visual scanning, signaling, vehicle positioning, speeding (mainly underspeeding, t(61) = 7.004, p < .001, and total errors. Although it is difficult to predict on-road outcomes, this study provides a foundation for doing so. PMID:24367958
-
Predictive monitoring of actions, EEG recordings in virtual reality.
Ozkan, Duru G; Pezzetta, Rachele
2018-04-01
Error-related negativity (ERN) is a signal that is associated with error detection. Joch and colleagues (Joch M, Hegele M, Maurer H, Müller H, Maurer LK. J Neurophysiol 118: 486-495, 2017) successfully separated the ERN as a response to online prediction error from feedback updates. We discuss the role of ERN in action and suggest insights from virtual reality techniques; we consider the potential benefit of self-evaluation in determining the mechanisms of ERN amplitude; finally, we review the oscillatory activity that has been claimed to accompany ERN.
-
Embedded Model Error Representation and Propagation in Climate Models
NASA Astrophysics Data System (ADS)
Sargsyan, K.; Ricciuto, D. M.; Safta, C.; Thornton, P. E.
2017-12-01
Over the last decade, parametric uncertainty quantification (UQ) methods have reached a level of maturity, while the same can not be said about representation and quantification of structural or model errors. Lack of characterization of model errors, induced by physical assumptions, phenomenological parameterizations or constitutive laws, is a major handicap in predictive science. In particular, e.g. in climate models, significant computational resources are dedicated to model calibration without gaining improvement in predictive skill. Neglecting model errors during calibration/tuning will lead to overconfident and biased model parameters. At the same time, the most advanced methods accounting for model error merely correct output biases, augmenting model outputs with statistical error terms that can potentially violate physical laws, or make the calibrated model ineffective for extrapolative scenarios. This work will overview a principled path for representing and quantifying model errors, as well as propagating them together with the rest of the predictive uncertainty budget, including data noise, parametric uncertainties and surrogate-related errors. Namely, the model error terms will be embedded in select model components rather than as external corrections. Such embedding ensures consistency with physical constraints on model predictions, and renders calibrated model predictions meaningful and robust with respect to model errors. Besides, in the presence of observational data, the approach can effectively differentiate model structural deficiencies from those of data acquisition. The methodology is implemented in UQ Toolkit (www.sandia.gov/uqtoolkit), relying on a host of available forward and inverse UQ tools. We will demonstrate the application of the technique on few application of interest, including ACME Land Model calibration via a wide range of measurements obtained at select sites.
-
Martínez-Martínez, Víctor; Baladrón, Carlos; Gomez-Gil, Jaime; Ruiz-Ruiz, Gonzalo; Navas-Gracia, Luis M; Aguiar, Javier M; Carro, Belén
2012-10-17
This paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN). A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed.
-
Martínez-Martínez, Víctor; Baladrón, Carlos; Gomez-Gil, Jaime; Ruiz-Ruiz, Gonzalo; Navas-Gracia, Luis M.; Aguiar, Javier M.; Carro, Belén
2012-01-01
This paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN). A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed. PMID:23202032
-
NASA Astrophysics Data System (ADS)
Li, Xiaojing; Tang, Youmin; Yao, Zhixiong
2017-04-01
The predictability of the convection related to the Madden-Julian Oscillation (MJO) is studied using a coupled model CESM (Community Earth System Model) and the climatically relevant singular vector (CSV) approach. The CSV approach is an ensemble-based strategy to calculate the optimal initial error on climate scale. In this study, we focus on the optimal initial error of the sea surface temperature in Indian Ocean, where is the location of the MJO onset. Six MJO events are chosen from the 10 years model simulation output. The results show that the large values of the SVs are mainly located in the bay of Bengal and the south central IO (around (25°S, 90°E)), which is a meridional dipole-like pattern. The fast error growth of the CSVs have important impacts on the prediction of the convection related to the MJO. The initial perturbations with the SV pattern result in the deep convection damping more quickly in the east Pacific Ocean. Moreover, the sensitivity studies of the CSVs show that different initial fields do not affect the CSVs obviously, while the perturbation domain is a more responsive factor to the CSVs. The rapid growth of the CSVs is found to be related to the west bay of Bengal, where the wind stress starts to be perturbed due to the CSV initial error. These results contribute to the establishment of an ensemble prediction system, as well as the optimal observation network. In addition, the analysis of the error growth can provide us some enlightment about the relationship between SST and the intraseasonal convection related to the MJO.
-
Climbing fibers predict movement kinematics and performance errors.
Streng, Martha L; Popa, Laurentiu S; Ebner, Timothy J
2017-09-01
Requisite for understanding cerebellar function is a complete characterization of the signals provided by complex spike (CS) discharge of Purkinje cells, the output neurons of the cerebellar cortex. Numerous studies have provided insights into CS function, with the most predominant view being that they are evoked by error events. However, several reports suggest that CSs encode other aspects of movements and do not always respond to errors or unexpected perturbations. Here, we evaluated CS firing during a pseudo-random manual tracking task in the monkey ( Macaca mulatta ). This task provides extensive coverage of the work space and relative independence of movement parameters, delivering a robust data set to assess the signals that activate climbing fibers. Using reverse correlation, we determined feedforward and feedback CSs firing probability maps with position, velocity, and acceleration, as well as position error, a measure of tracking performance. The direction and magnitude of the CS modulation were quantified using linear regression analysis. The major findings are that CSs significantly encode all three kinematic parameters and position error, with acceleration modulation particularly common. The modulation is not related to "events," either for position error or kinematics. Instead, CSs are spatially tuned and provide a linear representation of each parameter evaluated. The CS modulation is largely predictive. Similar analyses show that the simple spike firing is modulated by the same parameters as the CSs. Therefore, CSs carry a broader array of signals than previously described and argue for climbing fiber input having a prominent role in online motor control. NEW & NOTEWORTHY This article demonstrates that complex spike (CS) discharge of cerebellar Purkinje cells encodes multiple parameters of movement, including motor errors and kinematics. The CS firing is not driven by error or kinematic events; instead it provides a linear representation of each parameter. In contrast with the view that CSs carry feedback signals, the CSs are predominantly predictive of upcoming position errors and kinematics. Therefore, climbing fibers carry multiple and predictive signals for online motor control. Copyright © 2017 the American Physiological Society.
-
Proximal antecedents and correlates of adopted error approach: a self-regulatory perspective.
Van Dyck, Cathy; Van Hooft, Edwin; De Gilder, Dick; Liesveld, Lillian
2010-01-01
The current study aims to further investigate earlier established advantages of an error mastery approach over an error aversion approach. The two main purposes of the study relate to (1) self-regulatory traits (i.e., goal orientation and action-state orientation) that may predict which error approach (mastery or aversion) is adopted, and (2) proximal, psychological processes (i.e., self-focused attention and failure attribution) that relate to adopted error approach. In the current study participants' goal orientation and action-state orientation were assessed, after which they worked on an error-prone task. Results show that learning goal orientation related to error mastery, while state orientation related to error aversion. Under a mastery approach, error occurrence did not result in cognitive resources "wasted" on self-consciousness. Rather, attention went to internal-unstable, thus controllable, improvement oriented causes of error. Participants that had adopted an aversion approach, in contrast, experienced heightened self-consciousness and attributed failure to internal-stable or external causes. These results imply that when working on an error-prone task, people should be stimulated to take on a mastery rather than an aversion approach towards errors.
-
Predictive accuracy of a ground-water model--Lessons from a postaudit
Konikow, Leonard F.
1986-01-01
Hydrogeologic studies commonly include the development, calibration, and application of a deterministic simulation model. To help assess the value of using such models to make predictions, a postaudit was conducted on a previously studied area in the Salt River and lower Santa Cruz River basins in central Arizona. A deterministic, distributed-parameter model of the ground-water system in these alluvial basins was calibrated by Anderson (1968) using about 40 years of data (1923–64). The calibrated model was then used to predict future water-level changes during the next 10 years (1965–74). Examination of actual water-level changes in 77 wells from 1965–74 indicates a poor correlation between observed and predicted water-level changes. The differences have a mean of 73 ft that is, predicted declines consistently exceeded those observed and a standard deviation of 47 ft. The bias in the predicted water-level change can be accounted for by the large error in the assumed total pumpage during the prediction period. However, the spatial distribution of errors in predicted water-level change does not correlate with the spatial distribution of errors in pumpage. Consequently, the lack of precision probably is not related only to errors in assumed pumpage, but may indicate the presence of other sources of error in the model, such as the two-dimensional representation of a three-dimensional problem or the lack of consideration of land-subsidence processes. This type of postaudit is a valuable method of verifying a model, and an evaluation of predictive errors can provide an increased understanding of the system and aid in assessing the value of undertaking development of a revised model.
-
NASA Astrophysics Data System (ADS)
Gao, Zhi-yu; Kang, Yu; Li, Yan-shuai; Meng, Chao; Pan, Tao
2018-04-01
Elevated-temperature flow behavior of a novel Ni-Cr-Mo-B ultra-heavy-plate steel was investigated by conducting hot compressive deformation tests on a Gleeble-3800 thermo-mechanical simulator at a temperature range of 1123 K–1423 K with a strain rate range from 0.01 s‑1 to10 s‑1 and a height reduction of 70%. Based on the experimental results, classic strain-compensated Arrhenius-type, a new revised strain-compensated Arrhenius-type and classic modified Johnson-Cook constitutive models were developed for predicting the high-temperature deformation behavior of the steel. The predictability of these models were comparatively evaluated in terms of statistical parameters including correlation coefficient (R), average absolute relative error (AARE), average root mean square error (RMSE), normalized mean bias error (NMBE) and relative error. The statistical results indicate that the new revised strain-compensated Arrhenius-type model could give prediction of elevated-temperature flow stress for the steel accurately under the entire process conditions. However, the predicted values by the classic modified Johnson-Cook model could not agree well with the experimental values, and the classic strain-compensated Arrhenius-type model could track the deformation behavior more accurately compared with the modified Johnson-Cook model, but less accurately with the new revised strain-compensated Arrhenius-type model. In addition, reasons of differences in predictability of these models were discussed in detail.
-
Trehan, Sumeet; Carlberg, Kevin T.; Durlofsky, Louis J.
2017-07-14
A machine learning–based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (eg, random forests, and LASSO) to map a large set of inexpensively computed “error indicators” (ie, features) produced by the surrogate model at a given time instance to a prediction of the surrogate-model error in a quantity of interest (QoI). This eliminates the need for the user to hand-select a small number of informative features. The methodology requires a training set of parameter instances at which the time-dependent surrogate-model error is computed bymore » simulating both the high-fidelity and surrogate models. Using these training data, the method first determines regression-model locality (via classification or clustering) and subsequently constructs a “local” regression model to predict the time-instantaneous error within each identified region of feature space. We consider 2 uses for the resulting error model: (1) as a correction to the surrogate-model QoI prediction at each time instance and (2) as a way to statistically model arbitrary functions of the time-dependent surrogate-model error (eg, time-integrated errors). We then apply the proposed framework to model errors in reduced-order models of nonlinear oil-water subsurface flow simulations, with time-varying well-control (bottom-hole pressure) parameters. The reduced-order models used in this work entail application of trajectory piecewise linearization in conjunction with proper orthogonal decomposition. Moreover, when the first use of the method is considered, numerical experiments demonstrate consistent improvement in accuracy in the time-instantaneous QoI prediction relative to the original surrogate model, across a large number of test cases. When the second use is considered, results show that the proposed method provides accurate statistical predictions of the time- and well-averaged errors.« less
-
DOE Office of Scientific and Technical Information (OSTI.GOV)
Trehan, Sumeet; Carlberg, Kevin T.; Durlofsky, Louis J.
A machine learning–based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (eg, random forests, and LASSO) to map a large set of inexpensively computed “error indicators” (ie, features) produced by the surrogate model at a given time instance to a prediction of the surrogate-model error in a quantity of interest (QoI). This eliminates the need for the user to hand-select a small number of informative features. The methodology requires a training set of parameter instances at which the time-dependent surrogate-model error is computed bymore » simulating both the high-fidelity and surrogate models. Using these training data, the method first determines regression-model locality (via classification or clustering) and subsequently constructs a “local” regression model to predict the time-instantaneous error within each identified region of feature space. We consider 2 uses for the resulting error model: (1) as a correction to the surrogate-model QoI prediction at each time instance and (2) as a way to statistically model arbitrary functions of the time-dependent surrogate-model error (eg, time-integrated errors). We then apply the proposed framework to model errors in reduced-order models of nonlinear oil-water subsurface flow simulations, with time-varying well-control (bottom-hole pressure) parameters. The reduced-order models used in this work entail application of trajectory piecewise linearization in conjunction with proper orthogonal decomposition. Moreover, when the first use of the method is considered, numerical experiments demonstrate consistent improvement in accuracy in the time-instantaneous QoI prediction relative to the original surrogate model, across a large number of test cases. When the second use is considered, results show that the proposed method provides accurate statistical predictions of the time- and well-averaged errors.« less
-
Hoogeveen, Suzanne; Schjoedt, Uffe; van Elk, Michiel
2018-06-19
This study examines the effects of expected transcranial stimulation on the error(-related) negativity (Ne or ERN) and the sense of agency in participants who perform a cognitive control task. Placebo transcranial direct current stimulation was used to elicit expectations of transcranially induced cognitive improvement or impairment. The improvement/impairment manipulation affected both the Ne/ERN and the sense of agency (i.e., whether participants attributed errors to oneself or the brain stimulation device): Expected improvement increased the ERN in response to errors compared with both impairment and control conditions. Expected impairment made participants falsely attribute errors to the transcranial stimulation. This decrease in sense of agency was correlated with a reduced ERN amplitude. These results show that expectations about transcranial stimulation impact users' neural response to self-generated errors and the attribution of responsibility-especially when actions lead to negative outcomes. We discuss our findings in relation to predictive processing theory according to which the effect of prior expectations on the ERN reflects the brain's attempt to generate predictive models of incoming information. By demonstrating that induced expectations about transcranial stimulation can have effects at a neural level, that is, beyond mere demand characteristics, our findings highlight the potential for placebo brain stimulation as a promising tool for research.
-
Meyer, Alexandria; Proudfit, Greg Hajcak; Bufferd, Sara J; Kujawa, Autumn J; Laptook, Rebecca S; Torpey, Dana C; Klein, Daniel N
2015-07-01
The error-related negativity (ERN) is a negative deflection in the event-related potential (ERP) occurring approximately 50 ms after error commission at fronto-central electrode sites and is thought to reflect the activation of a generic error monitoring system. Several studies have reported an increased ERN in clinically anxious children, and suggest that anxious children are more sensitive to error commission--although the mechanisms underlying this association are not clear. We have previously found that punishing errors results in a larger ERN, an effect that persists after punishment ends. It is possible that learning-related experiences that impact sensitivity to errors may lead to an increased ERN. In particular, punitive parenting might sensitize children to errors and increase their ERN. We tested this possibility in the current study by prospectively examining the relationship between parenting style during early childhood and children's ERN approximately 3 years later. Initially, 295 parents and children (approximately 3 years old) participated in a structured observational measure of parenting behavior, and parents completed a self-report measure of parenting style. At a follow-up assessment approximately 3 years later, the ERN was elicited during a Go/No-Go task, and diagnostic interviews were completed with parents to assess child psychopathology. Results suggested that both observational measures of hostile parenting and self-report measures of authoritarian parenting style uniquely predicted a larger ERN in children 3 years later. We previously reported that children in this sample with anxiety disorders were characterized by an increased ERN. A mediation analysis indicated that ERN magnitude mediated the relationship between harsh parenting and child anxiety disorder. Results suggest that parenting may shape children's error processing through environmental conditioning and thereby risk for anxiety, although future work is needed to confirm this hypothesis.
-
Ballistic projectile trajectory determining system
Karr, Thomas J.
1997-01-01
A computer controlled system determines the three-dimensional trajectory of a ballistic projectile. To initialize the system, predictions of state parameters for a ballistic projectile are received at an estimator. The estimator uses the predictions of the state parameters to estimate first trajectory characteristics of the ballistic projectile. A single stationary monocular sensor then observes the actual first trajectory characteristics of the ballistic projectile. A comparator generates an error value related to the predicted state parameters by comparing the estimated first trajectory characteristics of the ballistic projectile with the observed first trajectory characteristics of the ballistic projectile. If the error value is equal to or greater than a selected limit, the predictions of the state parameters are adjusted. New estimates for the trajectory characteristics of the ballistic projectile are made and are then compared with actual observed trajectory characteristics. This process is repeated until the error value is less than the selected limit. Once the error value is less than the selected limit, a calculator calculates trajectory characteristics such a the origin and destination of the ballistic projectile.
-
Conflict Probability Estimation for Free Flight
NASA Technical Reports Server (NTRS)
Paielli, Russell A.; Erzberger, Heinz
1996-01-01
The safety and efficiency of free flight will benefit from automated conflict prediction and resolution advisories. Conflict prediction is based on trajectory prediction and is less certain the farther in advance the prediction, however. An estimate is therefore needed of the probability that a conflict will occur, given a pair of predicted trajectories and their levels of uncertainty. A method is developed in this paper to estimate that conflict probability. The trajectory prediction errors are modeled as normally distributed, and the two error covariances for an aircraft pair are combined into a single equivalent covariance of the relative position. A coordinate transformation is then used to derive an analytical solution. Numerical examples and Monte Carlo validation are presented.
-
Pharmacogenetic excitation of dorsomedial prefrontal cortex restores fear prediction error.
Yau, Joanna Oi-Yue; McNally, Gavan P
2015-01-07
Pavlovian conditioning involves encoding the predictive relationship between a conditioned stimulus (CS) and an unconditioned stimulus, so that synaptic plasticity and learning is instructed by prediction error. Here we used pharmacogenetic techniques to show a causal relation between activity of rat dorsomedial prefrontal cortex (dmPFC) neurons and fear prediction error. We expressed the excitatory hM3Dq designer receptor exclusively activated by a designer drug (DREADD) in dmPFC and isolated actions of prediction error by using an associative blocking design. Rats were trained to fear the visual CS (CSA) in stage I via pairings with footshock. Then in stage II, rats received compound presentations of visual CSA and auditory CS (CSB) with footshock. This prior fear conditioning of CSA reduced the prediction error during stage II to block fear learning to CSB. The group of rats that received AAV-hSYN-eYFP vector that was treated with clozapine-N-oxide (CNO; 3 mg/kg, i.p.) before stage II showed blocking when tested in the absence of CNO the next day. In contrast, the groups that received AAV-hSYN-hM3Dq and AAV-CaMKIIα-hM3Dq that were treated with CNO before stage II training did not show blocking; learning toward CSB was restored. This restoration of prediction error and fear learning was specific to the injection of CNO because groups that received AAV-hSYN-hM3Dq and AAV-CaMKIIα-hM3Dq that were injected with vehicle before stage II training did show blocking. These effects were not attributable to the DREADD manipulation enhancing learning or arousal, increasing fear memory strength or asymptotic levels of fear learning, or altering fear memory retrieval. Together, these results identify a causal role for dmPFC in a signature of adaptive behavior: using the past to predict future danger and learning from errors in these predictions. Copyright © 2015 the authors 0270-6474/15/350074-10$15.00/0.
-
Toward Biopredictive Dissolution for Enteric Coated Dosage Forms.
Al-Gousous, J; Amidon, G L; Langguth, P
2016-06-06
The aim of this work was to develop a phosphate buffer based dissolution method for enteric-coated formulations with improved biopredictivity for fasted conditions. Two commercially available enteric-coated aspirin products were used as model formulations (Aspirin Protect 300 mg, and Walgreens Aspirin 325 mg). The disintegration performance of these products in a physiological 8 mM pH 6.5 bicarbonate buffer (representing the conditions in the proximal small intestine) was used as a standard to optimize the employed phosphate buffer molarity. To account for the fact that a pH and buffer molarity gradient exists along the small intestine, the introduction of such a gradient was proposed for products with prolonged lag times (when it leads to a release lower than 75% in the first hour post acid stage) in the proposed buffer. This would allow the method also to predict the performance of later-disintegrating products. Dissolution performance using the accordingly developed method was compared to that observed when using two well-established dissolution methods: the United States Pharmacopeia (USP) method and blank fasted state simulated intestinal fluid (FaSSIF). The resulting dissolution profiles were convoluted using GastroPlus software to obtain predicted pharmacokinetic profiles. A pharmacokinetic study on healthy human volunteers was performed to evaluate the predictions made by the different dissolution setups. The novel method provided the best prediction, by a relatively wide margin, for the difference between the lag times of the two tested formulations, indicating its being able to predict the post gastric emptying onset of drug release with reasonable accuracy. Both the new and the blank FaSSIF methods showed potential for establishing in vitro-in vivo correlation (IVIVC) concerning the prediction of Cmax and AUC0-24 (prediction errors not more than 20%). However, these predictions are strongly affected by the highly variable first pass metabolism necessitating the evaluation of an absorption rate metric that is more independent of the first-pass effect. The Cmax/AUC0-24 ratio was selected for this purpose. Regarding this metric's predictions, the new method provided very good prediction of the two products' performances relative to each other (only 1.05% prediction error in this regard), while its predictions for the individual products' values in absolute terms were borderline, narrowly missing the regulatory 20% prediction error limits (21.51% for Aspirin Protect and 22.58% for Walgreens Aspirin). The blank FaSSIF-based method provided good Cmax/AUC0-24 ratio prediction, in absolute terms, for Aspirin Protect (9.05% prediction error), but its prediction for Walgreens Aspirin (33.97% prediction error) was overwhelmingly poor. Thus it gave practically the same average but much higher maximum prediction errors compared to the new method, and it was strongly overdiscriminating as for predicting their performances relative to one another. The USP method, despite not being overdiscriminating, provided poor predictions of the individual products' Cmax/AUC0-24 ratios. This indicates that, overall, the new method is of improved biopredictivity compared to established methods.
-
Moors, Pieter
2015-01-01
In a recent functional magnetic resonance imaging study, Kok and de Lange (2014) observed that BOLD activity for a Kanizsa illusory shape stimulus, in which pacmen-like inducers elicit an illusory shape percept, was either enhanced or suppressed relative to a nonillusory control configuration depending on whether the spatial profile of BOLD activity in early visual cortex was related to the illusory shape or the inducers, respectively. The authors argued that these findings fit well with the predictive coding framework, because top-down predictions related to the illusory shape are not met with bottom-up sensory input and hence the feedforward error signal is enhanced. Conversely, for the inducing elements, there is a match between top-down predictions and input, leading to a decrease in error. Rather than invoking predictive coding as the explanatory framework, the suppressive effect related to the inducers might be caused by neural adaptation to perceptually stable input due to the trial sequence used in the experiment.
-
Corlett, P.R.; Fletcher, P.C.
2012-01-01
Healthy people sometimes report experiences and beliefs that are strikingly similar to the symptoms of psychosis in their bizarreness and the apparent lack of evidence supporting them. An important question is whether this represents merely a superficial resemblance or whether there is a genuine and deep similarity indicating, as some have suggested, a continuum between odd but healthy beliefs and the symptoms of psychotic illness. We sought to shed light on this question by determining whether the neural marker for prediction error - previously shown to be altered in early psychosis – is comparably altered in healthy individuals reporting schizotypal experiences and beliefs. We showed that non-clinical schizotypal experiences were significantly correlated with aberrant frontal and striatal prediction error signal. This correlation related to the distress associated with the beliefs. Given our previous observations that patients with first episode psychosis show altered neural responses to prediction error and that this alteration, in turn, relates to the severity of their delusional ideation, our results provide novel evidence in support of the view that schizotypy relates to psychosis at more than just a superficial descriptive level. However, the picture is a complex one in which the experiences, though associated with altered striatal responding, may provoke distress but may nonetheless be explained away, while an additional alteration in frontal cortical responding may allow the beliefs to become more delusion-like: intrusive and distressing. PMID:23079501
-
Hess, G.W.; Bohman, L.R.
1996-01-01
Techniques for estimating monthly mean streamflow at gaged sites and monthly streamflow duration characteristics at ungaged sites in central Nevada were developed using streamflow records at six gaged sites and basin physical and climatic characteristics. Streamflow data at gaged sites were related by regression techniques to concurrent flows at nearby gaging stations so that monthly mean streamflows for periods of missing or no record can be estimated for gaged sites in central Nevada. The standard error of estimate for relations at these sites ranged from 12 to 196 percent. Also, monthly streamflow data for selected percent exceedence levels were used in regression analyses with basin and climatic variables to determine relations for ungaged basins for annual and monthly percent exceedence levels. Analyses indicate that the drainage area and percent of drainage area at altitudes greater than 10,000 feet are the most significant variables. For the annual percent exceedence, the standard error of estimate of the relations for ungaged sites ranged from 51 to 96 percent and standard error of prediction for ungaged sites ranged from 96 to 249 percent. For the monthly percent exceedence values, the standard error of estimate of the relations ranged from 31 to 168 percent, and the standard error of prediction ranged from 115 to 3,124 percent. Reliability and limitations of the estimating methods are described.
-
DOE Office of Scientific and Technical Information (OSTI.GOV)
Singh, Kunwar Pal, E-mail: k-psingh@yahoo.com; Department of Physics, Shri Venkateshwara University, Gajraula, Amroha, Uttar Pradesh 244236; Arya, Rashmi
2015-09-14
We have investigated the effect of initial phase on error in electron energy obtained using paraxial approximation to study electron acceleration by a focused laser pulse in vacuum using a three dimensional test-particle simulation code. The error is obtained by comparing the energy of the electron for paraxial approximation and seventh-order correction description of the fields of Gaussian laser. The paraxial approximation predicts wrong laser divergence and wrong electron escape time from the pulse which leads to prediction of higher energy. The error shows strong phase dependence for the electrons lying along the axis of the laser for linearly polarizedmore » laser pulse. The relative error may be significant for some specific values of initial phase even at moderate values of laser spot sizes. The error does not show initial phase dependence for a circularly laser pulse.« less
-
Vassena, Eliana; Deraeve, James; Alexander, William H
2017-10-01
Human behavior is strongly driven by the pursuit of rewards. In daily life, however, benefits mostly come at a cost, often requiring that effort be exerted to obtain potential benefits. Medial PFC (MPFC) and dorsolateral PFC (DLPFC) are frequently implicated in the expectation of effortful control, showing increased activity as a function of predicted task difficulty. Such activity partially overlaps with expectation of reward and has been observed both during decision-making and during task preparation. Recently, novel computational frameworks have been developed to explain activity in these regions during cognitive control, based on the principle of prediction and prediction error (predicted response-outcome [PRO] model [Alexander, W. H., & Brown, J. W. Medial prefrontal cortex as an action-outcome predictor. Nature Neuroscience, 14, 1338-1344, 2011], hierarchical error representation [HER] model [Alexander, W. H., & Brown, J. W. Hierarchical error representation: A computational model of anterior cingulate and dorsolateral prefrontal cortex. Neural Computation, 27, 2354-2410, 2015]). Despite the broad explanatory power of these models, it is not clear whether they can also accommodate effects related to the expectation of effort observed in MPFC and DLPFC. Here, we propose a translation of these computational frameworks to the domain of effort-based behavior. First, we discuss how the PRO model, based on prediction error, can explain effort-related activity in MPFC, by reframing effort-based behavior in a predictive context. We propose that MPFC activity reflects monitoring of motivationally relevant variables (such as effort and reward), by coding expectations and discrepancies from such expectations. Moreover, we derive behavioral and neural model-based predictions for healthy controls and clinical populations with impairments of motivation. Second, we illustrate the possible translation to effort-based behavior of the HER model, an extended version of PRO model based on hierarchical error prediction, developed to explain MPFC-DLPFC interactions. We derive behavioral predictions that describe how effort and reward information is coded in PFC and how changing the configuration of such environmental information might affect decision-making and task performance involving motivation.
-
The balanced mind: the variability of task-unrelated thoughts predicts error monitoring
Allen, Micah; Smallwood, Jonathan; Christensen, Joanna; Gramm, Daniel; Rasmussen, Beinta; Jensen, Christian Gaden; Roepstorff, Andreas; Lutz, Antoine
2013-01-01
Self-generated thoughts unrelated to ongoing activities, also known as “mind-wandering,” make up a substantial portion of our daily lives. Reports of such task-unrelated thoughts (TUTs) predict both poor performance on demanding cognitive tasks and blood-oxygen-level-dependent (BOLD) activity in the default mode network (DMN). However, recent findings suggest that TUTs and the DMN can also facilitate metacognitive abilities and related behaviors. To further understand these relationships, we examined the influence of subjective intensity, ruminative quality, and variability of mind-wandering on response inhibition and monitoring, using the Error Awareness Task (EAT). We expected to replicate links between TUT and reduced inhibition, and explored whether variance in TUT would predict improved error monitoring, reflecting a capacity to balance between internal and external cognition. By analyzing BOLD responses to subjective probes and the EAT, we dissociated contributions of the DMN, executive, and salience networks to task performance. While both response inhibition and online TUT ratings modulated BOLD activity in the medial prefrontal cortex (mPFC) of the DMN, the former recruited a more dorsal area implying functional segregation. We further found that individual differences in mean TUTs strongly predicted EAT stop accuracy, while TUT variability specifically predicted levels of error awareness. Interestingly, we also observed co-activation of salience and default mode regions during error awareness, supporting a link between monitoring and TUTs. Altogether our results suggest that although TUT is detrimental to task performance, fluctuations in attention between self-generated and external task-related thought is a characteristic of individuals with greater metacognitive monitoring capacity. Achieving a balance between internally and externally oriented thought may thus aid individuals in optimizing their task performance. PMID:24223545
-
Forster, Sarah E; Zirnheld, Patrick; Shekhar, Anantha; Steinhauer, Stuart R; O'Donnell, Brian F; Hetrick, William P
2017-09-01
Signals carried by the mesencephalic dopamine system and conveyed to anterior cingulate cortex are critically implicated in probabilistic reward learning and performance monitoring. A common evaluative mechanism purportedly subserves both functions, giving rise to homologous medial frontal negativities in feedback- and response-locked event-related brain potentials (the feedback-related negativity (FRN) and the error-related negativity (ERN), respectively), reflecting dopamine-dependent prediction error signals to unexpectedly negative events. Consistent with this model, the dopamine receptor antagonist, haloperidol, attenuates the ERN, but effects on FRN have not yet been evaluated. ERN and FRN were recorded during a temporal interval learning task (TILT) following randomized, double-blind administration of haloperidol (3 mg; n = 18), diphenhydramine (an active control for haloperidol; 25 mg; n = 20), or placebo (n = 21) to healthy controls. Centroparietal positivities, the Pe and feedback-locked P300, were also measured and correlations between ERP measures and behavioral indices of learning, overall accuracy, and post-error compensatory behavior were evaluated. We hypothesized that haloperidol would reduce ERN and FRN, but that ERN would uniquely track automatic, error-related performance adjustments, while FRN would be associated with learning and overall accuracy. As predicted, ERN was reduced by haloperidol and in those exhibiting less adaptive post-error performance; however, these effects were limited to ERNs following fast timing errors. In contrast, the FRN was not affected by drug condition, although increased FRN amplitude was associated with improved accuracy. Significant drug effects on centroparietal positivities were also absent. Our results support a functional and neurobiological dissociation between the ERN and FRN.
-
Toward isolating the role of dopamine in the acquisition of incentive salience attribution.
Chow, Jonathan J; Nickell, Justin R; Darna, Mahesh; Beckmann, Joshua S
2016-10-01
Stimulus-reward learning has been heavily linked to the reward-prediction error learning hypothesis and dopaminergic function. However, some evidence suggests dopaminergic function may not strictly underlie reward-prediction error learning, but may be specific to incentive salience attribution. Utilizing a Pavlovian conditioned approach procedure consisting of two stimuli that were equally reward-predictive (both undergoing reward-prediction error learning) but functionally distinct in regard to incentive salience (levers that elicited sign-tracking and tones that elicited goal-tracking), we tested the differential role of D1 and D2 dopamine receptors and nucleus accumbens dopamine in the acquisition of sign- and goal-tracking behavior and their associated conditioned reinforcing value within individuals. Overall, the results revealed that both D1 and D2 inhibition disrupted performance of sign- and goal-tracking. However, D1 inhibition specifically prevented the acquisition of sign-tracking to a lever, instead promoting goal-tracking and decreasing its conditioned reinforcing value, while neither D1 nor D2 signaling was required for goal-tracking in response to a tone. Likewise, nucleus accumbens dopaminergic lesions disrupted acquisition of sign-tracking to a lever, while leaving goal-tracking in response to a tone unaffected. Collectively, these results are the first evidence of an intraindividual dissociation of dopaminergic function in incentive salience attribution from reward-prediction error learning, indicating that incentive salience, reward-prediction error, and their associated dopaminergic signaling exist within individuals and are stimulus-specific. Thus, individual differences in incentive salience attribution may be reflective of a differential balance in dopaminergic function that may bias toward the attribution of incentive salience, relative to reward-prediction error learning only. Copyright © 2016 Elsevier Ltd. All rights reserved.
-
Metacognition and proofreading: the roles of aging, motivation, and interest.
Hargis, Mary B; Yue, Carole L; Kerr, Tyson; Ikeda, Kenji; Murayama, Kou; Castel, Alan D
2017-03-01
The current study examined younger and older adults' error detection accuracy, prediction calibration, and postdiction calibration on a proofreading task, to determine if age-related differences would be present in this type of common error detection task. Participants were given text passages, and were first asked to predict the percentage of errors they would detect in the passage. They then read the passage and circled errors (which varied in complexity and locality), and made postdictions regarding their performance, before repeating this with another passage and answering a comprehension test of both passages. There were no age-related differences in error detection accuracy, text comprehension, or metacognitive calibration, though participants in both age groups were overconfident overall in their metacognitive judgments. Both groups gave similar ratings of motivation to complete the task. The older adults rated the passages as more interesting than younger adults did, although this level of interest did not appear to influence error-detection performance. The age equivalence in both proofreading ability and calibration suggests that the ability to proofread text passages and the associated metacognitive monitoring used in judging one's own performance are maintained in aging. These age-related similarities persisted when younger adults completed the proofreading tasks on a computer screen, rather than with paper and pencil. The findings provide novel insights regarding the influence that cognitive aging may have on metacognitive accuracy and text processing in an everyday task.
-
NASA Astrophysics Data System (ADS)
Yeganeh, B.; Motlagh, M. Shafie Pour; Rashidi, Y.; Kamalan, H.
2012-08-01
Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS-SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS-SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65-85% for hybrid PLS-SVM model respectively. Also it was found that the hybrid PLS-SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS-SVM model.
-
Crosslinking EEG time-frequency decomposition and fMRI in error monitoring.
Hoffmann, Sven; Labrenz, Franziska; Themann, Maria; Wascher, Edmund; Beste, Christian
2014-03-01
Recent studies implicate a common response monitoring system, being active during erroneous and correct responses. Converging evidence from time-frequency decompositions of the response-related ERP revealed that evoked theta activity at fronto-central electrode positions differentiates correct from erroneous responses in simple tasks, but also in more complex tasks. However, up to now it is unclear how different electrophysiological parameters of error processing, especially at the level of neural oscillations are related, or predictive for BOLD signal changes reflecting error processing at a functional-neuroanatomical level. The present study aims to provide crosslinks between time domain information, time-frequency information, MRI BOLD signal and behavioral parameters in a task examining error monitoring due to mistakes in a mental rotation task. The results show that BOLD signal changes reflecting error processing on a functional-neuroanatomical level are best predicted by evoked oscillations in the theta frequency band. Although the fMRI results in this study account for an involvement of the anterior cingulate cortex, middle frontal gyrus, and the Insula in error processing, the correlation of evoked oscillations and BOLD signal was restricted to a coupling of evoked theta and anterior cingulate cortex BOLD activity. The current results indicate that although there is a distributed functional-neuroanatomical network mediating error processing, only distinct parts of this network seem to modulate electrophysiological properties of error monitoring.
-
Demiral, Şükrü Barış; Golosheykin, Simon; Anokhin, Andrey P
2017-05-01
Detection and evaluation of the mismatch between the intended and actually obtained result of an action (reward prediction error) is an integral component of adaptive self-regulation of behavior. Extensive human and animal research has shown that evaluation of action outcome is supported by a distributed network of brain regions in which the anterior cingulate cortex (ACC) plays a central role, and the integration of distant brain regions into a unified feedback-processing network is enabled by long-range phase synchronization of cortical oscillations in the theta band. Neural correlates of feedback processing are associated with individual differences in normal and abnormal behavior, however, little is known about the role of genetic factors in the cerebral mechanisms of feedback processing. Here we examined genetic influences on functional cortical connectivity related to prediction error in young adult twins (age 18, n=399) using event-related EEG phase coherence analysis in a monetary gambling task. To identify prediction error-specific connectivity pattern, we compared responses to loss and gain feedback. Monetary loss produced a significant increase of theta-band synchronization between the frontal midline region and widespread areas of the scalp, particularly parietal areas, whereas gain resulted in increased synchrony primarily within the posterior regions. Genetic analyses showed significant heritability of frontoparietal theta phase synchronization (24 to 46%), suggesting that individual differences in large-scale network dynamics are under substantial genetic control. We conclude that theta-band synchronization of brain oscillations related to negative feedback reflects genetically transmitted differences in the neural mechanisms of feedback processing. To our knowledge, this is the first evidence for genetic influences on task-related functional brain connectivity assessed using direct real-time measures of neuronal synchronization. Copyright © 2016 Elsevier B.V. All rights reserved.
-
Mendiburu, Andrés Z; de Carvalho, João A; Coronado, Christian R
2015-03-21
Estimation of the lower flammability limits of C-H compounds at 25 °C and 1 atm; at moderate temperatures and in presence of diluent was the objective of this study. A set of 120 C-H compounds was divided into a correlation set and a prediction set of 60 compounds each. The absolute average relative error for the total set was 7.89%; for the correlation set, it was 6.09%; and for the prediction set it was 9.68%. However, it was shown that by considering different sources of experimental data the values were reduced to 6.5% for the prediction set and to 6.29% for the total set. The method showed consistency with Le Chatelier's law for binary mixtures of C-H compounds. When tested for a temperature range from 5 °C to 100 °C, the absolute average relative errors were 2.41% for methane; 4.78% for propane; 0.29% for iso-butane and 3.86% for propylene. When nitrogen was added, the absolute average relative errors were 2.48% for methane; 5.13% for propane; 0.11% for iso-butane and 0.15% for propylene. When carbon dioxide was added, the absolute relative errors were 1.80% for methane; 5.38% for propane; 0.86% for iso-butane and 1.06% for propylene. Copyright © 2014 Elsevier B.V. All rights reserved.
-
Dopamine reward prediction error coding.
Schultz, Wolfram
2016-03-01
Reward prediction errors consist of the differences between received and predicted rewards. They are crucial for basic forms of learning about rewards and make us strive for more rewards-an evolutionary beneficial trait. Most dopamine neurons in the midbrain of humans, monkeys, and rodents signal a reward prediction error; they are activated by more reward than predicted (positive prediction error), remain at baseline activity for fully predicted rewards, and show depressed activity with less reward than predicted (negative prediction error). The dopamine signal increases nonlinearly with reward value and codes formal economic utility. Drugs of addiction generate, hijack, and amplify the dopamine reward signal and induce exaggerated, uncontrolled dopamine effects on neuronal plasticity. The striatum, amygdala, and frontal cortex also show reward prediction error coding, but only in subpopulations of neurons. Thus, the important concept of reward prediction errors is implemented in neuronal hardware.
-
Dopamine reward prediction error coding
Schultz, Wolfram
2016-01-01
Reward prediction errors consist of the differences between received and predicted rewards. They are crucial for basic forms of learning about rewards and make us strive for more rewards—an evolutionary beneficial trait. Most dopamine neurons in the midbrain of humans, monkeys, and rodents signal a reward prediction error; they are activated by more reward than predicted (positive prediction error), remain at baseline activity for fully predicted rewards, and show depressed activity with less reward than predicted (negative prediction error). The dopamine signal increases nonlinearly with reward value and codes formal economic utility. Drugs of addiction generate, hijack, and amplify the dopamine reward signal and induce exaggerated, uncontrolled dopamine effects on neuronal plasticity. The striatum, amygdala, and frontal cortex also show reward prediction error coding, but only in subpopulations of neurons. Thus, the important concept of reward prediction errors is implemented in neuronal hardware. PMID:27069377
-
Seeing the Errors You Feel Enhances Locomotor Performance but Not Learning.
Roemmich, Ryan T; Long, Andrew W; Bastian, Amy J
2016-10-24
In human motor learning, it is thought that the more information we have about our errors, the faster we learn. Here, we show that additional error information can lead to improved motor performance without any concomitant improvement in learning. We studied split-belt treadmill walking that drives people to learn a new gait pattern using sensory prediction errors detected by proprioceptive feedback. When we also provided visual error feedback, participants acquired the new walking pattern far more rapidly and showed accelerated restoration of the normal walking pattern during washout. However, when the visual error feedback was removed during either learning or washout, errors reappeared with performance immediately returning to the level expected based on proprioceptive learning alone. These findings support a model with two mechanisms: a dual-rate adaptation process that learns invariantly from sensory prediction error detected by proprioception and a visual-feedback-dependent process that monitors learning and corrects residual errors but shows no learning itself. We show that our voluntary correction model accurately predicted behavior in multiple situations where visual feedback was used to change acquisition of new walking patterns while the underlying learning was unaffected. The computational and behavioral framework proposed here suggests that parallel learning and error correction systems allow us to rapidly satisfy task demands without necessarily committing to learning, as the relative permanence of learning may be inappropriate or inefficient when facing environments that are liable to change. Copyright © 2016 Elsevier Ltd. All rights reserved.
-
Chiang, Hui-Ying; Hsiao, Ya-Chu; Lee, Huan-Fang
Nurses' safety practices of medication administration, prevention of falls and unplanned extubations, and handover are essentials to patient safety. This study explored the prediction between such safety practices and work environment factors, workload, job satisfaction, and error-reporting culture of 1429 Taiwanese nurses. Nurses' job satisfaction, error-reporting culture, and one environmental factor of nursing quality were found to be major predictors of safety practices. The other environment factors related to professional development and participation in hospital affairs and nurses' workload had limited predictive effects on the safety practices. Increasing nurses' attention to patient safety by improving these predictors is recommended.
-
Ballistic projectile trajectory determining system
Karr, T.J.
1997-05-20
A computer controlled system determines the three-dimensional trajectory of a ballistic projectile. To initialize the system, predictions of state parameters for a ballistic projectile are received at an estimator. The estimator uses the predictions of the state parameters to estimate first trajectory characteristics of the ballistic projectile. A single stationary monocular sensor then observes the actual first trajectory characteristics of the ballistic projectile. A comparator generates an error value related to the predicted state parameters by comparing the estimated first trajectory characteristics of the ballistic projectile with the observed first trajectory characteristics of the ballistic projectile. If the error value is equal to or greater than a selected limit, the predictions of the state parameters are adjusted. New estimates for the trajectory characteristics of the ballistic projectile are made and are then compared with actual observed trajectory characteristics. This process is repeated until the error value is less than the selected limit. Once the error value is less than the selected limit, a calculator calculates trajectory characteristics such a the origin and destination of the ballistic projectile. 8 figs.
-
Iannaccone, Reto; Hauser, Tobias U; Ball, Juliane; Brandeis, Daniel; Walitza, Susanne; Brem, Silvia
2015-10-01
Attention-deficit/hyperactivity disorder (ADHD) is a common disabling psychiatric disorder associated with consistent deficits in error processing, inhibition and regionally decreased grey matter volumes. The diagnosis is based on clinical presentation, interviews and questionnaires, which are to some degree subjective and would benefit from verification through biomarkers. Here, pattern recognition of multiple discriminative functional and structural brain patterns was applied to classify adolescents with ADHD and controls. Functional activation features in a Flanker/NoGo task probing error processing and inhibition along with structural magnetic resonance imaging data served to predict group membership using support vector machines (SVMs). The SVM pattern recognition algorithm correctly classified 77.78% of the subjects with a sensitivity and specificity of 77.78% based on error processing. Predictive regions for controls were mainly detected in core areas for error processing and attention such as the medial and dorsolateral frontal areas reflecting deficient processing in ADHD (Hart et al., in Hum Brain Mapp 35:3083-3094, 2014), and overlapped with decreased activations in patients in conventional group comparisons. Regions more predictive for ADHD patients were identified in the posterior cingulate, temporal and occipital cortex. Interestingly despite pronounced univariate group differences in inhibition-related activation and grey matter volumes the corresponding classifiers failed or only yielded a poor discrimination. The present study corroborates the potential of task-related brain activation for classification shown in previous studies. It remains to be clarified whether error processing, which performed best here, also contributes to the discrimination of useful dimensions and subtypes, different psychiatric disorders, and prediction of treatment success across studies and sites.
-
Trempler, Ima; Binder, Ellen; El-Sourani, Nadiya; Schiffler, Patrick; Tenberge, Jan-Gerd; Schiffer, Anne-Marike; Fink, Gereon R; Schubotz, Ricarda I
2018-06-01
Parkinson's disease (PD), which is caused by degeneration of dopaminergic neurons in the midbrain, results in a heterogeneous clinical picture including cognitive decline. Since the phasic signal of dopamine neurons is proposed to guide learning by signifying mismatches between subjects' expectations and external events, we here investigated whether akinetic-rigid PD patients without mild cognitive impairment exhibit difficulties in dealing with either relevant (requiring flexibility) or irrelevant (requiring stability) prediction errors. Following our previous study on flexibility and stability in prediction (Trempler et al. J Cogn Neurosci 29(2):298-309, 2017), we then assessed whether deficits would correspond with specific structural alterations in dopaminergic regions as well as in inferior frontal cortex, medial prefrontal cortex, and the hippocampus. Twenty-one healthy controls and twenty-one akinetic-rigid PD patients on and off medication performed a task which required to serially predict upcoming items. Switches between predictable sequences had to be indicated via button press, whereas sequence omissions had to be ignored. Independent of the disease, midbrain volume was related to a general response bias to unexpected events, whereas right putamen volume correlated with the ability to discriminate between relevant and irrelevant prediction errors. However, patients compared with healthy participants showed deficits in stabilisation against irrelevant prediction errors, associated with thickness of right inferior frontal gyrus and left medial prefrontal cortex. Flexible updating due to relevant prediction errors was also affected in patients compared with controls and associated with right hippocampus volume. Dopaminergic medication influenced behavioural performance across, but not within the patients. Our exploratory study warrants further research on deficient prediction error processing and its structural correlates as a core of cognitive symptoms occurring already in early stages of the disease.
-
Effect of tumor amplitude and frequency on 4D modeling of Vero4DRT system.
Miura, Hideharu; Ozawa, Shuichi; Hayata, Masahiro; Tsuda, Shintaro; Yamada, Kiyoshi; Nagata, Yasushi
2017-01-01
An important issue in indirect dynamic tumor tracking with the Vero4DRT system is the accuracy of the model predictions of the internal target position based on surrogate infrared (IR) marker measurement. We investigated the predictive uncertainty of 4D modeling using an external IR marker, focusing on the effect of the target and surrogate amplitudes and periods. A programmable respiratory motion table was used to simulate breathing induced organ motion. Sinusoidal motion sequences were produced by a dynamic phantom with different amplitudes and periods. To investigate the 4D modeling error, the following amplitudes (peak-to-peak: 10-40 mm) and periods (2-8 s) were considered. The 95th percentile 4D modeling error (4D- E 95% ) between the detected and predicted target position ( μ + 2SD) was calculated to investigate the 4D modeling error. 4D- E 95% was linearly related to the target motion amplitude with a coefficient of determination R 2 = 0.99 and ranged from 0.21 to 0.88 mm. The 4D modeling error ranged from 1.49 to 0.14 mm and gradually decreased with increasing target motion period. We analyzed the predictive error in 4D modeling and the error due to the amplitude and period of target. 4D modeling error substantially increased with increasing amplitude and decreasing period of the target motion.
-
Parastar, Hadi; Mostafapour, Sara; Azimi, Gholamhasan
2016-01-01
Comprehensive two-dimensional gas chromatography and flame ionization detection combined with unfolded-partial least squares is proposed as a simple, fast and reliable method to assess the quality of gasoline and to detect its potential adulterants. The data for the calibration set are first baseline corrected using a two-dimensional asymmetric least squares algorithm. The number of significant partial least squares components to build the model is determined using the minimum value of root-mean square error of leave-one out cross validation, which was 4. In this regard, blends of gasoline with kerosene, white spirit and paint thinner as frequently used adulterants are used to make calibration samples. Appropriate statistical parameters of regression coefficient of 0.996-0.998, root-mean square error of prediction of 0.005-0.010 and relative error of prediction of 1.54-3.82% for the calibration set show the reliability of the developed method. In addition, the developed method is externally validated with three samples in validation set (with a relative error of prediction below 10.0%). Finally, to test the applicability of the proposed strategy for the analysis of real samples, five real gasoline samples collected from gas stations are used for this purpose and the gasoline proportions were in range of 70-85%. Also, the relative standard deviations were below 8.5% for different samples in the prediction set. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
-
Maboudi Afkham, Heydar; Qiu, Xuanbin; The, Matthew; Käll, Lukas
2017-02-15
Liquid chromatography is frequently used as a means to reduce the complexity of peptide-mixtures in shotgun proteomics. For such systems, the time when a peptide is released from a chromatography column and registered in the mass spectrometer is referred to as the peptide's retention time . Using heuristics or machine learning techniques, previous studies have demonstrated that it is possible to predict the retention time of a peptide from its amino acid sequence. In this paper, we are applying Gaussian Process Regression to the feature representation of a previously described predictor E lude . Using this framework, we demonstrate that it is possible to estimate the uncertainty of the prediction made by the model. Here we show how this uncertainty relates to the actual error of the prediction. In our experiments, we observe a strong correlation between the estimated uncertainty provided by Gaussian Process Regression and the actual prediction error. This relation provides us with new means for assessment of the predictions. We demonstrate how a subset of the peptides can be selected with lower prediction error compared to the whole set. We also demonstrate how such predicted standard deviations can be used for designing adaptive windowing strategies. lukas.kall@scilifelab.se. Our software and the data used in our experiments is publicly available and can be downloaded from https://github.com/statisticalbiotechnology/GPTime . © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
-
Empirical Observations on the Sensitivity of Hot Cathode Ionization Type Vacuum Gages
NASA Technical Reports Server (NTRS)
Summers, R. L.
1969-01-01
A study of empirical methods of predicting tile relative sensitivities of hot cathode ionization gages is presented. Using previously published gage sensitivities, several rules for predicting relative sensitivity are tested. The relative sensitivity to different gases is shown to be invariant with gage type, in the linear range of gage operation. The total ionization cross section, molecular and molar polarizability, and refractive index are demonstrated to be useful parameters for predicting relative gage sensitivity. Using data from the literature, the probable error of predictions of relative gage sensitivity based on these molecular properties is found to be about 10 percent. A comprehensive table of predicted relative sensitivities, based on empirical methods, is presented.
-
Predictive error detection in pianists: a combined ERP and motion capture study
Maidhof, Clemens; Pitkäniemi, Anni; Tervaniemi, Mari
2013-01-01
Performing a piece of music involves the interplay of several cognitive and motor processes and requires extensive training to achieve a high skill level. However, even professional musicians commit errors occasionally. Previous event-related potential (ERP) studies have investigated the neurophysiological correlates of pitch errors during piano performance, and reported pre-error negativity already occurring approximately 70–100 ms before the error had been committed and audible. It was assumed that this pre-error negativity reflects predictive control processes that compare predicted consequences with actual consequences of one's own actions. However, in previous investigations, correct and incorrect pitch events were confounded by their different tempi. In addition, no data about the underlying movements were available. In the present study, we exploratively recorded the ERPs and 3D movement data of pianists' fingers simultaneously while they performed fingering exercises from memory. Results showed a pre-error negativity for incorrect keystrokes when both correct and incorrect keystrokes were performed with comparable tempi. Interestingly, even correct notes immediately preceding erroneous keystrokes elicited a very similar negativity. In addition, we explored the possibility of computing ERPs time-locked to a kinematic landmark in the finger motion trajectories defined by when a finger makes initial contact with the key surface, that is, at the onset of tactile feedback. Results suggest that incorrect notes elicited a small difference after the onset of tactile feedback, whereas correct notes preceding incorrect ones elicited negativity before the onset of tactile feedback. The results tentatively suggest that tactile feedback plays an important role in error-monitoring during piano performance, because the comparison between predicted and actual sensory (tactile) feedback may provide the information necessary for the detection of an upcoming error. PMID:24133428
-
ERIC Educational Resources Information Center
Roessger, Kevin M.
2014-01-01
In work-related instrumental learning contexts, the role of reflective activities is unclear. Kolb's experiential learning theory and Mezirow's transformative learning theory predict skill adaptation as an outcome. This prediction was tested by manipulating reflective activities and assessing participants' response and error rates during novel…
-
Sensitivity to prediction error in reach adaptation
Haith, Adrian M.; Harran, Michelle D.; Shadmehr, Reza
2012-01-01
It has been proposed that the brain predicts the sensory consequences of a movement and compares it to the actual sensory feedback. When the two differ, an error signal is formed, driving adaptation. How does an error in one trial alter performance in the subsequent trial? Here we show that the sensitivity to error is not constant but declines as a function of error magnitude. That is, one learns relatively less from large errors compared with small errors. We performed an experiment in which humans made reaching movements and randomly experienced an error in both their visual and proprioceptive feedback. Proprioceptive errors were created with force fields, and visual errors were formed by perturbing the cursor trajectory to create a visual error that was smaller, the same size, or larger than the proprioceptive error. We measured single-trial adaptation and calculated sensitivity to error, i.e., the ratio of the trial-to-trial change in motor commands to error size. We found that for both sensory modalities sensitivity decreased with increasing error size. A reanalysis of a number of previously published psychophysical results also exhibited this feature. Finally, we asked how the brain might encode sensitivity to error. We reanalyzed previously published probabilities of cerebellar complex spikes (CSs) and found that this probability declined with increasing error size. From this we posit that a CS may be representative of the sensitivity to error, and not error itself, a hypothesis that may explain conflicting reports about CSs and their relationship to error. PMID:22773782
-
Brooker, Rebecca J.; Buss, Kristin A.
2014-01-01
Temperamentally fearful children are at increased risk for the development of anxiety problems relative to less-fearful children. This risk is even greater when early environments include high levels of harsh parenting behaviors. However, the mechanisms by which harsh parenting may impact fearful children’s risk for anxiety problems are largely unknown. Recent neuroscience work has suggested that punishment is associated with exaggerated error-related negativity (ERN), an event-related potential linked to performance monitoring, even after the threat of punishment is removed. In the current study, we examined the possibility that harsh parenting interacts with fearfulness, impacting anxiety risk via neural processes of performance monitoring. We found that greater fearfulness and harsher parenting at 2 years of age predicted greater fearfulness and greater ERN amplitudes at age 4. Supporting the role of cognitive processes in this association, greater fearfulness and harsher parenting also predicted less efficient neural processing during preschool. This study provides initial evidence that performance monitoring may be a candidate process by which early parenting interacts with fearfulness to predict risk for anxiety problems. PMID:24721466
-
NASA Astrophysics Data System (ADS)
Sarkar, Arnab; Karki, Vijay; Aggarwal, Suresh K.; Maurya, Gulab S.; Kumar, Rohit; Rai, Awadhesh K.; Mao, Xianglei; Russo, Richard E.
2015-06-01
Laser induced breakdown spectroscopy (LIBS) was applied for elemental characterization of high alloy steel using partial least squares regression (PLSR) with an objective to evaluate the analytical performance of this multivariate approach. The optimization of the number of principle components for minimizing error in PLSR algorithm was investigated. The effect of different pre-treatment procedures on the raw spectral data before PLSR analysis was evaluated based on several statistical (standard error of prediction, percentage relative error of prediction etc.) parameters. The pre-treatment with "NORM" parameter gave the optimum statistical results. The analytical performance of PLSR model improved by increasing the number of laser pulses accumulated per spectrum as well as by truncating the spectrum to appropriate wavelength region. It was found that the statistical benefit of truncating the spectrum can also be accomplished by increasing the number of laser pulses per accumulation without spectral truncation. The constituents (Co and Mo) present in hundreds of ppm were determined with relative precision of 4-9% (2σ), whereas the major constituents Cr and Ni (present at a few percent levels) were determined with a relative precision of ~ 2%(2σ).
-
Frontal Theta Reflects Uncertainty and Unexpectedness during Exploration and Exploitation
Figueroa, Christina M.; Cohen, Michael X; Frank, Michael J.
2012-01-01
In order to understand the exploitation/exploration trade-off in reinforcement learning, previous theoretical and empirical accounts have suggested that increased uncertainty may precede the decision to explore an alternative option. To date, the neural mechanisms that support the strategic application of uncertainty-driven exploration remain underspecified. In this study, electroencephalography (EEG) was used to assess trial-to-trial dynamics relevant to exploration and exploitation. Theta-band activities over middle and lateral frontal areas have previously been implicated in EEG studies of reinforcement learning and strategic control. It was hypothesized that these areas may interact during top-down strategic behavioral control involved in exploratory choices. Here, we used a dynamic reward–learning task and an associated mathematical model that predicted individual response times. This reinforcement-learning model generated value-based prediction errors and trial-by-trial estimates of exploration as a function of uncertainty. Mid-frontal theta power correlated with unsigned prediction error, although negative prediction errors had greater power overall. Trial-to-trial variations in response-locked frontal theta were linearly related to relative uncertainty and were larger in individuals who used uncertainty to guide exploration. This finding suggests that theta-band activities reflect prefrontal-directed strategic control during exploratory choices. PMID:22120491
-
Erdeniz, Burak; Rohe, Tim; Done, John; Seidler, Rachael D
2013-01-01
Conventional neuroimaging techniques provide information about condition-related changes of the BOLD (blood-oxygen-level dependent) signal, indicating only where and when the underlying cognitive processes occur. Recently, with the help of a new approach called "model-based" functional neuroimaging (fMRI), researchers are able to visualize changes in the internal variables of a time varying learning process, such as the reward prediction error or the predicted reward value of a conditional stimulus. However, despite being extremely beneficial to the imaging community in understanding the neural correlates of decision variables, a model-based approach to brain imaging data is also methodologically challenging due to the multicollinearity problem in statistical analysis. There are multiple sources of multicollinearity in functional neuroimaging including investigations of closely related variables and/or experimental designs that do not account for this. The source of multicollinearity discussed in this paper occurs due to correlation between different subjective variables that are calculated very close in time. Here, we review methodological approaches to analyzing such data by discussing the special case of separating the reward prediction error signal from reward outcomes.
-
NASA Astrophysics Data System (ADS)
Bukhari, W.; Hong, S.-M.
2015-01-01
Motion-adaptive radiotherapy aims to deliver a conformal dose to the target tumour with minimal normal tissue exposure by compensating for tumour motion in real time. The prediction as well as the gating of respiratory motion have received much attention over the last two decades for reducing the targeting error of the treatment beam due to respiratory motion. In this article, we present a real-time algorithm for predicting and gating respiratory motion that utilizes a model-based and a model-free Bayesian framework by combining them in a cascade structure. The algorithm, named EKF-GPR+, implements a gating function without pre-specifying a particular region of the patient’s breathing cycle. The algorithm first employs an extended Kalman filter (LCM-EKF) to predict the respiratory motion and then uses a model-free Gaussian process regression (GPR) to correct the error of the LCM-EKF prediction. The GPR is a non-parametric Bayesian algorithm that yields predictive variance under Gaussian assumptions. The EKF-GPR+ algorithm utilizes the predictive variance from the GPR component to capture the uncertainty in the LCM-EKF prediction error and systematically identify breathing points with a higher probability of large prediction error in advance. This identification allows us to pause the treatment beam over such instances. EKF-GPR+ implements the gating function by using simple calculations based on the predictive variance with no additional detection mechanism. A sparse approximation of the GPR algorithm is employed to realize EKF-GPR+ in real time. Extensive numerical experiments are performed based on a large database of 304 respiratory motion traces to evaluate EKF-GPR+. The experimental results show that the EKF-GPR+ algorithm effectively reduces the prediction error in a root-mean-square (RMS) sense by employing the gating function, albeit at the cost of a reduced duty cycle. As an example, EKF-GPR+ reduces the patient-wise RMS error to 37%, 39% and 42% in percent ratios relative to no prediction for a duty cycle of 80% at lookahead lengths of 192 ms, 384 ms and 576 ms, respectively. The experiments also confirm that EKF-GPR+ controls the duty cycle with reasonable accuracy.
-
Zhang, Ji-Li; Liu, Bo-Fei; Chu, Teng-Fei; Di, Xue-Ying; Jin, Sen
2012-06-01
A laboratory burning experiment was conducted to measure the fire spread speed, residual time, reaction intensity, fireline intensity, and flame length of the ground surface fuels collected from a Korean pine (Pinus koraiensis) and Mongolian oak (Quercus mongolica) mixed stand in Maoer Mountains of Northeast China under the conditions of no wind, zero slope, and different moisture content, load, and mixture ratio of the fuels. The results measured were compared with those predicted by the extended Rothermel model to test the performance of the model, especially for the effects of two different weighting methods on the fire behavior modeling of the mixed fuels. With the prediction of the model, the mean absolute errors of the fire spread speed and reaction intensity of the fuels were 0.04 m X min(-1) and 77 kW X m(-2), their mean relative errors were 16% and 22%, while the mean absolute errors of residual time, fireline intensity and flame length were 15.5 s, 17.3 kW X m(-1), and 9.7 cm, and their mean relative errors were 55.5%, 48.7%, and 24%, respectively, indicating that the predicted values of residual time, fireline intensity, and flame length were lower than the observed ones. These errors could be regarded as the lower limits for the application of the extended Rothermel model in predicting the fire behavior of similar fuel types, and provide valuable information for using the model to predict the fire behavior under the similar field conditions. As a whole, the two different weighting methods did not show significant difference in predicting the fire behavior of the mixed fuels by extended Rothermel model. When the proportion of Korean pine fuels was lower, the predicted values of spread speed and reaction intensity obtained by surface area weighting method and those of fireline intensity and flame length obtained by load weighting method were higher; when the proportion of Korean pine needles was higher, the contrary results were obtained.
-
Meyer, Alexandria; Proudfit, Greg Hajcak; Bufferd, Sara J.; Kujawa, Autumn J.; Laptook, Rebecca S.; Torpey, Dana C.; Klein, Daniel N.
2017-01-01
The error-related negativity (ERN) is a negative deflection in the event-related potential (ERP) occurring approximately 50 ms after error commission at fronto-central electrode sites and is thought to reflect the activation of a generic error monitoring system. Several studies have reported an increased ERN in clinically anxious children, and suggest that anxious children are more sensitive to error commission—although the mechanisms underlying this association are not clear. We have previously found that punishing errors results in a larger ERN, an effect that persists after punishment ends. It is possible that learning-related experiences that impact sensitivity to errors may lead to an increased ERN. In particular, punitive parenting might sensitize children to errors and increase their ERN. We tested this possibility in the current study by prospectively examining the relationship between parenting style during early childhood and children’s ERN approximately three years later. Initially, 295 parents and children (approximately 3 years old) participated in a structured observational measure of parenting behavior, and parents completed a self-report measure of parenting style. At a follow-up assessment approximately three years later, the ERN was elicited during a Go/No-Go task, and diagnostic interviews were completed with parents to assess child psychopathology. Results suggested that both observational measures of hostile parenting and self-report measures of authoritarian parenting style uniquely predicted a larger ERN in children 3 years later. We previously reported that children in this sample with anxiety disorders were characterized by an increased ERN. A mediation analysis indicated that ERN magnitude mediated the relationship between harsh parenting and child anxiety disorder. Results suggest that parenting may shape children’s error processing through environmental conditioning and thereby risk for anxiety, although future work is needed to confirm this hypothesis. PMID:25092483
-
DOE Office of Scientific and Technical Information (OSTI.GOV)
Voisin, Sophie; Pinto, Frank M; Morin-Ducote, Garnetta
2013-01-01
Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels. Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from 4 Radiology residents and 2 breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADsmore » images features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated. Results: Diagnostic error can be predicted reliably by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model (AUC=0.79). Personalized user modeling was far more accurate for the more experienced readers (average AUC of 0.837 0.029) than for the less experienced ones (average AUC of 0.667 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features. Conclusions: Diagnostic errors in mammography can be predicted reliably by leveraging the radiologists gaze behavior and image content.« less
-
DOE Office of Scientific and Technical Information (OSTI.GOV)
Velec, Michael, E-mail: michael.velec@rmp.uhn.on.ca; Institute of Medical Science, University of Toronto, Toronto, ON; Moseley, Joanne L.
2012-07-15
Purpose: To investigate the accumulated dose deviations to tumors and normal tissues in liver stereotactic body radiotherapy (SBRT) and investigate their geometric causes. Methods and Materials: Thirty previously treated liver cancer patients were retrospectively evaluated. Stereotactic body radiotherapy was planned on the static exhale CT for 27-60 Gy in 6 fractions, and patients were treated in free-breathing with daily cone-beam CT guidance. Biomechanical model-based deformable image registration accumulated dose over both the planning four-dimensional (4D) CT (predicted breathing dose) and also over each fraction's respiratory-correlated cone-beam CT (accumulated treatment dose). The contribution of different geometric errors to changes between themore » accumulated and predicted breathing dose were quantified. Results: Twenty-one patients (70%) had accumulated dose deviations relative to the planned static prescription dose >5%, ranging from -15% to 5% in tumors and -42% to 8% in normal tissues. Sixteen patients (53%) still had deviations relative to the 4D CT-predicted dose, which were similar in magnitude. Thirty-two tissues in these 16 patients had deviations >5% relative to the 4D CT-predicted dose, and residual setup errors (n = 17) were most often the largest cause of the deviations, followed by deformations (n = 8) and breathing variations (n = 7). Conclusion: The majority of patients had accumulated dose deviations >5% relative to the static plan. Significant deviations relative to the predicted breathing dose still occurred in more than half the patients, commonly owing to residual setup errors. Accumulated SBRT dose may be warranted to pursue further dose escalation, adaptive SBRT, and aid in correlation with clinical outcomes.« less
-
Neural substrates of updating the prediction through prediction error during decision making.
Wang, Ying; Ma, Ning; He, Xiaosong; Li, Nan; Wei, Zhengde; Yang, Lizhuang; Zha, Rujing; Han, Long; Li, Xiaoming; Zhang, Daren; Liu, Ying; Zhang, Xiaochu
2017-08-15
Learning of prediction error (PE), including reward PE and risk PE, is crucial for updating the prediction in reinforcement learning (RL). Neurobiological and computational models of RL have reported extensive brain activations related to PE. However, the occurrence of PE does not necessarily predict updating the prediction, e.g., in a probability-known event. Therefore, the brain regions specifically engaged in updating the prediction remain unknown. Here, we conducted two functional magnetic resonance imaging (fMRI) experiments, the probability-unknown Iowa Gambling Task (IGT) and the probability-known risk decision task (RDT). Behavioral analyses confirmed that PEs occurred in both tasks but were only used for updating the prediction in the IGT. By comparing PE-related brain activations between the two tasks, we found that the rostral anterior cingulate cortex/ventral medial prefrontal cortex (rACC/vmPFC) and the posterior cingulate cortex (PCC) activated only during the IGT and were related to both reward and risk PE. Moreover, the responses in the rACC/vmPFC and the PCC were modulated by uncertainty and were associated with reward prediction-related brain regions. Electric brain stimulation over these regions lowered the performance in the IGT but not in the RDT. Our findings of a distributed neural circuit of PE processing suggest that the rACC/vmPFC and the PCC play a key role in updating the prediction through PE processing during decision making. Copyright © 2017 Elsevier Inc. All rights reserved.
-
Spatial Resolution, Grayscale, and Error Diffusion Trade-offs: Impact on Display System Design
NASA Technical Reports Server (NTRS)
Gille, Jennifer L. (Principal Investigator)
1996-01-01
We examine technology trade-offs related to grayscale resolution, spatial resolution, and error diffusion for tessellated display systems. We present new empirical results from our psychophysical study of these trade-offs and compare them to the predictions of a model of human vision.
-
The cerebellum for jocks and nerds alike.
Popa, Laurentiu S; Hewitt, Angela L; Ebner, Timothy J
2014-01-01
Historically the cerebellum has been implicated in the control of movement. However, the cerebellum's role in non-motor functions, including cognitive and emotional processes, has also received increasing attention. Starting from the premise that the uniform architecture of the cerebellum underlies a common mode of information processing, this review examines recent electrophysiological findings on the motor signals encoded in the cerebellar cortex and then relates these signals to observations in the non-motor domain. Simple spike firing of individual Purkinje cells encodes performance errors, both predicting upcoming errors as well as providing feedback about those errors. Further, this dual temporal encoding of prediction and feedback involves a change in the sign of the simple spike modulation. Therefore, Purkinje cell simple spike firing both predicts and responds to feedback about a specific parameter, consistent with computing sensory prediction errors in which the predictions about the consequences of a motor command are compared with the feedback resulting from the motor command execution. These new findings are in contrast with the historical view that complex spikes encode errors. Evaluation of the kinematic coding in the simple spike discharge shows the same dual temporal encoding, suggesting this is a common mode of signal processing in the cerebellar cortex. Decoding analyses show the considerable accuracy of the predictions provided by Purkinje cells across a range of times. Further, individual Purkinje cells encode linearly and independently a multitude of signals, both kinematic and performance errors. Therefore, the cerebellar cortex's capacity to make associations across different sensory, motor and non-motor signals is large. The results from studying how Purkinje cells encode movement signals suggest that the cerebellar cortex circuitry can support associative learning, sequencing, working memory, and forward internal models in non-motor domains.
-
The cerebellum for jocks and nerds alike
Popa, Laurentiu S.; Hewitt, Angela L.; Ebner, Timothy J.
2014-01-01
Historically the cerebellum has been implicated in the control of movement. However, the cerebellum's role in non-motor functions, including cognitive and emotional processes, has also received increasing attention. Starting from the premise that the uniform architecture of the cerebellum underlies a common mode of information processing, this review examines recent electrophysiological findings on the motor signals encoded in the cerebellar cortex and then relates these signals to observations in the non-motor domain. Simple spike firing of individual Purkinje cells encodes performance errors, both predicting upcoming errors as well as providing feedback about those errors. Further, this dual temporal encoding of prediction and feedback involves a change in the sign of the simple spike modulation. Therefore, Purkinje cell simple spike firing both predicts and responds to feedback about a specific parameter, consistent with computing sensory prediction errors in which the predictions about the consequences of a motor command are compared with the feedback resulting from the motor command execution. These new findings are in contrast with the historical view that complex spikes encode errors. Evaluation of the kinematic coding in the simple spike discharge shows the same dual temporal encoding, suggesting this is a common mode of signal processing in the cerebellar cortex. Decoding analyses show the considerable accuracy of the predictions provided by Purkinje cells across a range of times. Further, individual Purkinje cells encode linearly and independently a multitude of signals, both kinematic and performance errors. Therefore, the cerebellar cortex's capacity to make associations across different sensory, motor and non-motor signals is large. The results from studying how Purkinje cells encode movement signals suggest that the cerebellar cortex circuitry can support associative learning, sequencing, working memory, and forward internal models in non-motor domains. PMID:24987338
-
A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM.
Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei; Song, Houbing
2018-01-15
Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model's performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM's parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models' performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors.
-
NASA Astrophysics Data System (ADS)
Li, Xiongwei; Wang, Zhe; Lui, Siu-Lung; Fu, Yangting; Li, Zheng; Liu, Jianming; Ni, Weidou
2013-10-01
A bottleneck of the wide commercial application of laser-induced breakdown spectroscopy (LIBS) technology is its relatively high measurement uncertainty. A partial least squares (PLS) based normalization method was proposed to improve pulse-to-pulse measurement precision for LIBS based on our previous spectrum standardization method. The proposed model utilized multi-line spectral information of the measured element and characterized the signal fluctuations due to the variation of plasma characteristic parameters (plasma temperature, electron number density, and total number density) for signal uncertainty reduction. The model was validated by the application of copper concentration prediction in 29 brass alloy samples. The results demonstrated an improvement on both measurement precision and accuracy over the generally applied normalization as well as our previously proposed simplified spectrum standardization method. The average relative standard deviation (RSD), average of the standard error (error bar), the coefficient of determination (R2), the root-mean-square error of prediction (RMSEP), and average value of the maximum relative error (MRE) were 1.80%, 0.23%, 0.992, 1.30%, and 5.23%, respectively, while those for the generally applied spectral area normalization were 3.72%, 0.71%, 0.973, 1.98%, and 14.92%, respectively.
-
Nelson, Jonathan M.; Shimizu, Yasuyuki; Giri, Sanjay; McDonald, Richard R.
2010-01-01
Uncertainties in flood stage prediction and bed evolution in rivers are frequently associated with the evolution of bedforms over a hydrograph. For the case of flood prediction, the evolution of the bedforms may alter the effective bed roughness, so predictions of stage and velocity based on assuming bedforms retain the same size and shape over a hydrograph will be incorrect. These same effects will produce errors in the prediction of the sediment transport and bed evolution, but in this latter case the errors are typically larger, as even small errors in the prediction of bedform form drag can make very large errors in predicting the rates of sediment motion and the associated erosion and deposition. In situations where flows change slowly, it may be possible to use empirical results that relate bedform morphology to roughness and effective form drag to avoid these errors; but in many cases where the bedforms evolve rapidly and are in disequilibrium with the instantaneous flow, these empirical methods cannot be accurately applied. Over the past few years, computational models for bedform development, migration, and adjustment to varying flows have been developed and tested with a variety of laboratory and field data. These models, which are based on detailed multidimensional flow modeling incorporating large eddy simulation, appear to be capable of predicting bedform dimensions during steady flows as well as their time dependence during discharge variations. In the work presented here, models of this type are used to investigate the impacts of bedform on stage and bed evolution in rivers during flood hydrographs. The method is shown to reproduce hysteresis in rating curves as well as other more subtle effects in the shape of flood waves. Techniques for combining the bedform evolution models with larger-scale models for river reach flow, sediment transport, and bed evolution are described and used to show the importance of including dynamic bedform effects in river modeling. For example calculations for a flood on the Kootenai River, errors of almost 1m in predicted stage and errors of about a factor of two in the predicted maximum depths of erosion can be attributed to bedform evolution. Thus, treating bedforms explicitly in flood and bed evolution models can decrease uncertainty and increase the accuracy of predictions.
-
Harmsen, Wouter J; Ribbers, Gerard M; Slaman, Jorrit; Heijenbrok-Kal, Majanka H; Khajeh, Ladbon; van Kooten, Fop; Neggers, Sebastiaan J C M M; van den Berg-Emons, Rita J
2017-05-01
Peak oxygen uptake (VO 2peak ) established during progressive cardiopulmonary exercise testing (CPET) is the "gold-standard" for cardiorespiratory fitness. However, CPET measurements may be limited in patients with aneurysmal subarachnoid hemorrhage (a-SAH) by disease-related complaints, such as cardiovascular health-risks or anxiety. Furthermore, CPET with gas-exchange analyses require specialized knowledge and infrastructure with limited availability in most rehabilitation facilities. To determine whether an easy-to-administer six-minute walk test (6MWT) is a valid clinical alternative to progressive CPET in order to predict VO 2peak in individuals with a-SAH. Twenty-seven patients performed the 6MWT and CPET with gas-exchange analyses on a cycle ergometer. Univariate and multivariate regression models were made to investigate the predictability of VO 2peak from the six-minute walk distance (6MWD). Univariate regression showed that the 6MWD was strongly related to VO 2peak (r = 0.75, p < 0.001), with an explained variance of 56% and a prediction error of 4.12 ml/kg/min, representing 18% of mean VO 2peak . Adding age and sex to an extended multivariate regression model improved this relationship (r = 0.82, p < 0.001), with an explained variance of 67% and a prediction error of 3.67 ml/kg/min corresponding to 16% of mean VO 2peak . The 6MWT is an easy-to-administer submaximal exercise test that can be selected to estimate cardiorespiratory fitness at an aggregated level, in groups of patients with a-SAH, which may help to evaluate interventions in a clinical or research setting. However, the relatively large prediction error does not allow for an accurate prediction in individual patients.
-
Efficient Reduction and Analysis of Model Predictive Error
NASA Astrophysics Data System (ADS)
Doherty, J.
2006-12-01
Most groundwater models are calibrated against historical measurements of head and other system states before being used to make predictions in a real-world context. Through the calibration process, parameter values are estimated or refined such that the model is able to reproduce historical behaviour of the system at pertinent observation points reasonably well. Predictions made by the model are deemed to have greater integrity because of this. Unfortunately, predictive integrity is not as easy to achieve as many groundwater practitioners would like to think. The level of parameterisation detail estimable through the calibration process (especially where estimation takes place on the basis of heads alone) is strictly limited, even where full use is made of modern mathematical regularisation techniques such as those encapsulated in the PEST calibration package. (Use of these mechanisms allows more information to be extracted from a calibration dataset than is possible using simpler regularisation devices such as zones of piecewise constancy.) Where a prediction depends on aspects of parameterisation detail that are simply not inferable through the calibration process (which is often the case for predictions related to contaminant movement, and/or many aspects of groundwater/surface water interaction), then that prediction may be just as much in error as it would have been if the model had not been calibrated at all. Model predictive error arises from two sources. These are (a) the presence of measurement noise within the calibration dataset through which linear combinations of parameters spanning the "calibration solution space" are inferred, and (b) the sensitivity of the prediction to members of the "calibration null space" spanned by linear combinations of parameters which are not inferable through the calibration process. The magnitude of the former contribution depends on the level of measurement noise. The magnitude of the latter contribution (which often dominates the former) depends on the "innate variability" of hydraulic properties within the model domain. Knowledge of both of these is a prerequisite for characterisation of the magnitude of possible model predictive error. Unfortunately, in most cases, such knowledge is incomplete and subjective. Nevertheless, useful analysis of model predictive error can still take place. The present paper briefly discusses the means by which mathematical regularisation can be employed in the model calibration process in order to extract as much information as possible on hydraulic property heterogeneity prevailing within the model domain, thereby reducing predictive error to the lowest that can be achieved on the basis of that dataset. It then demonstrates the means by which predictive error variance can be quantified based on information supplied by the regularised inversion process. Both linear and nonlinear predictive error variance analysis is demonstrated using a number of real-world and synthetic examples.
-
Trajectory Control of Rendezvous with Maneuver Target Spacecraft
NASA Technical Reports Server (NTRS)
Zhou, Zhinqiang
2012-01-01
In this paper, a nonlinear trajectory control algorithm of rendezvous with maneuvering target spacecraft is presented. The disturbance forces on the chaser and target spacecraft and the thrust forces on the chaser spacecraft are considered in the analysis. The control algorithm developed in this paper uses the relative distance and relative velocity between the target and chaser spacecraft as the inputs. A general formula of reference relative trajectory of the chaser spacecraft to the target spacecraft is developed and applied to four different proximity maneuvers, which are in-track circling, cross-track circling, in-track spiral rendezvous and cross-track spiral rendezvous. The closed-loop differential equations of the proximity relative motion with the control algorithm are derived. It is proven in the paper that the tracking errors between the commanded relative trajectory and the actual relative trajectory are bounded within a constant region determined by the control gains. The prediction of the tracking errors is obtained. Design examples are provided to show the implementation of the control algorithm. The simulation results show that the actual relative trajectory tracks the commanded relative trajectory tightly. The predicted tracking errors match those calculated in the simulation results. The control algorithm developed in this paper can also be applied to interception of maneuver target spacecraft and relative trajectory control of spacecraft formation flying.
-
Analysis of a Shock-Associated Noise Prediction Model Using Measured Jet Far-Field Noise Data
NASA Technical Reports Server (NTRS)
Dahl, Milo D.; Sharpe, Jacob A.
2014-01-01
A code for predicting supersonic jet broadband shock-associated noise was assessed using a database containing noise measurements of a jet issuing from a convergent nozzle. The jet was operated at 24 conditions covering six fully expanded Mach numbers with four total temperature ratios. To enable comparisons of the predicted shock-associated noise component spectra with data, the measured total jet noise spectra were separated into mixing noise and shock-associated noise component spectra. Comparisons between predicted and measured shock-associated noise component spectra were used to identify deficiencies in the prediction model. Proposed revisions to the model, based on a study of the overall sound pressure levels for the shock-associated noise component of the measured data, a sensitivity analysis of the model parameters with emphasis on the definition of the convection velocity parameter, and a least-squares fit of the predicted to the measured shock-associated noise component spectra, resulted in a new definition for the source strength spectrum in the model. An error analysis showed that the average error in the predicted spectra was reduced by as much as 3.5 dB for the revised model relative to the average error for the original model.
-
High accuracy satellite drag model (HASDM)
NASA Astrophysics Data System (ADS)
Storz, M.; Bowman, B.; Branson, J.
The dominant error source in the force models used to predict low perigee satellite trajectories is atmospheric drag. Errors in operational thermospheric density models cause significant errors in predicted satellite positions, since these models do not account for dynamic changes in atmospheric drag for orbit predictions. The Air Force Space Battlelab's High Accuracy Satellite Drag Model (HASDM) estimates and predicts (out three days) a dynamically varying high-resolution density field. HASDM includes the Dynamic Calibration Atmosphere (DCA) algorithm that solves for the phases and amplitudes of the diurnal, semidiurnal and terdiurnal variations of thermospheric density near real-time from the observed drag effects on a set of Low Earth Orbit (LEO) calibration satellites. The density correction is expressed as a function of latitude, local solar time and altitude. In HASDM, a time series prediction filter relates the extreme ultraviolet (EUV) energy index E10.7 and the geomagnetic storm index a p to the DCA density correction parameters. The E10.7 index is generated by the SOLAR2000 model, the first full spectrum model of solar irradiance. The estimated and predicted density fields will be used operationally to significantly improve the accuracy of predicted trajectories for all low perigee satellites.
-
High accuracy satellite drag model (HASDM)
NASA Astrophysics Data System (ADS)
Storz, Mark F.; Bowman, Bruce R.; Branson, Major James I.; Casali, Stephen J.; Tobiska, W. Kent
The dominant error source in force models used to predict low-perigee satellite trajectories is atmospheric drag. Errors in operational thermospheric density models cause significant errors in predicted satellite positions, since these models do not account for dynamic changes in atmospheric drag for orbit predictions. The Air Force Space Battlelab's High Accuracy Satellite Drag Model (HASDM) estimates and predicts (out three days) a dynamically varying global density field. HASDM includes the Dynamic Calibration Atmosphere (DCA) algorithm that solves for the phases and amplitudes of the diurnal and semidiurnal variations of thermospheric density near real-time from the observed drag effects on a set of Low Earth Orbit (LEO) calibration satellites. The density correction is expressed as a function of latitude, local solar time and altitude. In HASDM, a time series prediction filter relates the extreme ultraviolet (EUV) energy index E10.7 and the geomagnetic storm index ap, to the DCA density correction parameters. The E10.7 index is generated by the SOLAR2000 model, the first full spectrum model of solar irradiance. The estimated and predicted density fields will be used operationally to significantly improve the accuracy of predicted trajectories for all low-perigee satellites.
-
Purpora, Christina; Blegen, Mary A; Stotts, Nancy A
2015-01-01
To test hypotheses from a horizontal violence and quality and safety of patient care model: horizontal violence (negative behavior among peers) is inversely related to peer relations, quality of care and it is positively related to errors and adverse events. Additionally, the association between horizontal violence, peer relations, quality of care, errors and adverse events, and nurse and work characteristics were determined. A random sample (n= 175) of hospital staff Registered Nurses working in California. Nurses participated via survey. Bivariate and multivariate analyses tested the study hypotheses. Hypotheses were supported. Horizontal violence was inversely related to peer relations and quality of care, and positively related to errors and adverse events. Including peer relations in the analyses altered the relationship between horizontal violence and quality of care but not between horizontal violence, errors and adverse events. Nurse and hospital characteristics were not related to other variables. Clinical area contributed significantly in predicting the quality of care, errors and adverse events but not peer relationships. Horizontal violence affects peer relationships and the quality and safety of patient care as perceived by participating nurses. Supportive peer relationships are important to mitigate the impact of horizontal violence on quality of care.
-
Analytical performance evaluation of SAR ATR with inaccurate or estimated models
NASA Astrophysics Data System (ADS)
DeVore, Michael D.
2004-09-01
Hypothesis testing algorithms for automatic target recognition (ATR) are often formulated in terms of some assumed distribution family. The parameter values corresponding to a particular target class together with the distribution family constitute a model for the target's signature. In practice such models exhibit inaccuracy because of incorrect assumptions about the distribution family and/or because of errors in the assumed parameter values, which are often determined experimentally. Model inaccuracy can have a significant impact on performance predictions for target recognition systems. Such inaccuracy often causes model-based predictions that ignore the difference between assumed and actual distributions to be overly optimistic. This paper reports on research to quantify the effect of inaccurate models on performance prediction and to estimate the effect using only trained parameters. We demonstrate that for large observation vectors the class-conditional probabilities of error can be expressed as a simple function of the difference between two relative entropies. These relative entropies quantify the discrepancies between the actual and assumed distributions and can be used to express the difference between actual and predicted error rates. Focusing on the problem of ATR from synthetic aperture radar (SAR) imagery, we present estimators of the probabilities of error in both ideal and plug-in tests expressed in terms of the trained model parameters. These estimators are defined in terms of unbiased estimates for the first two moments of the sample statistic. We present an analytical treatment of these results and include demonstrations from simulated radar data.
-
Stevens, Antoine; Nocita, Marco; Tóth, Gergely; Montanarella, Luca; van Wesemael, Bas
2013-01-01
Soil organic carbon is a key soil property related to soil fertility, aggregate stability and the exchange of CO2 with the atmosphere. Existing soil maps and inventories can rarely be used to monitor the state and evolution in soil organic carbon content due to their poor spatial resolution, lack of consistency and high updating costs. Visible and Near Infrared diffuse reflectance spectroscopy is an alternative method to provide cheap and high-density soil data. However, there are still some uncertainties on its capacity to produce reliable predictions for areas characterized by large soil diversity. Using a large-scale EU soil survey of about 20,000 samples and covering 23 countries, we assessed the performance of reflectance spectroscopy for the prediction of soil organic carbon content. The best calibrations achieved a root mean square error ranging from 4 to 15 g C kg(-1) for mineral soils and a root mean square error of 50 g C kg(-1) for organic soil materials. Model errors are shown to be related to the levels of soil organic carbon and variations in other soil properties such as sand and clay content. Although errors are ∼5 times larger than the reproducibility error of the laboratory method, reflectance spectroscopy provides unbiased predictions of the soil organic carbon content. Such estimates could be used for assessing the mean soil organic carbon content of large geographical entities or countries. This study is a first step towards providing uniform continental-scale spectroscopic estimations of soil organic carbon, meeting an increasing demand for information on the state of the soil that can be used in biogeochemical models and the monitoring of soil degradation.
-
Stevens, Antoine; Nocita, Marco; Tóth, Gergely; Montanarella, Luca; van Wesemael, Bas
2013-01-01
Soil organic carbon is a key soil property related to soil fertility, aggregate stability and the exchange of CO2 with the atmosphere. Existing soil maps and inventories can rarely be used to monitor the state and evolution in soil organic carbon content due to their poor spatial resolution, lack of consistency and high updating costs. Visible and Near Infrared diffuse reflectance spectroscopy is an alternative method to provide cheap and high-density soil data. However, there are still some uncertainties on its capacity to produce reliable predictions for areas characterized by large soil diversity. Using a large-scale EU soil survey of about 20,000 samples and covering 23 countries, we assessed the performance of reflectance spectroscopy for the prediction of soil organic carbon content. The best calibrations achieved a root mean square error ranging from 4 to 15 g C kg−1 for mineral soils and a root mean square error of 50 g C kg−1 for organic soil materials. Model errors are shown to be related to the levels of soil organic carbon and variations in other soil properties such as sand and clay content. Although errors are ∼5 times larger than the reproducibility error of the laboratory method, reflectance spectroscopy provides unbiased predictions of the soil organic carbon content. Such estimates could be used for assessing the mean soil organic carbon content of large geographical entities or countries. This study is a first step towards providing uniform continental-scale spectroscopic estimations of soil organic carbon, meeting an increasing demand for information on the state of the soil that can be used in biogeochemical models and the monitoring of soil degradation. PMID:23840459
-
Evaluating concentration estimation errors in ELISA microarray experiments
DOE Office of Scientific and Technical Information (OSTI.GOV)
Daly, Don S.; White, Amanda M.; Varnum, Susan M.
Enzyme-linked immunosorbent assay (ELISA) is a standard immunoassay to predict a protein concentration in a sample. Deploying ELISA in a microarray format permits simultaneous prediction of the concentrations of numerous proteins in a small sample. These predictions, however, are uncertain due to processing error and biological variability. Evaluating prediction error is critical to interpreting biological significance and improving the ELISA microarray process. Evaluating prediction error must be automated to realize a reliable high-throughput ELISA microarray system. Methods: In this paper, we present a statistical method based on propagation of error to evaluate prediction errors in the ELISA microarray process. Althoughmore » propagation of error is central to this method, it is effective only when comparable data are available. Therefore, we briefly discuss the roles of experimental design, data screening, normalization and statistical diagnostics when evaluating ELISA microarray prediction errors. We use an ELISA microarray investigation of breast cancer biomarkers to illustrate the evaluation of prediction errors. The illustration begins with a description of the design and resulting data, followed by a brief discussion of data screening and normalization. In our illustration, we fit a standard curve to the screened and normalized data, review the modeling diagnostics, and apply propagation of error.« less
-
Predictive momentum management for a space station measurement and computation requirements
NASA Technical Reports Server (NTRS)
Adams, John Carl
1986-01-01
An analysis is made of the effects of errors and uncertainties in the predicting of disturbance torques on the peak momentum buildup on a space station. Models of the disturbance torques acting on a space station in low Earth orbit are presented, to estimate how accurately they can be predicted. An analysis of the torque and momentum buildup about the pitch axis of the Dual Keel space station configuration is formulated, and a derivation of the Average Torque Equilibrium Attitude (ATEA) is presented, for the case of no MRMS (Mobile Remote Manipulation System) motion, Y vehicle axis MRMS motion, and Z vehicle axis MRMS motion. Results showed the peak momentum buildup to be approximately 20000 N-m-s and to be relatively insensitive to errors in the predicting torque models, for Z axis motion of the MRMS was found to vary significantly with model errors, but not exceed a value of approximately 15000 N-m-s for the Y axis MRMS motion with 1 deg attitude hold error. Minimum peak disturbance momentum was found not to occur at the ATEA angle, but at a slightly smaller angle. However, this minimum peak momentum attitude was found to produce significant disturbance momentum at the end of the predicting time interval.
-
Survey and Method for Determination of Trajectory Predictor Requirements
NASA Technical Reports Server (NTRS)
Rentas, Tamika L.; Green, Steven M.; Cate, Karen Tung
2009-01-01
A survey of air-traffic-management researchers, representing a broad range of automation applications, was conducted to document trajectory-predictor requirements for future decision-support systems. Results indicated that the researchers were unable to articulate a basic set of trajectory-prediction requirements for their automation concepts. Survey responses showed the need to establish a process to help developers determine the trajectory-predictor-performance requirements for their concepts. Two methods for determining trajectory-predictor requirements are introduced. A fast-time simulation method is discussed that captures the sensitivity of a concept to the performance of its trajectory-prediction capability. A characterization method is proposed to provide quicker, yet less precise results, based on analysis and simulation to characterize the trajectory-prediction errors associated with key modeling options for a specific concept. Concept developers can then identify the relative sizes of errors associated with key modeling options, and qualitatively determine which options lead to significant errors. The characterization method is demonstrated for a case study involving future airport surface traffic management automation. Of the top four sources of error, results indicated that the error associated with accelerations to and from turn speeds was unacceptable, the error associated with the turn path model was acceptable, and the error associated with taxi-speed estimation was of concern and needed a higher fidelity concept simulation to obtain a more precise result
-
Estimation of lipids and lean mass of migrating sandpipers
Skagen, Susan K.; Knopf, Fritz L.; Cade, Brian S.
1993-01-01
Estimation of lean mass and lipid levels in birds involves the derivation of predictive equations that relate morphological measurements and, more recently, total body electrical conductivity (TOBEC) indices to known lean and lipid masses. Using cross-validation techniques, we evaluated the ability of several published and new predictive equations to estimate lean and lipid mass of Semipalmated Sandpipers (Calidris pusilla) and White-rumped Sandpipers (C. fuscicollis). We also tested ideas of Morton et al. (1991), who stated that current statistical approaches to TOBEC methodology misrepresent precision in estimating body fat. Three published interspecific equations using TOBEC indices predicted lean and lipid masses of our sample of birds with average errors of 8-28% and 53-155%, respectively. A new two-species equation relating lean mass and TOBEC indices revealed average errors of 4.6% and 23.2% in predicting lean and lipid mass, respectively. New intraspecific equations that estimate lipid mass directly from body mass, morphological measurements, and TOBEC indices yielded about a 13% error in lipid estimates. Body mass and morphological measurements explained a substantial portion of the variance (about 90%) in fat mass of both species. Addition of TOBEC indices improved the predictive model more for the smaller than for the larger sandpiper. TOBEC indices explained an additional 7.8% and 2.6% of the variance in fat mass and reduced the minimum breadth of prediction intervals by 0.95 g (32%) and 0.39 g (13%) for Semipalmated and White-rumped Sandpipers, respectively. The breadth of prediction intervals for models used to predict fat levels of individual birds must be considered when interpreting the resultant lipid estimates.
-
A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM
Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei
2018-01-01
Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model’s performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models’ performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors. PMID:29342942
-
Pailing, Patricia E; Segalowitz, Sidney J
2004-01-01
This study examines changes in the error-related negativity (ERN/Ne) related to motivational incentives and personality traits. ERPs were gathered while adults completed a four-choice letter task during four motivational conditions. Monetary incentives for finger and hand accuracy were altered across motivation conditions to either be equal or favor one type of accuracy over the other in a 3:1 ratio. Larger ERN/Ne amplitudes were predicted with increased incentives, with personality moderating this effect. Results were as expected: Individuals higher on conscientiousness displayed smaller motivation-related changes in the ERN/Ne. Similarly, those low on neuroticism had smaller effects, with the effect of Conscientiousness absent after accounting for Neuroticism. These results emphasize an emotional/evaluative function for the ERN/Ne, and suggest that the ability to selectively invest in error monitoring is moderated by underlying personality.
-
ERIC Educational Resources Information Center
Miller, A. Eve; Watson, Jason M.; Strayer, David L.
2012-01-01
Neuroscience suggests that the anterior cingulate cortex (ACC) is responsible for conflict monitoring and the detection of errors in cognitive tasks, thereby contributing to the implementation of attentional control. Though individual differences in frontally mediated goal maintenance have clearly been shown to influence outward behavior in…
-
Outliers: A Potential Data Problem.
ERIC Educational Resources Information Center
Douzenis, Cordelia; Rakow, Ernest A.
Outliers, extreme data values relative to others in a sample, may distort statistics that assume internal levels of measurement and normal distribution. The outlier may be a valid value or an error. Several procedures are available for identifying outliers, and each may be applied to errors of prediction from the regression lines for utility in a…
-
Schlagenhauf, Florian; Rapp, Michael A.; Huys, Quentin J. M.; Beck, Anne; Wüstenberg, Torsten; Deserno, Lorenz; Buchholz, Hans-Georg; Kalbitzer, Jan; Buchert, Ralph; Kienast, Thorsten; Cumming, Paul; Plotkin, Michail; Kumakura, Yoshitaka; Grace, Anthony A.; Dolan, Raymond J.; Heinz, Andreas
2013-01-01
Fluid intelligence represents the capacity for flexible problem solving and rapid behavioral adaptation. Rewards drive flexible behavioral adaptation, in part via a teaching signal expressed as reward prediction errors in the ventral striatum, which has been associated with phasic dopamine release in animal studies. We examined a sample of 28 healthy male adults using multimodal imaging and biological parametric mapping with 1) functional magnetic resonance imaging during a reversal learning task and 2) in a subsample of 17 subjects also with positron emission tomography using 6-[18F]fluoro-L-DOPA to assess dopamine synthesis capacity. Fluid intelligence was measured using a battery of nine standard neuropsychological tests. Ventral striatal BOLD correlates of reward prediction errors were positively correlated with fluid intelligence and, in the right ventral striatum, also inversely correlated with dopamine synthesis capacity (FDOPA Kinapp). When exploring aspects of fluid intelligence, we observed that prediction error signaling correlates with complex attention and reasoning. These findings indicate that individual differences in the capacity for flexible problem solving may be driven by ventral striatal activation during reward-related learning, which in turn proved to be inversely associated with ventral striatal dopamine synthesis capacity. PMID:22344813
-
Heil, Lieke; Kwisthout, Johan; van Pelt, Stan; van Rooij, Iris; Bekkering, Harold
2018-01-01
Evidence is accumulating that our brains process incoming information using top-down predictions. If lower level representations are correctly predicted by higher level representations, this enhances processing. However, if they are incorrectly predicted, additional processing is required at higher levels to "explain away" prediction errors. Here, we explored the potential nature of the models generating such predictions. More specifically, we investigated whether a predictive processing model with a hierarchical structure and causal relations between its levels is able to account for the processing of agent-caused events. In Experiment 1, participants watched animated movies of "experienced" and "novice" bowlers. The results are in line with the idea that prediction errors at a lower level of the hierarchy (i.e., the outcome of how many pins fell down) slow down reporting of information at a higher level (i.e., which agent was throwing the ball). Experiments 2 and 3 suggest that this effect is specific to situations in which the predictor is causally related to the outcome. Overall, the study supports the idea that a hierarchical predictive processing model can account for the processing of observed action outcomes and that the predictions involved are specific to cases where action outcomes can be predicted based on causal knowledge.
-
Relationships of Measurement Error and Prediction Error in Observed-Score Regression
ERIC Educational Resources Information Center
Moses, Tim
2012-01-01
The focus of this paper is assessing the impact of measurement errors on the prediction error of an observed-score regression. Measures are presented and described for decomposing the linear regression's prediction error variance into parts attributable to the true score variance and the error variances of the dependent variable and the predictor…
-
Archer, Steven M.
2007-01-01
Purpose Ordinary spherocylindrical refractive errors have been recognized as a cause of monocular diplopia for over a century, yet explanation of this phenomenon using geometrical optics has remained problematic. This study tests the hypothesis that the diffraction theory treatment of refractive errors will provide a more satisfactory explanation of monocular diplopia. Methods Diffraction theory calculations were carried out for modulation transfer functions, point spread functions, and line spread functions under conditions of defocus, astigmatism, and mixed spherocylindrical refractive errors. Defocused photographs of inked and projected black lines were made to demonstrate the predicted consequences of the theoretical calculations. Results For certain amounts of defocus, line spread functions resulting from spherical defocus are predicted to have a bimodal intensity distribution that could provide the basis for diplopia with line targets. Multimodal intensity distributions are predicted in point spread functions and provide a basis for diplopia or polyopia of point targets under conditions of astigmatism. The predicted doubling effect is evident in defocused photographs of black lines, but the effect is not as robust as the subjective experience of monocular diplopia. Conclusions Monocular diplopia due to ordinary refractive errors can be predicted from diffraction theory. Higher-order aberrations—such as spherical aberration—are not necessary but may, under some circumstances, enhance the features of monocular diplopia. The physical basis for monocular diplopia is relatively subtle, and enhancement by neural processing is probably needed to account for the robustness of the percept. PMID:18427616
-
Dopamine Modulates Adaptive Prediction Error Coding in the Human Midbrain and Striatum.
Diederen, Kelly M J; Ziauddeen, Hisham; Vestergaard, Martin D; Spencer, Tom; Schultz, Wolfram; Fletcher, Paul C
2017-02-15
Learning to optimally predict rewards requires agents to account for fluctuations in reward value. Recent work suggests that individuals can efficiently learn about variable rewards through adaptation of the learning rate, and coding of prediction errors relative to reward variability. Such adaptive coding has been linked to midbrain dopamine neurons in nonhuman primates, and evidence in support for a similar role of the dopaminergic system in humans is emerging from fMRI data. Here, we sought to investigate the effect of dopaminergic perturbations on adaptive prediction error coding in humans, using a between-subject, placebo-controlled pharmacological fMRI study with a dopaminergic agonist (bromocriptine) and antagonist (sulpiride). Participants performed a previously validated task in which they predicted the magnitude of upcoming rewards drawn from distributions with varying SDs. After each prediction, participants received a reward, yielding trial-by-trial prediction errors. Under placebo, we replicated previous observations of adaptive coding in the midbrain and ventral striatum. Treatment with sulpiride attenuated adaptive coding in both midbrain and ventral striatum, and was associated with a decrease in performance, whereas bromocriptine did not have a significant impact. Although we observed no differential effect of SD on performance between the groups, computational modeling suggested decreased behavioral adaptation in the sulpiride group. These results suggest that normal dopaminergic function is critical for adaptive prediction error coding, a key property of the brain thought to facilitate efficient learning in variable environments. Crucially, these results also offer potential insights for understanding the impact of disrupted dopamine function in mental illness. SIGNIFICANCE STATEMENT To choose optimally, we have to learn what to expect. Humans dampen learning when there is a great deal of variability in reward outcome, and two brain regions that are modulated by the brain chemical dopamine are sensitive to reward variability. Here, we aimed to directly relate dopamine to learning about variable rewards, and the neural encoding of associated teaching signals. We perturbed dopamine in healthy individuals using dopaminergic medication and asked them to predict variable rewards while we made brain scans. Dopamine perturbations impaired learning and the neural encoding of reward variability, thus establishing a direct link between dopamine and adaptation to reward variability. These results aid our understanding of clinical conditions associated with dopaminergic dysfunction, such as psychosis. Copyright © 2017 Diederen et al.
-
DOE Office of Scientific and Technical Information (OSTI.GOV)
Voisin, Sophie; Tourassi, Georgia D.; Pinto, Frank
2013-10-15
Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists’ gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels.Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from four Radiology residents and two breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADS imagesmore » features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated.Results: Machine learning can be used to predict diagnostic error by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model [area under the ROC curve (AUC) = 0.792 ± 0.030]. Personalized user modeling was far more accurate for the more experienced readers (AUC = 0.837 ± 0.029) than for the less experienced ones (AUC = 0.667 ± 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features.Conclusions: Diagnostic errors in mammography can be predicted to a good extent by leveraging the radiologists’ gaze behavior and image content.« less
-
Fossett, Tepanta R D; McNeil, Malcolm R; Pratt, Sheila R; Tompkins, Connie A; Shuster, Linda I
Although many speech errors can be generated at either a linguistic or motoric level of production, phonetically well-formed sound-level serial-order errors are generally assumed to result from disruption of phonologic encoding (PE) processes. An influential model of PE (Dell, 1986; Dell, Burger & Svec, 1997) predicts that speaking rate should affect the relative proportion of these serial-order sound errors (anticipations, perseverations, exchanges). These predictions have been extended to, and have special relevance for persons with aphasia (PWA) because of the increased frequency with which speech errors occur and because their localization within the functional linguistic architecture may help in diagnosis and treatment. Supporting evidence regarding the effect of speaking rate on phonological encoding has been provided by studies using young normal language (NL) speakers and computer simulations. Limited data exist for older NL users and no group data exist for PWA. This study tested the phonologic encoding properties of Dell's model of speech production (Dell, 1986; Dell,et al., 1997), which predicts that increasing speaking rate affects the relative proportion of serial-order sound errors (i.e., anticipations, perseverations, and exchanges). The effects of speech rate on the error ratios of anticipation/exchange (AE), anticipation/perseveration (AP) and vocal reaction time (VRT) were examined in 16 normal healthy controls (NHC) and 16 PWA without concomitant motor speech disorders. The participants were recorded performing a phonologically challenging (tongue twister) speech production task at their typical and two faster speaking rates. A significant effect of increased rate was obtained for the AP but not the AE ratio. Significant effects of group and rate were obtained for VRT. Although the significant effect of rate for the AP ratio provided evidence that changes in speaking rate did affect PE, the results failed to support the model derived predictions regarding the direction of change for error type proportions. The current findings argued for an alternative concept of the role of activation and decay in influencing types of serial-order sound errors. Rather than a slow activation decay rate (Dell, 1986), the results of the current study were more compatible with an alternative explanation of rapid activation decay or slow build-up of residual activation.
-
An error taxonomy system for analysis of haemodialysis incidents.
Gu, Xiuzhu; Itoh, Kenji; Suzuki, Satoshi
2014-12-01
This paper describes the development of a haemodialysis error taxonomy system for analysing incidents and predicting the safety status of a dialysis organisation. The error taxonomy system was developed by adapting an error taxonomy system which assumed no specific specialty to haemodialysis situations. Its application was conducted with 1,909 incident reports collected from two dialysis facilities in Japan. Over 70% of haemodialysis incidents were reported as problems or complications related to dialyser, circuit, medication and setting of dialysis condition. Approximately 70% of errors took place immediately before and after the four hours of haemodialysis therapy. Error types most frequently made in the dialysis unit were omission and qualitative errors. Failures or complications classified to staff human factors, communication, task and organisational factors were found in most dialysis incidents. Device/equipment/materials, medicine and clinical documents were most likely to be involved in errors. Haemodialysis nurses were involved in more incidents related to medicine and documents, whereas dialysis technologists made more errors with device/equipment/materials. This error taxonomy system is able to investigate incidents and adverse events occurring in the dialysis setting but is also able to estimate safety-related status of an organisation, such as reporting culture. © 2014 European Dialysis and Transplant Nurses Association/European Renal Care Association.
-
Schaefer, Alexandre; Buratto, Luciano G.; Goto, Nobuhiko; Brotherhood, Emilie V.
2016-01-01
A large body of evidence shows that buying behaviour is strongly determined by consumers’ price expectations and the extent to which real prices violate these expectations. Despite the importance of this phenomenon, little is known regarding its neural mechanisms. Here we show that two patterns of electrical brain activity known to index prediction errors–the Feedback-Related Negativity (FRN) and the feedback-related P300 –were sensitive to price offers that were cheaper than participants’ expectations. In addition, we also found that FRN amplitude time-locked to price offers predicted whether a product would be subsequently purchased or not, and further analyses suggest that this result was driven by the sensitivity of the FRN to positive price expectation violations. This finding strongly suggests that ensembles of neurons coding positive prediction errors play a critical role in real-life consumer behaviour. Further, these findings indicate that theoretical models based on the notion of prediction error, such as the Reinforcement Learning Theory, can provide a neurobiologically grounded account of consumer behavior. PMID:27658301
-
NASA Astrophysics Data System (ADS)
Adineh-Vand, A.; Torabi, M.; Roshani, G. H.; Taghipour, M.; Feghhi, S. A. H.; Rezaei, M.; Sadati, S. M.
2013-09-01
This paper presents a soft computing based artificial intelligent technique, adaptive neuro-fuzzy inference system (ANFIS) to predict the neutron production rate (NPR) of IR-IECF device in wide discharge current and voltage ranges. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the ANFIS model. The performance of the proposed ANFIS model is tested using the experimental data using four performance measures: correlation coefficient, mean absolute error, mean relative error percentage (MRE%) and root mean square error. The obtained results show that the proposed ANFIS model has achieved good agreement with the experimental results. In comparison to the experimental data the proposed ANFIS model has MRE% <1.53 and 2.85 % for training and testing data respectively. Therefore, this model can be used as an efficient tool to predict the NPR in the IR-IECF device.
-
Emotion blocks the path to learning under stereotype threat
Good, Catherine; Whiteman, Ronald C.; Maniscalco, Brian; Dweck, Carol S.
2012-01-01
Gender-based stereotypes undermine females’ performance on challenging math tests, but how do they influence their ability to learn from the errors they make? Females under stereotype threat or non-threat were presented with accuracy feedback after each problem on a GRE-like math test, followed by an optional interactive tutorial that provided step-wise problem-solving instruction. Event-related potentials tracked the initial detection of the negative feedback following errors [feedback related negativity (FRN), P3a], as well as any subsequent sustained attention/arousal to that information [late positive potential (LPP)]. Learning was defined as success in applying tutorial information to correction of initial test errors on a surprise retest 24-h later. Under non-threat conditions, emotional responses to negative feedback did not curtail exploration of the tutor, and the amount of tutor exploration predicted learning success. In the stereotype threat condition, however, greater initial salience of the failure (FRN) predicted less exploration of the tutor, and sustained attention to the negative feedback (LPP) predicted poor learning from what was explored. Thus, under stereotype threat, emotional responses to negative feedback predicted both disengagement from learning and interference with learning attempts. We discuss the importance of emotion regulation in successful rebound from failure for stigmatized groups in stereotype-salient environments. PMID:21252312
-
Emotion blocks the path to learning under stereotype threat.
Mangels, Jennifer A; Good, Catherine; Whiteman, Ronald C; Maniscalco, Brian; Dweck, Carol S
2012-02-01
Gender-based stereotypes undermine females' performance on challenging math tests, but how do they influence their ability to learn from the errors they make? Females under stereotype threat or non-threat were presented with accuracy feedback after each problem on a GRE-like math test, followed by an optional interactive tutorial that provided step-wise problem-solving instruction. Event-related potentials tracked the initial detection of the negative feedback following errors [feedback related negativity (FRN), P3a], as well as any subsequent sustained attention/arousal to that information [late positive potential (LPP)]. Learning was defined as success in applying tutorial information to correction of initial test errors on a surprise retest 24-h later. Under non-threat conditions, emotional responses to negative feedback did not curtail exploration of the tutor, and the amount of tutor exploration predicted learning success. In the stereotype threat condition, however, greater initial salience of the failure (FRN) predicted less exploration of the tutor, and sustained attention to the negative feedback (LPP) predicted poor learning from what was explored. Thus, under stereotype threat, emotional responses to negative feedback predicted both disengagement from learning and interference with learning attempts. We discuss the importance of emotion regulation in successful rebound from failure for stigmatized groups in stereotype-salient environments.
-
NASA Astrophysics Data System (ADS)
Maheshwera Reddy Paturi, Uma; Devarasetti, Harish; Abimbola Fadare, David; Reddy Narala, Suresh Kumar
2018-04-01
In the present paper, the artificial neural network (ANN) and response surface methodology (RSM) are used in modeling of surface roughness in WS2 (tungsten disulphide) solid lubricant assisted minimal quantity lubrication (MQL) machining. The real time MQL turning of Inconel 718 experimental data considered in this paper was available in the literature [1]. In ANN modeling, performance parameters such as mean square error (MSE), mean absolute percentage error (MAPE) and average error in prediction (AEP) for the experimental data were determined based on Levenberg–Marquardt (LM) feed forward back propagation training algorithm with tansig as transfer function. The MATLAB tool box has been utilized in training and testing of neural network model. Neural network model with three input neurons, one hidden layer with five neurons and one output neuron (3-5-1 architecture) is found to be most confidence and optimal. The coefficient of determination (R2) for both the ANN and RSM model were seen to be 0.998 and 0.982 respectively. The surface roughness predictions from ANN and RSM model were related with experimentally measured values and found to be in good agreement with each other. However, the prediction efficacy of ANN model is relatively high when compared with RSM model predictions.
-
Pourtois, Gilles
2017-01-01
Abstract Positive mood broadens attention and builds additional mental resources. However, its effect on performance monitoring and reward prediction errors remain unclear. To examine this issue, we used a standard mood induction procedure (based on guided imagery) and asked 45 participants to complete a gambling task suited to study reward prediction errors by means of the feedback-related negativity (FRN) and mid-frontal theta band power. Results showed a larger FRN for negative feedback as well as a lack of reward expectation modulation for positive feedback at the theta level with positive mood, relative to a neutral mood condition. A control analysis showed that this latter result could not be explained by the mere superposition of the event-related brain potential component on the theta oscillations. Moreover, these neurophysiological effects were evidenced in the absence of impairments at the behavioral level or increase in autonomic arousal with positive mood, suggesting that this mood state reliably altered brain mechanisms of reward prediction errors during performance monitoring. We interpret these new results as reflecting a genuine mood congruency effect, whereby reward is anticipated as the default outcome with positive mood and therefore processed as unsurprising (even when it is unlikely), while negative feedback is perceived as unexpected. PMID:28199707
-
Doble, Brett; Lorgelly, Paula
2016-04-01
To determine the external validity of existing mapping algorithms for predicting EQ-5D-3L utility values from EORTC QLQ-C30 responses and to establish their generalizability in different types of cancer. A main analysis (pooled) sample of 3560 observations (1727 patients) and two disease severity patient samples (496 and 93 patients) with repeated observations over time from Cancer 2015 were used to validate the existing algorithms. Errors were calculated between observed and predicted EQ-5D-3L utility values using a single pooled sample and ten pooled tumour type-specific samples. Predictive accuracy was assessed using mean absolute error (MAE) and standardized root-mean-squared error (RMSE). The association between observed and predicted EQ-5D utility values and other covariates across the distribution was tested using quantile regression. Quality-adjusted life years (QALYs) were calculated using observed and predicted values to test responsiveness. Ten 'preferred' mapping algorithms were identified. Two algorithms estimated via response mapping and ordinary least-squares regression using dummy variables performed well on number of validation criteria, including accurate prediction of the best and worst QLQ-C30 health states, predicted values within the EQ-5D tariff range, relatively small MAEs and RMSEs, and minimal differences between estimated QALYs. Comparison of predictive accuracy across ten tumour type-specific samples highlighted that algorithms are relatively insensitive to grouping by tumour type and affected more by differences in disease severity. Two of the 'preferred' mapping algorithms suggest more accurate predictions, but limitations exist. We recommend extensive scenario analyses if mapped utilities are used in cost-utility analyses.
-
Decision-making in schizophrenia: A predictive-coding perspective.
Sterzer, Philipp; Voss, Martin; Schlagenhauf, Florian; Heinz, Andreas
2018-05-31
Dysfunctional decision-making has been implicated in the positive and negative symptoms of schizophrenia. Decision-making can be conceptualized within the framework of hierarchical predictive coding as the result of a Bayesian inference process that uses prior beliefs to infer states of the world. According to this idea, prior beliefs encoded at higher levels in the brain are fed back as predictive signals to lower levels. Whenever these predictions are violated by the incoming sensory data, a prediction error is generated and fed forward to update beliefs encoded at higher levels. Well-documented impairments in cognitive decision-making support the view that these neural inference mechanisms are altered in schizophrenia. There is also extensive evidence relating the symptoms of schizophrenia to aberrant signaling of prediction errors, especially in the domain of reward and value-based decision-making. Moreover, the idea of altered predictive coding is supported by evidence for impaired low-level sensory mechanisms and motor processes. We review behavioral and neural findings from these research areas and provide an integrated view suggesting that schizophrenia may be related to a pervasive alteration in predictive coding at multiple hierarchical levels, including cognitive and value-based decision-making processes as well as sensory and motor systems. We relate these findings to decision-making processes and propose that varying degrees of impairment in the implicated brain areas contribute to the variety of psychotic experiences. Copyright © 2018 Elsevier Inc. All rights reserved.
-
Risk prediction and aversion by anterior cingulate cortex.
Brown, Joshua W; Braver, Todd S
2007-12-01
The recently proposed error-likelihood hypothesis suggests that anterior cingulate cortex (ACC) and surrounding areas will become active in proportion to the perceived likelihood of an error. The hypothesis was originally derived from a computational model prediction. The same computational model now makes a further prediction that ACC will be sensitive not only to predicted error likelihood, but also to the predicted magnitude of the consequences, should an error occur. The product of error likelihood and predicted error consequence magnitude collectively defines the general "expected risk" of a given behavior in a manner analogous but orthogonal to subjective expected utility theory. New fMRI results from an incentivechange signal task now replicate the error-likelihood effect, validate the further predictions of the computational model, and suggest why some segments of the population may fail to show an error-likelihood effect. In particular, error-likelihood effects and expected risk effects in general indicate greater sensitivity to earlier predictors of errors and are seen in risk-averse but not risk-tolerant individuals. Taken together, the results are consistent with an expected risk model of ACC and suggest that ACC may generally contribute to cognitive control by recruiting brain activity to avoid risk.
-
Brain Potentials Measured During a Go/NoGo Task Predict Completion of Substance Abuse Treatment
Steele, Vaughn R.; Fink, Brandi C.; Maurer, J. Michael; Arbabshirani, Mohammad R.; Wilber, Charles H.; Jaffe, Adam J.; Sidz, Anna; Pearlson, Godfrey D.; Calhoun, Vince D.; Clark, Vincent P.; Kiehl, Kent A.
2014-01-01
Background US nationwide estimates indicate 50–80% of prisoners have a history of substance abuse or dependence. Tailoring substance abuse treatment to specific needs of incarcerated individuals could improve effectiveness of treating substance dependence and preventing drug abuse relapse. The purpose of the present study was to test the hypothesis that pre-treatment neural measures of a Go/NoGo task would predict which individuals would or would not complete a 12-week cognitive behavioral substance abuse treatment program. Methods Adult incarcerated participants (N=89; Females=55) who volunteered for substance abuse treatment performed a response inhibition (Go/NoGo) task while event-related potentials (ERP) were recorded. Stimulus- and response-locked ERPs were compared between individuals who completed (N=68; Females=45) and discontinued (N=21; Females=10) treatment. Results As predicted, stimulus-locked P2, response-locked error-related negativity (ERN/Ne), and response-locked error positivity (Pe), measured with windowed time-domain and principal component analysis, differed between groups. Using logistic regression and support-vector machine (i.e., pattern classifiers) models, P2 and Pe predicted treatment completion above and beyond other measures (i.e., N2, P300, ERN/Ne, age, sex, IQ, impulsivity, and self-reported depression, anxiety, motivation for change, and years of drug abuse). Conclusions We conclude individuals who discontinue treatment exhibited deficiencies in sensory gating, as indexed by smaller P2, error-monitoring, as indexed by smaller ERN/Ne, and adjusting response strategy post-error, as indexed by larger Pe. However, the combination of P2 and Pe reliably predicted 83.33% of individuals who discontinued treatment. These results may help in the development of individualized therapies, which could lead to more favorable, long-term outcomes. PMID:24238783
-
Punishment sensitivity modulates the processing of negative feedback but not error-induced learning.
Unger, Kerstin; Heintz, Sonja; Kray, Jutta
2012-01-01
Accumulating evidence suggests that individual differences in punishment and reward sensitivity are associated with functional alterations in neural systems underlying error and feedback processing. In particular, individuals highly sensitive to punishment have been found to be characterized by larger mediofrontal error signals as reflected in the error negativity/error-related negativity (Ne/ERN) and the feedback-related negativity (FRN). By contrast, reward sensitivity has been shown to relate to the error positivity (Pe). Given that Ne/ERN, FRN, and Pe have been functionally linked to flexible behavioral adaptation, the aim of the present research was to examine how these electrophysiological reflections of error and feedback processing vary as a function of punishment and reward sensitivity during reinforcement learning. We applied a probabilistic learning task that involved three different conditions of feedback validity (100%, 80%, and 50%). In contrast to prior studies using response competition tasks, we did not find reliable correlations between punishment sensitivity and the Ne/ERN. Instead, higher punishment sensitivity predicted larger FRN amplitudes, irrespective of feedback validity. Moreover, higher reward sensitivity was associated with a larger Pe. However, only reward sensitivity was related to better overall learning performance and higher post-error accuracy, whereas highly punishment sensitive participants showed impaired learning performance, suggesting that larger negative feedback-related error signals were not beneficial for learning or even reflected maladaptive information processing in these individuals. Thus, although our findings indicate that individual differences in reward and punishment sensitivity are related to electrophysiological correlates of error and feedback processing, we found less evidence for influences of these personality characteristics on the relation between performance monitoring and feedback-based learning.
-
Lombardo, Franco; Berellini, Giuliano; Labonte, Laura R; Liang, Guiqing; Kim, Sean
2016-03-01
We present a systematic evaluation of the Wajima superpositioning method to estimate the human intravenous (i.v.) pharmacokinetic (PK) profile based on a set of 54 marketed drugs with diverse structure and range of physicochemical properties. We illustrate the use of average of "best methods" for the prediction of clearance (CL) and volume of distribution at steady state (VDss) as described in our earlier work (Lombardo F, Waters NJ, Argikar UA, et al. J Clin Pharmacol. 2013;53(2):178-191; Lombardo F, Waters NJ, Argikar UA, et al. J Clin Pharmacol. 2013;53(2):167-177). These methods provided much more accurate prediction of human PK parameters, yielding 88% and 70% of the prediction within 2-fold error for VDss and CL, respectively. The prediction of human i.v. profile using Wajima superpositioning of rat, dog, and monkey time-concentration profiles was tested against the observed human i.v. PK using fold error statistics. The results showed that 63% of the compounds yielded a geometric mean of fold error below 2-fold, and an additional 19% yielded a geometric mean of fold error between 2- and 3-fold, leaving only 18% of the compounds with a relatively poor prediction. Our results showed that good superposition was observed in any case, demonstrating the predictive value of the Wajima approach, and that the cause of poor prediction of human i.v. profile was mainly due to the poorly predicted CL value, while VDss prediction had a minor impact on the accuracy of human i.v. profile prediction. Copyright © 2016. Published by Elsevier Inc.
-
NASA Astrophysics Data System (ADS)
Zhang, Hao; Yuan, Yan; Su, Lijuan; Huang, Fengzhen; Bai, Qing
2016-09-01
The Risley-prism-based light beam steering apparatus delivers superior pointing accuracy and it is used in imaging LIDAR and imaging microscopes. A general model for pointing error analysis of the Risley prisms is proposed in this paper, based on ray direction deviation in light refraction. This model captures incident beam deviation, assembly deflections, and prism rotational error. We derive the transmission matrixes of the model firstly. Then, the independent and cumulative effects of different errors are analyzed through this model. Accuracy study of the model shows that the prediction deviation of pointing error for different error is less than 4.1×10-5° when the error amplitude is 0.1°. Detailed analyses of errors indicate that different error sources affect the pointing accuracy to varying degree, and the major error source is the incident beam deviation. The prism tilting has a relative big effect on the pointing accuracy when prism tilts in the principal section. The cumulative effect analyses of multiple errors represent that the pointing error can be reduced by tuning the bearing tilting in the same direction. The cumulative effect of rotational error is relative big when the difference of these two prism rotational angles equals 0 or π, while it is relative small when the difference equals π/2. The novelty of these results suggests that our analysis can help to uncover the error distribution and aid in measurement calibration of Risley-prism systems.
-
Robust and Adaptive Online Time Series Prediction with Long Short-Term Memory
Tao, Qing
2017-01-01
Online time series prediction is the mainstream method in a wide range of fields, ranging from speech analysis and noise cancelation to stock market analysis. However, the data often contains many outliers with the increasing length of time series in real world. These outliers can mislead the learned model if treated as normal points in the process of prediction. To address this issue, in this paper, we propose a robust and adaptive online gradient learning method, RoAdam (Robust Adam), for long short-term memory (LSTM) to predict time series with outliers. This method tunes the learning rate of the stochastic gradient algorithm adaptively in the process of prediction, which reduces the adverse effect of outliers. It tracks the relative prediction error of the loss function with a weighted average through modifying Adam, a popular stochastic gradient method algorithm for training deep neural networks. In our algorithm, the large value of the relative prediction error corresponds to a small learning rate, and vice versa. The experiments on both synthetic data and real time series show that our method achieves better performance compared to the existing methods based on LSTM. PMID:29391864
-
Robust and Adaptive Online Time Series Prediction with Long Short-Term Memory.
Yang, Haimin; Pan, Zhisong; Tao, Qing
2017-01-01
Online time series prediction is the mainstream method in a wide range of fields, ranging from speech analysis and noise cancelation to stock market analysis. However, the data often contains many outliers with the increasing length of time series in real world. These outliers can mislead the learned model if treated as normal points in the process of prediction. To address this issue, in this paper, we propose a robust and adaptive online gradient learning method, RoAdam (Robust Adam), for long short-term memory (LSTM) to predict time series with outliers. This method tunes the learning rate of the stochastic gradient algorithm adaptively in the process of prediction, which reduces the adverse effect of outliers. It tracks the relative prediction error of the loss function with a weighted average through modifying Adam, a popular stochastic gradient method algorithm for training deep neural networks. In our algorithm, the large value of the relative prediction error corresponds to a small learning rate, and vice versa. The experiments on both synthetic data and real time series show that our method achieves better performance compared to the existing methods based on LSTM.
-
NASA Astrophysics Data System (ADS)
Liang, Zhongmin; Li, Yujie; Hu, Yiming; Li, Binquan; Wang, Jun
2017-06-01
Accurate and reliable long-term forecasting plays an important role in water resources management and utilization. In this paper, a hybrid model called SVR-HUP is presented to predict long-term runoff and quantify the prediction uncertainty. The model is created based on three steps. First, appropriate predictors are selected according to the correlations between meteorological factors and runoff. Second, a support vector regression (SVR) model is structured and optimized based on the LibSVM toolbox and a genetic algorithm. Finally, using forecasted and observed runoff, a hydrologic uncertainty processor (HUP) based on a Bayesian framework is used to estimate the posterior probability distribution of the simulated values, and the associated uncertainty of prediction was quantitatively analyzed. Six precision evaluation indexes, including the correlation coefficient (CC), relative root mean square error (RRMSE), relative error (RE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE), and qualification rate (QR), are used to measure the prediction accuracy. As a case study, the proposed approach is applied in the Han River basin, South Central China. Three types of SVR models are established to forecast the monthly, flood season and annual runoff volumes. The results indicate that SVR yields satisfactory accuracy and reliability at all three scales. In addition, the results suggest that the HUP cannot only quantify the uncertainty of prediction based on a confidence interval but also provide a more accurate single value prediction than the initial SVR forecasting result. Thus, the SVR-HUP model provides an alternative method for long-term runoff forecasting.
-
Keysers, Christian; Perrett, David I; Gazzola, Valeria
2014-04-01
Hebbian Learning should not be reduced to contiguity, as it detects contingency and causality. Hebbian Learning accounts of mirror neurons make predictions that differ from associative learning: Through Hebbian Learning, mirror neurons become dynamic networks that calculate predictions and prediction errors and relate to ideomotor theories. The social force of imitation is important for mirror neuron emergence and suggests canalization.
-
Prediction of boiling points of organic compounds by QSPR tools.
Dai, Yi-min; Zhu, Zhi-ping; Cao, Zhong; Zhang, Yue-fei; Zeng, Ju-lan; Li, Xun
2013-07-01
The novel electro-negativity topological descriptors of YC, WC were derived from molecular structure by equilibrium electro-negativity of atom and relative bond length of molecule. The quantitative structure-property relationships (QSPR) between descriptors of YC, WC as well as path number parameter P3 and the normal boiling points of 80 alkanes, 65 unsaturated hydrocarbons and 70 alcohols were obtained separately. The high-quality prediction models were evidenced by coefficient of determination (R(2)), the standard error (S), average absolute errors (AAE) and predictive parameters (Qext(2),RCV(2),Rm(2)). According to the regression equations, the influences of the length of carbon backbone, the size, the degree of branching of a molecule and the role of functional groups on the normal boiling point were analyzed. Comparison results with reference models demonstrated that novel topological descriptors based on the equilibrium electro-negativity of atom and the relative bond length were useful molecular descriptors for predicting the normal boiling points of organic compounds. Copyright © 2013 Elsevier Inc. All rights reserved.
-
Drug Distribution. Part 1. Models to Predict Membrane Partitioning.
Nagar, Swati; Korzekwa, Ken
2017-03-01
Tissue partitioning is an important component of drug distribution and half-life. Protein binding and lipid partitioning together determine drug distribution. Two structure-based models to predict partitioning into microsomal membranes are presented. An orientation-based model was developed using a membrane template and atom-based relative free energy functions to select drug conformations and orientations for neutral and basic drugs. The resulting model predicts the correct membrane positions for nine compounds tested, and predicts the membrane partitioning for n = 67 drugs with an average fold-error of 2.4. Next, a more facile descriptor-based model was developed for acids, neutrals and bases. This model considers the partitioning of neutral and ionized species at equilibrium, and can predict membrane partitioning with an average fold-error of 2.0 (n = 92 drugs). Together these models suggest that drug orientation is important for membrane partitioning and that membrane partitioning can be well predicted from physicochemical properties.
-
From feedback- to response-based performance monitoring in active and observational learning.
Bellebaum, Christian; Colosio, Marco
2014-09-01
Humans can adapt their behavior by learning from the consequences of their own actions or by observing others. Gradual active learning of action-outcome contingencies is accompanied by a shift from feedback- to response-based performance monitoring. This shift is reflected by complementary learning-related changes of two ACC-driven ERP components, the feedback-related negativity (FRN) and the error-related negativity (ERN), which have both been suggested to signal events "worse than expected," that is, a negative prediction error. Although recent research has identified comparable components for observed behavior and outcomes (observational ERN and FRN), it is as yet unknown, whether these components are similarly modulated by prediction errors and thus also reflect behavioral adaptation. In this study, two groups of 15 participants learned action-outcome contingencies either actively or by observation. In active learners, FRN amplitude for negative feedback decreased and ERN amplitude in response to erroneous actions increased with learning, whereas observational ERN and FRN in observational learners did not exhibit learning-related changes. Learning performance, assessed in test trials without feedback, was comparable between groups, as was the ERN following actively performed errors during test trials. In summary, the results show that action-outcome associations can be learned similarly well actively and by observation. The mechanisms involved appear to differ, with the FRN in active learning reflecting the integration of information about own actions and the accompanying outcomes.
-
Phonological Awareness and Types of Sound Errors in Preschoolers with Speech Sound Disorders
ERIC Educational Resources Information Center
Preston, Jonathan; Edwards, Mary Louise
2010-01-01
Purpose: Some children with speech sound disorders (SSD) have difficulty with literacy-related skills, particularly phonological awareness (PA). This study investigates the PA skills of preschoolers with SSD by using a regression model to evaluate the degree to which PA can be concurrently predicted by types of speech sound errors. Method:…
-
ERIC Educational Resources Information Center
Rodriguez, Paul F.
2009-01-01
Memory systems are known to be influenced by feedback and error processing, but it is not well known what aspects of outcome contingencies are related to different memory systems. Here we use the Rescorla-Wagner model to estimate prediction errors in an fMRI study of stimulus-outcome association learning. The conditional probabilities of outcomes…
-
The predictive mind and the experience of visual art work
Kesner, Ladislav
2014-01-01
Among the main challenges of the predictive brain/mind concept is how to link prediction at the neural level to prediction at the cognitive-psychological level and finding conceptually robust and empirically verifiable ways to harness this theoretical framework toward explaining higher-order mental and cognitive phenomena, including the subjective experience of aesthetic and symbolic forms. Building on the tentative prediction error account of visual art, this article extends the application of the predictive coding framework to the visual arts. It does so by linking this theoretical discussion to a subjective, phenomenological account of how a work of art is experienced. In order to engage more deeply with a work of art, viewers must be able to tune or adapt their prediction mechanism to recognize art as a specific class of objects whose ontological nature defies predictability, and they must be able to sustain a productive flow of predictions from low-level sensory, recognitional to abstract semantic, conceptual, and affective inferences. The affective component of the process of predictive error optimization that occurs when a viewer enters into dialog with a painting is constituted both by activating the affective affordances within the image and by the affective consequences of prediction error minimization itself. The predictive coding framework also has implications for the problem of the culturality of vision. A person’s mindset, which determines what top–down expectations and predictions are generated, is co-constituted by culture-relative skills and knowledge, which form hyperpriors that operate in the perception of art. PMID:25566111
-
The predictive mind and the experience of visual art work.
Kesner, Ladislav
2014-01-01
Among the main challenges of the predictive brain/mind concept is how to link prediction at the neural level to prediction at the cognitive-psychological level and finding conceptually robust and empirically verifiable ways to harness this theoretical framework toward explaining higher-order mental and cognitive phenomena, including the subjective experience of aesthetic and symbolic forms. Building on the tentative prediction error account of visual art, this article extends the application of the predictive coding framework to the visual arts. It does so by linking this theoretical discussion to a subjective, phenomenological account of how a work of art is experienced. In order to engage more deeply with a work of art, viewers must be able to tune or adapt their prediction mechanism to recognize art as a specific class of objects whose ontological nature defies predictability, and they must be able to sustain a productive flow of predictions from low-level sensory, recognitional to abstract semantic, conceptual, and affective inferences. The affective component of the process of predictive error optimization that occurs when a viewer enters into dialog with a painting is constituted both by activating the affective affordances within the image and by the affective consequences of prediction error minimization itself. The predictive coding framework also has implications for the problem of the culturality of vision. A person's mindset, which determines what top-down expectations and predictions are generated, is co-constituted by culture-relative skills and knowledge, which form hyperpriors that operate in the perception of art.
-
NASA Astrophysics Data System (ADS)
Wold, Alexandra M.; Mays, M. Leila; Taktakishvili, Aleksandre; Jian, Lan K.; Odstrcil, Dusan; MacNeice, Peter
2018-03-01
The Wang-Sheeley-Arge (WSA)-ENLIL+Cone model is used extensively in space weather operations world-wide to model coronal mass ejection (CME) propagation. As such, it is important to assess its performance. We present validation results of the WSA-ENLIL+Cone model installed at the Community Coordinated Modeling Center (CCMC) and executed in real-time by the CCMC space weather team. CCMC uses the WSA-ENLIL+Cone model to predict CME arrivals at NASA missions throughout the inner heliosphere. In this work we compare model predicted CME arrival-times to in situ interplanetary coronal mass ejection leading edge measurements at Solar TErrestrial RElations Observatory-Ahead (STEREO-A), Solar TErrestrial RElations Observatory-Behind (STEREO-B), and Earth (Wind and ACE) for simulations completed between March 2010 and December 2016 (over 1,800 CMEs). We report hit, miss, false alarm, and correct rejection statistics for all three locations. For all predicted CME arrivals, the hit rate is 0.5, and the false alarm rate is 0.1. For the 273 events where the CME was predicted to arrive at Earth, STEREO-A, or STEREO-B, and was actually observed (hit event), the mean absolute arrival-time prediction error was 10.4 ± 0.9 h, with a tendency to early prediction error of -4.0 h. We show the dependence of the arrival-time error on CME input parameters. We also explore the impact of the multi-spacecraft observations used to initialize the model CME inputs by comparing model verification results before and after the STEREO-B communication loss (since September 2014) and STEREO-A sidelobe operations (August 2014-December 2015). There is an increase of 1.7 h in the CME arrival time error during single, or limited two-viewpoint periods, compared to the three-spacecraft viewpoint period. This trend would apply to a future space weather mission at L5 or L4 as another coronagraph viewpoint to reduce CME arrival time errors compared to a single L1 viewpoint.
-
Double dissociation of value computations in orbitofrontal and anterior cingulate neurons
Kennerley, Steven W.; Behrens, Timothy E. J.; Wallis, Jonathan D.
2011-01-01
Damage to prefrontal cortex (PFC) impairs decision-making, but the underlying value computations that might cause such impairments remain unclear. Here we report that value computations are doubly dissociable within PFC neurons. While many PFC neurons encoded chosen value, they used opponent encoding schemes such that averaging the neuronal population eliminated value coding. However, a special population of neurons in anterior cingulate cortex (ACC) - but not orbitofrontal cortex (OFC) - multiplex chosen value across decision parameters using a unified encoding scheme, and encoded reward prediction errors. In contrast, neurons in OFC - but not ACC - encoded chosen value relative to the recent history of choice values. Together, these results suggest complementary valuation processes across PFC areas: OFC neurons dynamically evaluate current choices relative to recent choice values, while ACC neurons encode choice predictions and prediction errors using a common valuation currency reflecting the integration of multiple decision parameters. PMID:22037498
-
Routine cognitive errors: a trait-like predictor of individual differences in anxiety and distress.
Fetterman, Adam K; Robinson, Michael D
2011-02-01
Five studies (N=361) sought to model a class of errors--namely, those in routine tasks--that several literatures have suggested may predispose individuals to higher levels of emotional distress. Individual differences in error frequency were assessed in choice reaction-time tasks of a routine cognitive type. In Study 1, it was found that tendencies toward error in such tasks exhibit trait-like stability over time. In Study 3, it was found that tendencies toward error exhibit trait-like consistency across different tasks. Higher error frequency, in turn, predicted higher levels of negative affect, general distress symptoms, displayed levels of negative emotion during an interview, and momentary experiences of negative emotion in daily life (Studies 2-5). In all cases, such predictive relations remained significant with individual differences in neuroticism controlled. The results thus converge on the idea that error frequency in simple cognitive tasks is a significant and consequential predictor of emotional distress in everyday life. The results are novel, but discussed within the context of the wider literatures that informed them. © 2010 Psychology Press, an imprint of the Taylor & Francis Group, an Informa business
-
Are phonological influences on lexical (mis)selection the result of a monitoring bias?
Ratinckx, Elie; Ferreira, Victor S.; Hartsuiker, Robert J.
2009-01-01
A monitoring bias account is often used to explain speech error patterns that seem to be the result of an interactive language production system, like phonological influences on lexical selection errors. A biased monitor is suggested to detect and covertly correct certain errors more often than others. For instance, this account predicts that errors which are phonologically similar to intended words are harder to detect than ones that are phonologically dissimilar. To test this, we tried to elicit phonological errors under the same conditions that show other kinds of lexical selection errors. In five experiments, we presented participants with high cloze probability sentence fragments followed by a picture that was either semantically related, a homophone of a semantically related word, or phonologically related to the (implicit) last word of the sentence. All experiments elicited semantic completions or homophones of semantic completions, but none elicited phonological completions. This finding is hard to reconcile with a monitoring bias account and is better explained with an interactive production system. Additionally, this finding constrains the amount of bottom-up information flow in interactive models. PMID:18942035
-
Analysis of a Shock-Associated Noise Prediction Model Using Measured Jet Far-Field Noise Data
NASA Technical Reports Server (NTRS)
Dahl, Milo D.; Sharpe, Jacob A.
2014-01-01
A code for predicting supersonic jet broadband shock-associated noise was assessed us- ing a database containing noise measurements of a jet issuing from a convergent nozzle. The jet was operated at 24 conditions covering six fully expanded Mach numbers with four total temperature ratios. To enable comparisons of the predicted shock-associated noise component spectra with data, the measured total jet noise spectra were separated into mixing noise and shock-associated noise component spectra. Comparisons between predicted and measured shock-associated noise component spectra were used to identify de ciencies in the prediction model. Proposed revisions to the model, based on a study of the overall sound pressure levels for the shock-associated noise component of the mea- sured data, a sensitivity analysis of the model parameters with emphasis on the de nition of the convection velocity parameter, and a least-squares t of the predicted to the mea- sured shock-associated noise component spectra, resulted in a new de nition for the source strength spectrum in the model. An error analysis showed that the average error in the predicted spectra was reduced by as much as 3.5 dB for the revised model relative to the average error for the original model.
-
NASA Technical Reports Server (NTRS)
Duda, David P.; Minnis, Patrick
2009-01-01
Straightforward application of the Schmidt-Appleman contrail formation criteria to diagnose persistent contrail occurrence from numerical weather prediction data is hindered by significant bias errors in the upper tropospheric humidity. Logistic models of contrail occurrence have been proposed to overcome this problem, but basic questions remain about how random measurement error may affect their accuracy. A set of 5000 synthetic contrail observations is created to study the effects of random error in these probabilistic models. The simulated observations are based on distributions of temperature, humidity, and vertical velocity derived from Advanced Regional Prediction System (ARPS) weather analyses. The logistic models created from the simulated observations were evaluated using two common statistical measures of model accuracy, the percent correct (PC) and the Hanssen-Kuipers discriminant (HKD). To convert the probabilistic results of the logistic models into a dichotomous yes/no choice suitable for the statistical measures, two critical probability thresholds are considered. The HKD scores are higher when the climatological frequency of contrail occurrence is used as the critical threshold, while the PC scores are higher when the critical probability threshold is 0.5. For both thresholds, typical random errors in temperature, relative humidity, and vertical velocity are found to be small enough to allow for accurate logistic models of contrail occurrence. The accuracy of the models developed from synthetic data is over 85 percent for both the prediction of contrail occurrence and non-occurrence, although in practice, larger errors would be anticipated.
-
Zainudin, Suhaila; Arif, Shereena M.
2017-01-01
Gene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR) to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C) as a direct interaction (A → C). Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5. PMID:28250767
-
Blind prediction of cyclohexane-water distribution coefficients from the SAMPL5 challenge.
Bannan, Caitlin C; Burley, Kalistyn H; Chiu, Michael; Shirts, Michael R; Gilson, Michael K; Mobley, David L
2016-11-01
In the recent SAMPL5 challenge, participants submitted predictions for cyclohexane/water distribution coefficients for a set of 53 small molecules. Distribution coefficients (log D) replace the hydration free energies that were a central part of the past five SAMPL challenges. A wide variety of computational methods were represented by the 76 submissions from 18 participating groups. Here, we analyze submissions by a variety of error metrics and provide details for a number of reference calculations we performed. As in the SAMPL4 challenge, we assessed the ability of participants to evaluate not just their statistical uncertainty, but their model uncertainty-how well they can predict the magnitude of their model or force field error for specific predictions. Unfortunately, this remains an area where prediction and analysis need improvement. In SAMPL4 the top performing submissions achieved a root-mean-squared error (RMSE) around 1.5 kcal/mol. If we anticipate accuracy in log D predictions to be similar to the hydration free energy predictions in SAMPL4, the expected error here would be around 1.54 log units. Only a few submissions had an RMSE below 2.5 log units in their predicted log D values. However, distribution coefficients introduced complexities not present in past SAMPL challenges, including tautomer enumeration, that are likely to be important in predicting biomolecular properties of interest to drug discovery, therefore some decrease in accuracy would be expected. Overall, the SAMPL5 distribution coefficient challenge provided great insight into the importance of modeling a variety of physical effects. We believe these types of measurements will be a promising source of data for future blind challenges, especially in view of the relatively straightforward nature of the experiments and the level of insight provided.
-
Blind prediction of cyclohexane-water distribution coefficients from the SAMPL5 challenge
Bannan, Caitlin C.; Burley, Kalistyn H.; Chiu, Michael; Shirts, Michael R.; Gilson, Michael K.; Mobley, David L.
2016-01-01
In the recent SAMPL5 challenge, participants submitted predictions for cyclohexane/water distribution coefficients for a set of 53 small molecules. Distribution coefficients (log D) replace the hydration free energies that were a central part of the past five SAMPL challenges. A wide variety of computational methods were represented by the 76 submissions from 18 participating groups. Here, we analyze submissions by a variety of error metrics and provide details for a number of reference calculations we performed. As in the SAMPL4 challenge, we assessed the ability of participants to evaluate not just their statistical uncertainty, but their model uncertainty – how well they can predict the magnitude of their model or force field error for specific predictions. Unfortunately, this remains an area where prediction and analysis need improvement. In SAMPL4 the top performing submissions achieved a root-mean-squared error (RMSE) around 1.5 kcal/mol. If we anticipate accuracy in log D predictions to be similar to the hydration free energy predictions in SAMPL4, the expected error here would be around 1.54 log units. Only a few submissions had an RMSE below 2.5 log units in their predicted log D values. However, distribution coefficients introduced complexities not present in past SAMPL challenges, including tautomer enumeration, that are likely to be important in predicting biomolecular properties of interest to drug discovery, therefore some decrease in accuracy would be expected. Overall, the SAMPL5 distribution coefficient challenge provided great insight into the importance of modeling a variety of physical effects. We believe these types of measurements will be a promising source of data for future blind challenges, especially in view of the relatively straightforward nature of the experiments and the level of insight provided. PMID:27677750
-
New equations improve NIR prediction of body fat among high school wrestlers.
Oppliger, R A; Clark, R R; Nielsen, D H
2000-09-01
Methodologic study to derive prediction equations for percent body fat (%BF). To develop valid regression equations using NIR to assess body composition among high school wrestlers. Clinicians need a portable, fast, and simple field method for assessing body composition among wrestlers. Near-infrared photospectrometry (NIR) meets these criteria, but its efficacy has been challenged. Subjects were 150 high school wrestlers from 2 Midwestern states with mean +/- SD age of 16.3 +/- 1.1 yrs, weight of 69.5 +/- 11.7 kg, and height of 174.4 +/- 7.0 cm. Relative body fatness (%BF) determined from hydrostatic weighing was the criterion measure, and NIR optical density (OD) measurements at multiple sites, plus height, weight, and body mass index (BMI) were the predictor variables. Four equations were developed with multiple R2s that varied from .530 to .693, root mean squared errors varied from 2.8% BF to 3.4% BF, and prediction errors varied from 2.9% BF to 3.1% BF. The best equation used OD measurements at the biceps, triceps, and thigh sites, BMI, and age. The root mean squared error and prediction error for all 4 equations were equal to or smaller than for a skinfold equation commonly used with wrestlers. The results substantiate the validity of NIR for predicting % BF among high school wrestlers. Cross-validation of these equations is warranted.
-
Compound Stimulus Presentation Does Not Deepen Extinction in Human Causal Learning
Griffiths, Oren; Holmes, Nathan; Westbrook, R. Fred
2017-01-01
Models of associative learning have proposed that cue-outcome learning critically depends on the degree of prediction error encountered during training. Two experiments examined the role of error-driven extinction learning in a human causal learning task. Target cues underwent extinction in the presence of additional cues, which differed in the degree to which they predicted the outcome, thereby manipulating outcome expectancy and, in the absence of any change in reinforcement, prediction error. These prediction error manipulations have each been shown to modulate extinction learning in aversive conditioning studies. While both manipulations resulted in increased prediction error during training, neither enhanced extinction in the present human learning task (one manipulation resulted in less extinction at test). The results are discussed with reference to the types of associations that are regulated by prediction error, the types of error terms involved in their regulation, and how these interact with parameters involved in training. PMID:28232809
-
Wei, Wenjuan; Xiong, Jianyin; Zhang, Yinping
2013-01-01
Mass transfer models are useful in predicting the emissions of volatile organic compounds (VOCs) and formaldehyde from building materials in indoor environments. They are also useful for human exposure evaluation and in sustainable building design. The measurement errors in the emission characteristic parameters in these mass transfer models, i.e., the initial emittable concentration (C 0), the diffusion coefficient (D), and the partition coefficient (K), can result in errors in predicting indoor VOC and formaldehyde concentrations. These errors have not yet been quantitatively well analyzed in the literature. This paper addresses this by using modelling to assess these errors for some typical building conditions. The error in C 0, as measured in environmental chambers and applied to a reference living room in Beijing, has the largest influence on the model prediction error in indoor VOC and formaldehyde concentration, while the error in K has the least effect. A correlation between the errors in D, K, and C 0 and the error in the indoor VOC and formaldehyde concentration prediction is then derived for engineering applications. In addition, the influence of temperature on the model prediction of emissions is investigated. It shows the impact of temperature fluctuations on the prediction errors in indoor VOC and formaldehyde concentrations to be less than 7% at 23±0.5°C and less than 30% at 23±2°C.
-
Hazel, Joseph E.; Kaplinski, Matt; Parnell, Rod; Kohl, Keith; Topping, David J.
2007-01-01
This report presents stage-discharge relations for 47 discrete locations along the Colorado River, downstream from Glen Canyon Dam. Predicting the river stage that results from changes in flow regime is important for many studies investigating the effects of dam operations on resources in and along the Colorado River. The empirically based stage-discharge relations were developed from water-surface elevation data surveyed at known discharges at all 47 locations. The rating curves accurately predict stage at each location for discharges between 141 cubic meters per second and 1,274 cubic meters per second. The coefficient of determination (R2) of the fit to the data ranged from 0.993 to 1.00. Given the various contributing errors to the method, a conservative error estimate of ?0.05 m was assigned to the rating curves.
-
Representation of aversive prediction errors in the human periaqueductal gray
Roy, Mathieu; Shohamy, Daphna; Daw, Nathaniel; Jepma, Marieke; Wimmer, Elliott; Wager, Tor D.
2014-01-01
Pain is a primary driver of learning and motivated action. It is also a target of learning, as nociceptive brain responses are shaped by learning processes. We combined an instrumental pain avoidance task with an axiomatic approach to assessing fMRI signals related to prediction errors (PEs), which drive reinforcement-based learning. We found that pain PEs were encoded in the periaqueductal gray (PAG), an important structure for pain control and learning in animal models. Axiomatic tests combined with dynamic causal modeling suggested that ventromedial prefrontal cortex, supported by putamen, provides an expected value-related input to the PAG, which then conveys PE signals to prefrontal regions important for behavioral regulation, including orbitofrontal, anterior mid-cingulate, and dorsomedial prefrontal cortices. Thus, pain-related learning involves distinct neural circuitry, with implications for behavior and pain dynamics. PMID:25282614
-
Aberg, Kristoffer C; Müller, Julia; Schwartz, Sophie
2017-01-01
Anticipation and delivery of rewards improves memory formation, but little effort has been made to disentangle their respective contributions to memory enhancement. Moreover, it has been suggested that the effects of reward on memory are mediated by dopaminergic influences on hippocampal plasticity. Yet, evidence linking memory improvements to actual reward computations reflected in the activity of the dopaminergic system, i.e., prediction errors and expected values, is scarce and inconclusive. For example, different previous studies reported that the magnitude of prediction errors during a reinforcement learning task was a positive, negative, or non-significant predictor of successfully encoding simultaneously presented images. Individual sensitivities to reward and punishment have been found to influence the activation of the dopaminergic reward system and could therefore help explain these seemingly discrepant results. Here, we used a novel associative memory task combined with computational modeling and showed independent effects of reward-delivery and reward-anticipation on memory. Strikingly, the computational approach revealed positive influences from both reward delivery, as mediated by prediction error magnitude, and reward anticipation, as mediated by magnitude of expected value, even in the absence of behavioral effects when analyzed using standard methods, i.e., by collapsing memory performance across trials within conditions. We additionally measured trait estimates of reward and punishment sensitivity and found that individuals with increased reward (vs. punishment) sensitivity had better memory for associations encoded during positive (vs. negative) prediction errors when tested after 20 min, but a negative trend when tested after 24 h. In conclusion, modeling trial-by-trial fluctuations in the magnitude of reward, as we did here for prediction errors and expected value computations, provides a comprehensive and biologically plausible description of the dynamic interplay between reward, dopamine, and associative memory formation. Our results also underline the importance of considering individual traits when assessing reward-related influences on memory.
-
Impacts of Satellite Orbit and Clock on Real-Time GPS Point and Relative Positioning.
Shi, Junbo; Wang, Gaojing; Han, Xianquan; Guo, Jiming
2017-06-12
Satellite orbit and clock corrections are always treated as known quantities in GPS positioning models. Therefore, any error in the satellite orbit and clock products will probably cause significant consequences for GPS positioning, especially for real-time applications. Currently three types of satellite products have been made available for real-time positioning, including the broadcast ephemeris, the International GNSS Service (IGS) predicted ultra-rapid product, and the real-time product. In this study, these three predicted/real-time satellite orbit and clock products are first evaluated with respect to the post-mission IGS final product, which demonstrates cm to m level orbit accuracies and sub-ns to ns level clock accuracies. Impacts of real-time satellite orbit and clock products on GPS point and relative positioning are then investigated using the P3 and GAMIT software packages, respectively. Numerical results show that the real-time satellite clock corrections affect the point positioning more significantly than the orbit corrections. On the contrary, only the real-time orbit corrections impact the relative positioning. Compared with the positioning solution using the IGS final product with the nominal orbit accuracy of ~2.5 cm, the real-time broadcast ephemeris with ~2 m orbit accuracy provided <2 cm relative positioning error for baselines no longer than 216 km. As for the baselines ranging from 574 to 2982 km, the cm-dm level positioning error was identified for the relative positioning solution using the broadcast ephemeris. The real-time product could result in <5 mm relative positioning accuracy for baselines within 2982 km, slightly better than the predicted ultra-rapid product.
-
DOE Office of Scientific and Technical Information (OSTI.GOV)
Morley, Steven Karl
This report reviews existing literature describing forecast accuracy metrics, concentrating on those based on relative errors and percentage errors. We then review how the most common of these metrics, the mean absolute percentage error (MAPE), has been applied in recent radiation belt modeling literature. Finally, we describe metrics based on the ratios of predicted to observed values (the accuracy ratio) that address the drawbacks inherent in using MAPE. Specifically, we define and recommend the median log accuracy ratio as a measure of bias and the median symmetric accuracy as a measure of accuracy.
-
Buzzell, George A; Troller-Renfree, Sonya V; Barker, Tyson V; Bowman, Lindsay C; Chronis-Tuscano, Andrea; Henderson, Heather A; Kagan, Jerome; Pine, Daniel S; Fox, Nathan A
2017-12-01
Behavioral inhibition (BI) is a temperament identified in early childhood that is a risk factor for later social anxiety. However, mechanisms underlying the development of social anxiety remain unclear. To better understand the emergence of social anxiety, longitudinal studies investigating changes at behavioral neural levels are needed. BI was assessed in the laboratory at 2 and 3 years of age (N = 268). Children returned at 12 years, and an electroencephalogram was recorded while children performed a flanker task under 2 conditions: once while believing they were being observed by peers and once while not being observed. This methodology isolated changes in error monitoring (error-related negativity) and behavior (post-error reaction time slowing) as a function of social context. At 12 years, current social anxiety symptoms and lifetime diagnoses of social anxiety were obtained. Childhood BI prospectively predicted social-specific error-related negativity increases and social anxiety symptoms in adolescence; these symptoms directly related to clinical diagnoses. Serial mediation analysis showed that social error-related negativity changes explained relations between BI and social anxiety symptoms (n = 107) and diagnosis (n = 92), but only insofar as social context also led to increased post-error reaction time slowing (a measure of error preoccupation); this model was not significantly related to generalized anxiety. Results extend prior work on socially induced changes in error monitoring and error preoccupation. These measures could index a neurobehavioral mechanism linking BI to adolescent social anxiety symptoms and diagnosis. This mechanism could relate more strongly to social than to generalized anxiety in the peri-adolescent period. Copyright © 2017 American Academy of Child and Adolescent Psychiatry. All rights reserved.
-
Alternative mechanisms for regulating racial responses according to internal vs external cues.
Amodio, David M; Kubota, Jennifer T; Harmon-Jones, Eddie; Devine, Patricia G
2006-06-01
Personal (internal) and normative (external) impetuses for regulating racially biased behaviour are well-documented, yet the extent to which internally and externally driven regulatory processes arise from the same mechanism is unknown. Whereas the regulation of race bias according to internal cues has been associated with conflict-monitoring processes and activation of the dorsal anterior cingulate cortex (dACC), we proposed that responses regulated according to external cues to respond without prejudice involves mechanisms of error-perception, a process associated with rostral anterior cingulate cortex (rACC) activity. We recruited low-prejudice participants who reported high or low sensitivity to non-prejudiced norms, and participants completed a stereotype inhibition task in private or public while electroencephalography was recorded. Analysis of event-related potentials revealed that the error-related negativity component, linked to dACC activity, predicted behavioural control of bias across conditions, whereas the error-perception component, linked to rACC activity, predicted control only in public among participants sensitive to external pressures to respond without prejudice.
-
Hughes, Charmayne M L; Baber, Chris; Bienkiewicz, Marta; Worthington, Andrew; Hazell, Alexa; Hermsdörfer, Joachim
2015-01-01
Approximately 33% of stroke patients have difficulty performing activities of daily living, often committing errors during the planning and execution of such activities. The objective of this study was to evaluate the ability of the human error identification (HEI) technique SHERPA (Systematic Human Error Reduction and Prediction Approach) to predict errors during the performance of daily activities in stroke patients with left and right hemisphere lesions. Using SHERPA we successfully predicted 36 of the 38 observed errors, with analysis indicating that the proportion of predicted and observed errors was similar for all sub-tasks and severity levels. HEI results were used to develop compensatory cognitive strategies that clinicians could employ to reduce or prevent errors from occurring. This study provides evidence for the reliability and validity of SHERPA in the design of cognitive rehabilitation strategies in stroke populations.
-
Keysers, Christian; Perrett, David I.; Gazzola, Valeria
2015-01-01
Hebbian Learning should not be reduced to contiguity since it detects contingency and causality. Hebbian Learning accounts of mirror neurons make predictions that differ from associative learning: through Hebbian Learning mirror neurons become dynamic networks that calculate predictions and prediction errors and relate to ideomotor theories. The social force of imitation is important for mirror neuron emergence and suggests canalization. PMID:24775162
-
Understanding seasonal variability of uncertainty in hydrological prediction
NASA Astrophysics Data System (ADS)
Li, M.; Wang, Q. J.
2012-04-01
Understanding uncertainty in hydrological prediction can be highly valuable for improving the reliability of streamflow prediction. In this study, a monthly water balance model, WAPABA, in a Bayesian joint probability with error models are presented to investigate the seasonal dependency of prediction error structure. A seasonal invariant error model, analogous to traditional time series analysis, uses constant parameters for model error and account for no seasonal variations. In contrast, a seasonal variant error model uses a different set of parameters for bias, variance and autocorrelation for each individual calendar month. Potential connection amongst model parameters from similar months is not considered within the seasonal variant model and could result in over-fitting and over-parameterization. A hierarchical error model further applies some distributional restrictions on model parameters within a Bayesian hierarchical framework. An iterative algorithm is implemented to expedite the maximum a posterior (MAP) estimation of a hierarchical error model. Three error models are applied to forecasting streamflow at a catchment in southeast Australia in a cross-validation analysis. This study also presents a number of statistical measures and graphical tools to compare the predictive skills of different error models. From probability integral transform histograms and other diagnostic graphs, the hierarchical error model conforms better to reliability when compared to the seasonal invariant error model. The hierarchical error model also generally provides the most accurate mean prediction in terms of the Nash-Sutcliffe model efficiency coefficient and the best probabilistic prediction in terms of the continuous ranked probability score (CRPS). The model parameters of the seasonal variant error model are very sensitive to each cross validation, while the hierarchical error model produces much more robust and reliable model parameters. Furthermore, the result of the hierarchical error model shows that most of model parameters are not seasonal variant except for error bias. The seasonal variant error model is likely to use more parameters than necessary to maximize the posterior likelihood. The model flexibility and robustness indicates that the hierarchical error model has great potential for future streamflow predictions.
-
Cole, Sindy; McNally, Gavan P
2007-10-01
Three experiments studied temporal-difference (TD) prediction errors during Pavlovian fear conditioning. In Stage I, rats received conditioned stimulus A (CSA) paired with shock. In Stage II, they received pairings of CSA and CSB with shock that blocked learning to CSB. In Stage III, a serial overlapping compound, CSB --> CSA, was followed by shock. The change in intratrial durations supported fear learning to CSB but reduced fear of CSA, revealing the operation of TD prediction errors. N-methyl- D-aspartate (NMDA) receptor antagonism prior to Stage III prevented learning, whereas opioid receptor antagonism selectively affected predictive learning. These findings support a role for TD prediction errors in fear conditioning. They suggest that NMDA receptors contribute to fear learning by acting on the product of predictive error, whereas opioid receptors contribute to predictive error. (PsycINFO Database Record (c) 2007 APA, all rights reserved).
-
Dopamine neurons share common response function for reward prediction error
Eshel, Neir; Tian, Ju; Bukwich, Michael; Uchida, Naoshige
2016-01-01
Dopamine neurons are thought to signal reward prediction error, or the difference between actual and predicted reward. How dopamine neurons jointly encode this information, however, remains unclear. One possibility is that different neurons specialize in different aspects of prediction error; another is that each neuron calculates prediction error in the same way. We recorded from optogenetically-identified dopamine neurons in the lateral ventral tegmental area (VTA) while mice performed classical conditioning tasks. Our tasks allowed us to determine the full prediction error functions of dopamine neurons and compare them to each other. We found striking homogeneity among individual dopamine neurons: their responses to both unexpected and expected rewards followed the same function, just scaled up or down. As a result, we could describe both individual and population responses using just two parameters. Such uniformity ensures robust information coding, allowing each dopamine neuron to contribute fully to the prediction error signal. PMID:26854803
-
Takahashi, Yuji K.; Langdon, Angela J.; Niv, Yael; Schoenbaum, Geoffrey
2016-01-01
Summary Dopamine neurons signal reward prediction errors. This requires accurate reward predictions. It has been suggested that the ventral striatum provides these predictions. Here we tested this hypothesis by recording from putative dopamine neurons in the VTA of rats performing a task in which prediction errors were induced by shifting reward timing or number. In controls, the neurons exhibited error signals in response to both manipulations. However, dopamine neurons in rats with ipsilateral ventral striatal lesions exhibited errors only to changes in number and failed to respond to changes in timing of reward. These results, supported by computational modeling, indicate that predictions about the temporal specificity and the number of expected rewards are dissociable, and that dopaminergic prediction-error signals rely on the ventral striatum for the former but not the latter. PMID:27292535
-
USDA-ARS?s Scientific Manuscript database
In Ensemble Kalman Filter (EnKF)-based data assimilation, the background prediction of a model is updated using observations and relative weights based on the model prediction and observation uncertainties. In practice, both model and observation uncertainties are difficult to quantify and they have...
-
Inventory implications of using sampling variances in estimation of growth model coefficients
Albert R. Stage; William R. Wykoff
2000-01-01
Variables based on stand densities or stocking have sampling errors that depend on the relation of tree size to plot size and on the spatial structure of the population, ignoring the sampling errors of such variables, which include most measures of competition used in both distance-dependent and distance-independent growth models, can bias the predictions obtained from...
-
Jiang, Wenyu; Simon, Richard
2007-12-20
This paper first provides a critical review on some existing methods for estimating the prediction error in classifying microarray data where the number of genes greatly exceeds the number of specimens. Special attention is given to the bootstrap-related methods. When the sample size n is small, we find that all the reviewed methods suffer from either substantial bias or variability. We introduce a repeated leave-one-out bootstrap (RLOOB) method that predicts for each specimen in the sample using bootstrap learning sets of size ln. We then propose an adjusted bootstrap (ABS) method that fits a learning curve to the RLOOB estimates calculated with different bootstrap learning set sizes. The ABS method is robust across the situations we investigate and provides a slightly conservative estimate for the prediction error. Even with small samples, it does not suffer from large upward bias as the leave-one-out bootstrap and the 0.632+ bootstrap, and it does not suffer from large variability as the leave-one-out cross-validation in microarray applications. Copyright (c) 2007 John Wiley & Sons, Ltd.
-
Sani, Susan Raouf Hadadi; Tabibi, Zahra; Fadardi, Javad Salehi; Stavrinos, Despina
2017-12-01
The present study explored whether aggression, emotional regulation, cognitive inhibition, and attentional bias towards emotional stimuli were related to risky driving behavior (driving errors, and driving violations). A total of 117 applicants for taxi driver positions (89% male, M age=36.59years, SD=9.39, age range 24-62years) participated in the study. Measures included the Ahwaz Aggression Inventory, the Difficulties in emotion regulation Questionnaire, the emotional Stroop task, the Go/No-go task, and the Driving Behavior Questionnaire. Correlation and regression analyses showed that aggression and emotional regulation predicted risky driving behavior. Difficulties in emotion regulation, the obstinacy and revengeful component of aggression, attentional bias toward emotional stimuli, and cognitive inhibition predicted driving errors. Aggression was the only significant predictive factor for driving violations. In conclusion, aggression and difficulties in regulating emotions may exacerbate risky driving behaviors. Deficits in cognitive inhibition and attentional bias toward negative emotional stimuli can increase driving errors. Predisposition to aggression has strong effect on making one vulnerable to violation of traffic rules and crashes. Copyright © 2017 Elsevier Ltd. All rights reserved.
-
Role-modeling and medical error disclosure: a national survey of trainees.
Martinez, William; Hickson, Gerald B; Miller, Bonnie M; Doukas, David J; Buckley, John D; Song, John; Sehgal, Niraj L; Deitz, Jennifer; Braddock, Clarence H; Lehmann, Lisa Soleymani
2014-03-01
To measure trainees' exposure to negative and positive role-modeling for responding to medical errors and to examine the association between that exposure and trainees' attitudes and behaviors regarding error disclosure. Between May 2011 and June 2012, 435 residents at two large academic medical centers and 1,187 medical students from seven U.S. medical schools received anonymous, electronic questionnaires. The questionnaire asked respondents about (1) experiences with errors, (2) training for responding to errors, (3) behaviors related to error disclosure, (4) exposure to role-modeling for responding to errors, and (5) attitudes regarding disclosure. Using multivariate regression, the authors analyzed whether frequency of exposure to negative and positive role-modeling independently predicted two primary outcomes: (1) attitudes regarding disclosure and (2) nontransparent behavior in response to a harmful error. The response rate was 55% (884/1,622). Training on how to respond to errors had the largest independent, positive effect on attitudes (standardized effect estimate, 0.32, P < .001); negative role-modeling had the largest independent, negative effect (standardized effect estimate, -0.26, P < .001). Positive role-modeling had a positive effect on attitudes (standardized effect estimate, 0.26, P < .001). Exposure to negative role-modeling was independently associated with an increased likelihood of trainees' nontransparent behavior in response to an error (OR 1.37, 95% CI 1.15-1.64; P < .001). Exposure to role-modeling predicts trainees' attitudes and behavior regarding the disclosure of harmful errors. Negative role models may be a significant impediment to disclosure among trainees.
-
Mirolli, Marco; Santucci, Vieri G; Baldassarre, Gianluca
2013-03-01
An important issue of recent neuroscientific research is to understand the functional role of the phasic release of dopamine in the striatum, and in particular its relation to reinforcement learning. The literature is split between two alternative hypotheses: one considers phasic dopamine as a reward prediction error similar to the computational TD-error, whose function is to guide an animal to maximize future rewards; the other holds that phasic dopamine is a sensory prediction error signal that lets the animal discover and acquire novel actions. In this paper we propose an original hypothesis that integrates these two contrasting positions: according to our view phasic dopamine represents a TD-like reinforcement prediction error learning signal determined by both unexpected changes in the environment (temporary, intrinsic reinforcements) and biological rewards (permanent, extrinsic reinforcements). Accordingly, dopamine plays the functional role of driving both the discovery and acquisition of novel actions and the maximization of future rewards. To validate our hypothesis we perform a series of experiments with a simulated robotic system that has to learn different skills in order to get rewards. We compare different versions of the system in which we vary the composition of the learning signal. The results show that only the system reinforced by both extrinsic and intrinsic reinforcements is able to reach high performance in sufficiently complex conditions. Copyright © 2013 Elsevier Ltd. All rights reserved.
-
Interactions of timing and prediction error learning.
Kirkpatrick, Kimberly
2014-01-01
Timing and prediction error learning have historically been treated as independent processes, but growing evidence has indicated that they are not orthogonal. Timing emerges at the earliest time point when conditioned responses are observed, and temporal variables modulate prediction error learning in both simple conditioning and cue competition paradigms. In addition, prediction errors, through changes in reward magnitude or value alter timing of behavior. Thus, there appears to be a bi-directional interaction between timing and prediction error learning. Modern theories have attempted to integrate the two processes with mixed success. A neurocomputational approach to theory development is espoused, which draws on neurobiological evidence to guide and constrain computational model development. Heuristics for future model development are presented with the goal of sparking new approaches to theory development in the timing and prediction error fields. Copyright © 2013 Elsevier B.V. All rights reserved.
-
Diuk, Carlos; Tsai, Karin; Wallis, Jonathan; Botvinick, Matthew; Niv, Yael
2013-03-27
Studies suggest that dopaminergic neurons report a unitary, global reward prediction error signal. However, learning in complex real-life tasks, in particular tasks that show hierarchical structure, requires multiple prediction errors that may coincide in time. We used functional neuroimaging to measure prediction error signals in humans performing such a hierarchical task involving simultaneous, uncorrelated prediction errors. Analysis of signals in a priori anatomical regions of interest in the ventral striatum and the ventral tegmental area indeed evidenced two simultaneous, but separable, prediction error signals corresponding to the two levels of hierarchy in the task. This result suggests that suitably designed tasks may reveal a more intricate pattern of firing in dopaminergic neurons. Moreover, the need for downstream separation of these signals implies possible limitations on the number of different task levels that we can learn about simultaneously.
-
Jones, J.W.; Jarnagin, T.
2009-01-01
Given the relatively high cost of mapping impervious surfaces at regional scales, substantial effort is being expended in the development of moderate-resolution, satellite-based methods for estimating impervious surface area (ISA). To rigorously assess the accuracy of these data products high quality, independently derived validation data are needed. High-resolution data were collected across a gradient of development within the Mid-Atlantic region to assess the accuracy of National Land Cover Data (NLCD) Landsat-based ISA estimates. Absolute error (satellite predicted area - "reference area") and relative error [satellite (predicted area - "reference area")/ "reference area"] were calculated for each of 240 sample regions that are each more than 15 Landsat pixels on a side. The ability to compile and examine ancillary data in a geographic information system environment provided for evaluation of both validation and NLCD data and afforded efficient exploration of observed errors. In a minority of cases, errors could be explained by temporal discontinuities between the date of satellite image capture and validation source data in rapidly changing places. In others, errors were created by vegetation cover over impervious surfaces and by other factors that bias the satellite processing algorithms. On average in the Mid-Atlantic region, the NLCD product underestimates ISA by approximately 5%. While the error range varies between 2 and 8%, this underestimation occurs regardless of development intensity. Through such analyses the errors, strengths, and weaknesses of particular satellite products can be explored to suggest appropriate uses for regional, satellite-based data in rapidly developing areas of environmental significance. ?? 2009 ASCE.
-
Wei, Wenjuan; Xiong, Jianyin; Zhang, Yinping
2013-01-01
Mass transfer models are useful in predicting the emissions of volatile organic compounds (VOCs) and formaldehyde from building materials in indoor environments. They are also useful for human exposure evaluation and in sustainable building design. The measurement errors in the emission characteristic parameters in these mass transfer models, i.e., the initial emittable concentration (C 0), the diffusion coefficient (D), and the partition coefficient (K), can result in errors in predicting indoor VOC and formaldehyde concentrations. These errors have not yet been quantitatively well analyzed in the literature. This paper addresses this by using modelling to assess these errors for some typical building conditions. The error in C 0, as measured in environmental chambers and applied to a reference living room in Beijing, has the largest influence on the model prediction error in indoor VOC and formaldehyde concentration, while the error in K has the least effect. A correlation between the errors in D, K, and C 0 and the error in the indoor VOC and formaldehyde concentration prediction is then derived for engineering applications. In addition, the influence of temperature on the model prediction of emissions is investigated. It shows the impact of temperature fluctuations on the prediction errors in indoor VOC and formaldehyde concentrations to be less than 7% at 23±0.5°C and less than 30% at 23±2°C. PMID:24312497
-
Quan, Guo-zheng; Yu, Chun-tang; Liu, Ying-ying; Xia, Yu-feng
2014-01-01
The stress-strain data of 20MnNiMo alloy were collected from a series of hot compressions on Gleeble-1500 thermal-mechanical simulator in the temperature range of 1173 ∼ 1473 K and strain rate range of 0.01 ∼ 10 s(-1). Based on the experimental data, the improved Arrhenius-type constitutive model and the artificial neural network (ANN) model were established to predict the high temperature flow stress of as-cast 20MnNiMo alloy. The accuracy and reliability of the improved Arrhenius-type model and the trained ANN model were further evaluated in terms of the correlation coefficient (R), the average absolute relative error (AARE), and the relative error (η). For the former, R and AARE were found to be 0.9954 and 5.26%, respectively, while, for the latter, 0.9997 and 1.02%, respectively. The relative errors (η) of the improved Arrhenius-type model and the ANN model were, respectively, in the range of -39.99% ∼ 35.05% and -3.77% ∼ 16.74%. As for the former, only 16.3% of the test data set possesses η-values within ± 1%, while, as for the latter, more than 79% possesses. The results indicate that the ANN model presents a higher predictable ability than the improved Arrhenius-type constitutive model.
-
Developing stochastic model of thrust and flight dynamics for small UAVs
NASA Astrophysics Data System (ADS)
Tjhai, Chandra
This thesis presents a stochastic thrust model and aerodynamic model for small propeller driven UAVs whose power plant is a small electric motor. First a model which relates thrust generated by a small propeller driven electric motor as a function of throttle setting and commanded engine RPM is developed. A perturbation of this model is then used to relate the uncertainty in throttle and engine RPM commanded to the error in the predicted thrust. Such a stochastic model is indispensable in the design of state estimation and control systems for UAVs where the performance requirements of the systems are specied in stochastic terms. It is shown that thrust prediction models for small UAVs are not a simple, explicit functions relating throttle input and RPM command to thrust generated. Rather they are non-linear, iterative procedures which depend on a geometric description of the propeller and mathematical model of the motor. A detailed derivation of the iterative procedure is presented and the impact of errors which arise from inaccurate propeller and motor descriptions are discussed. Validation results from a series of wind tunnel tests are presented. The results show a favorable statistical agreement between the thrust uncertainty predicted by the model and the errors measured in the wind tunnel. The uncertainty model of aircraft aerodynamic coefficients developed based on wind tunnel experiment will be discussed at the end of this thesis.
-
Uncertainty Analysis in Humidity Measurements by the Psychrometer Method
Chen, Jiunyuan; Chen, Chiachung
2017-01-01
The most common and cheap indirect technique to measure relative humidity is by using psychrometer based on a dry and a wet temperature sensor. In this study, the measurement uncertainty of relative humidity was evaluated by this indirect method with some empirical equations for calculating relative humidity. Among the six equations tested, the Penman equation had the best predictive ability for the dry bulb temperature range of 15–50 °C. At a fixed dry bulb temperature, an increase in the wet bulb depression increased the error. A new equation for the psychrometer constant was established by regression analysis. This equation can be computed by using a calculator. The average predictive error of relative humidity was <0.1% by this new equation. The measurement uncertainty of the relative humidity affected by the accuracy of dry and wet bulb temperature and the numeric values of measurement uncertainty were evaluated for various conditions. The uncertainty of wet bulb temperature was the main factor on the RH measurement uncertainty. PMID:28216599
-
Uncertainty Analysis in Humidity Measurements by the Psychrometer Method.
Chen, Jiunyuan; Chen, Chiachung
2017-02-14
The most common and cheap indirect technique to measure relative humidity is by using psychrometer based on a dry and a wet temperature sensor. In this study, the measurement uncertainty of relative humidity was evaluated by this indirect method with some empirical equations for calculating relative humidity. Among the six equations tested, the Penman equation had the best predictive ability for the dry bulb temperature range of 15-50 °C. At a fixed dry bulb temperature, an increase in the wet bulb depression increased the error. A new equation for the psychrometer constant was established by regression analysis. This equation can be computed by using a calculator. The average predictive error of relative humidity was <0.1% by this new equation. The measurement uncertainty of the relative humidity affected by the accuracy of dry and wet bulb temperature and the numeric values of measurement uncertainty were evaluated for various conditions. The uncertainty of wet bulb temperature was the main factor on the RH measurement uncertainty.
-
Ben Natan, Merav; Sharon, Ira; Mahajna, Marlen; Mahajna, Sara
2017-11-01
Medication errors are common among nursing students. Nonetheless, these errors are often underreported. To examine factors related to nursing students' intention to report medication errors, using the Theory of Planned Behavior, and to examine whether the theory is useful in predicting students' intention to report errors. This study has a descriptive cross-sectional design. Study population was recruited in a university and a large nursing school in central and northern Israel. A convenience sample of 250 nursing students took part in the study. The students completed a self-report questionnaire, based on the Theory of Planned Behavior. The findings indicate that students' intention to report medication errors was high. The Theory of Planned Behavior constructs explained 38% of variance in students' intention to report medication errors. The constructs of behavioral beliefs, subjective norms, and perceived behavioral control were found as affecting this intention, while the most significant factor was behavioral beliefs. The findings also reveal that students' fear of the reaction to disclosure of the error from superiors and colleagues may impede them from reporting the error. Understanding factors related to reporting medication errors is crucial to designing interventions that foster error reporting. Copyright © 2017 Elsevier Ltd. All rights reserved.
-
NASA Technical Reports Server (NTRS)
Miller, J. M.
1980-01-01
ATMOS is a Fourier transform spectrometer to measure atmospheric trace molecules over a spectral range of 2-16 microns. Assessment of the system performance of ATMOS includes evaluations of optical system errors induced by thermal and structural effects. In order to assess the optical system errors induced from thermal and structural effects, error budgets are assembled during system engineering tasks and line of sight and wavefront deformations predictions (using operational thermal and vibration environments and computer models) are subsequently compared to the error budgets. This paper discusses the thermal/structural error budgets, modelling and analysis methods used to predict thermal/structural induced errors and the comparisons that show that predictions are within the error budgets.
-
Thomas, D C; Bowman, J D; Jiang, L; Jiang, F; Peters, J M
1999-10-01
Case-control data on childhood leukemia in Los Angeles County were reanalyzed with residential magnetic fields predicted from the wiring configurations of nearby transmission and distribution lines. As described in a companion paper, the 24-h means of the magnetic field's magnitude in subjects' homes were predicted by a physically based regression model that had been fitted to 24-h measurements and wiring data. In addition, magnetic field exposures were adjusted for the most likely form of exposure assessment errors: classic errors for the 24-h measurements and Berkson errors for the predictions from wire configurations. Although the measured fields had no association with childhood leukemia (P for trend=.88), the risks were significant for predicted magnetic fields above 1.25 mG (odds ratio=2.00, 95% confidence interval=1.03-3.89), and a significant dose-response was seen (P for trend=.02). When exposures were determined by a combination of predictions and measurements that corrects for errors, the odds ratio (odd ratio=2.19, 95% confidence interval=1.12-4.31) and the trend (p =.007) showed somewhat greater significance. These findings support the hypothesis that magnetic fields from electrical lines are causally related to childhood leukemia but that this association has been inconsistent among epidemiologic studies due to different types of exposure assessment error. In these data, the leukemia risks from a child's residential magnetic field exposure appears to be better assessed by wire configurations than by 24-h area measurements. However, the predicted fields only partially account for the effect of the Wertheimer-Leeper wire code in a multivariate analysis and do not completely explain why these wire codes have been so often associated with childhood leukemia. The most plausible explanation for our findings is that the causal factor is another magnetic field exposure metric correlated to both wire code and the field's time-averaged magnitude. Copyright 1999 Wiley-Liss, Inc.
-
The Impact of Truth Surrogate Variance on Quality Assessment/Assurance in Wind Tunnel Testing
NASA Technical Reports Server (NTRS)
DeLoach, Richard
2016-01-01
Minimum data volume requirements for wind tunnel testing are reviewed and shown to depend on error tolerance, response model complexity, random error variance in the measurement environment, and maximum acceptable levels of inference error risk. Distinctions are made between such related concepts as quality assurance and quality assessment in response surface modeling, as well as between precision and accuracy. Earlier research on the scaling of wind tunnel tests is extended to account for variance in the truth surrogates used at confirmation sites in the design space to validate proposed response models. A model adequacy metric is presented that represents the fraction of the design space within which model predictions can be expected to satisfy prescribed quality specifications. The impact of inference error on the assessment of response model residuals is reviewed. The number of sites where reasonably well-fitted response models actually predict inadequately is shown to be considerably less than the number of sites where residuals are out of tolerance. The significance of such inference error effects on common response model assessment strategies is examined.
-
Interpolating precipitation and its relation to runoff and non-point source pollution.
Chang, Chia-Ling; Lo, Shang-Lien; Yu, Shaw-L
2005-01-01
When rainfall spatially varies, complete rainfall data for each region with different rainfall characteristics are very important. Numerous interpolation methods have been developed for estimating unknown spatial characteristics. However, no interpolation method is suitable for all circumstances. In this study, several methods, including the arithmetic average method, the Thiessen Polygons method, the traditional inverse distance method, and the modified inverse distance method, were used to interpolate precipitation. The modified inverse distance method considers not only horizontal distances but also differences between the elevations of the region with no rainfall records and of its surrounding rainfall stations. The results show that when the spatial variation of rainfall is strong, choosing a suitable interpolation method is very important. If the rainfall is uniform, the precipitation estimated using any interpolation method would be quite close to the actual precipitation. When rainfall is heavy in locations with high elevation, the rainfall changes with the elevation. In this situation, the modified inverse distance method is much more effective than any other method discussed herein for estimating the rainfall input for WinVAST to estimate runoff and non-point source pollution (NPSP). When the spatial variation of rainfall is random, regardless of the interpolation method used to yield rainfall input, the estimation errors of runoff and NPSP are large. Moreover, the relationship between the relative error of the predicted runoff and predicted pollutant loading of SS is high. However, the pollutant concentration is affected by both runoff and pollutant export, so the relationship between the relative error of the predicted runoff and the predicted pollutant concentration of SS may be unstable.
-
Evaluating and Predicting Patient Safety for Medical Devices With Integral Information Technology
2005-01-01
have the potential to become solid tools for manufacturers, purchasers, and consumers to evaluate patient safety issues in various health related...323 Evaluating and Predicting Patient Safety for Medical Devices with Integral Information Technology Jiajie Zhang, Vimla L. Patel, Todd R...errors are due to inappropriate designs for user interactions, rather than mechanical failures. Evaluating and predicting patient safety in medical
-
Tsai, Karin; Wallis, Jonathan; Botvinick, Matthew
2013-01-01
Studies suggest that dopaminergic neurons report a unitary, global reward prediction error signal. However, learning in complex real-life tasks, in particular tasks that show hierarchical structure, requires multiple prediction errors that may coincide in time. We used functional neuroimaging to measure prediction error signals in humans performing such a hierarchical task involving simultaneous, uncorrelated prediction errors. Analysis of signals in a priori anatomical regions of interest in the ventral striatum and the ventral tegmental area indeed evidenced two simultaneous, but separable, prediction error signals corresponding to the two levels of hierarchy in the task. This result suggests that suitably designed tasks may reveal a more intricate pattern of firing in dopaminergic neurons. Moreover, the need for downstream separation of these signals implies possible limitations on the number of different task levels that we can learn about simultaneously. PMID:23536092
-
Neural evidence for description dependent reward processing in the framing effect.
Yu, Rongjun; Zhang, Ping
2014-01-01
Human decision making can be influenced by emotionally valenced contexts, known as the framing effect. We used event-related brain potentials to investigate how framing influences the encoding of reward. We found that the feedback related negativity (FRN), which indexes the "worse than expected" negative prediction error in the anterior cingulate cortex (ACC), was more negative for the negative frame than for the positive frame in the win domain. Consistent with previous findings that the FRN is not sensitive to "better than expected" positive prediction error, the FRN did not differentiate the positive and negative frame in the loss domain. Our results provide neural evidence that the description invariance principle which states that reward representation and decision making are not influenced by how options are presented is violated in the framing effect.
-
Characterization of errors in a coupled snow hydrology-microwave emission model
Andreadis, K.M.; Liang, D.; Tsang, L.; Lettenmaier, D.P.; Josberger, E.G.
2008-01-01
Traditional approaches to the direct estimation of snow properties from passive microwave remote sensing have been plagued by limitations such as the tendency of estimates to saturate for moderately deep snowpacks and the effects of mixed land cover within remotely sensed pixels. An alternative approach is to assimilate satellite microwave emission observations directly, which requires embedding an accurate microwave emissions model into a hydrologic prediction scheme, as well as quantitative information of model and observation errors. In this study a coupled snow hydrology [Variable Infiltration Capacity (VIC)] and microwave emission [Dense Media Radiative Transfer (DMRT)] model are evaluated using multiscale brightness temperature (TB) measurements from the Cold Land Processes Experiment (CLPX). The ability of VIC to reproduce snowpack properties is shown with the use of snow pit measurements, while TB model predictions are evaluated through comparison with Ground-Based Microwave Radiometer (GBMR), air-craft [Polarimetric Scanning Radiometer (PSR)], and satellite [Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E)] TB measurements. Limitations of the model at the point scale were not as evident when comparing areal estimates. The coupled model was able to reproduce the TB spatial patterns observed by PSR in two of three sites. However, this was mostly due to the presence of relatively dense forest cover. An interesting result occurs when examining the spatial scaling behavior of the higher-resolution errors; the satellite-scale error is well approximated by the mode of the (spatial) histogram of errors at the smaller scale. In addition, TB prediction errors were almost invariant when aggregated to the satellite scale, while forest-cover fractions greater than 30% had a significant effect on TB predictions. ?? 2008 American Meteorological Society.
-
Adaptive reduction of constitutive model-form error using a posteriori error estimation techniques
DOE Office of Scientific and Technical Information (OSTI.GOV)
Bishop, Joseph E.; Brown, Judith Alice
In engineering practice, models are typically kept as simple as possible for ease of setup and use, computational efficiency, maintenance, and overall reduced complexity to achieve robustness. In solid mechanics, a simple and efficient constitutive model may be favored over one that is more predictive, but is difficult to parameterize, is computationally expensive, or is simply not available within a simulation tool. In order to quantify the modeling error due to the choice of a relatively simple and less predictive constitutive model, we adopt the use of a posteriori model-form error-estimation techniques. Based on local error indicators in the energymore » norm, an algorithm is developed for reducing the modeling error by spatially adapting the material parameters in the simpler constitutive model. The resulting material parameters are not material properties per se, but depend on the given boundary-value problem. As a first step to the more general nonlinear case, we focus here on linear elasticity in which the “complex” constitutive model is general anisotropic elasticity and the chosen simpler model is isotropic elasticity. As a result, the algorithm for adaptive error reduction is demonstrated using two examples: (1) A transversely-isotropic plate with hole subjected to tension, and (2) a transversely-isotropic tube with two side holes subjected to torsion.« less
-
Adaptive reduction of constitutive model-form error using a posteriori error estimation techniques
Bishop, Joseph E.; Brown, Judith Alice
2018-06-15
In engineering practice, models are typically kept as simple as possible for ease of setup and use, computational efficiency, maintenance, and overall reduced complexity to achieve robustness. In solid mechanics, a simple and efficient constitutive model may be favored over one that is more predictive, but is difficult to parameterize, is computationally expensive, or is simply not available within a simulation tool. In order to quantify the modeling error due to the choice of a relatively simple and less predictive constitutive model, we adopt the use of a posteriori model-form error-estimation techniques. Based on local error indicators in the energymore » norm, an algorithm is developed for reducing the modeling error by spatially adapting the material parameters in the simpler constitutive model. The resulting material parameters are not material properties per se, but depend on the given boundary-value problem. As a first step to the more general nonlinear case, we focus here on linear elasticity in which the “complex” constitutive model is general anisotropic elasticity and the chosen simpler model is isotropic elasticity. As a result, the algorithm for adaptive error reduction is demonstrated using two examples: (1) A transversely-isotropic plate with hole subjected to tension, and (2) a transversely-isotropic tube with two side holes subjected to torsion.« less
-
NASA Astrophysics Data System (ADS)
Baker, N. L.; Langland, R.
2016-12-01
Variations in Earth rotation are measured by comparing a time based on Earth's variable rotation rate about its axis to a time standard based on an internationally coordinated ensemble of atomic clocks that provide a uniform time scale. The variability of Earth's rotation is partly due to the changes in angular momentum that occur in the atmosphere and ocean as weather patterns and ocean features develop, propagate, and dissipate. The NAVGEM Effective Atmospheric Angular Momentum Functions (EAAMF) and their predictions are computed following Barnes et al. (1983), and provided to the U.S. Naval Observatory daily. These along with similar data from the NOAA GFS model are used to calculate and predict the Earth orientation parameters (Stamatakos et al., 2016). The Navy's high-resolution global weather prediction system consists of the Navy Global Environmental Model (NAVGEM; Hogan et al., 2014) and a hybrid four-dimensional variational data assimilation system (4DVar) (Kuhl et al., 2013). An important component of NAVGEM is the Forecast Sensitivity Observation Impact (FSOI). FSOI is a mathematical method to quantify the contribution of individual observations or sets of observations to the reduction in the 24-hr forecast error (Langland and Baker, 2004). The FSOI allows for dynamic monitoring of the relative quality and value of the observations assimilated by NAVGEM, and the relative ability of the data assimilation system to effectively use the observation information to generate an improved forecast. For this study, along with the FSOI based on the global moist energy error norm, we computed the FSOI using an error norm based on the Effective Angular Momentum Functions. This modification allowed us to assess which observations were most beneficial in reducing the 24-hr forecast error for the atmospheric angular momentum.
-
Fatigue Life Prediction of Metallic Materials Based on the Combined Nonlinear Ultrasonic Parameter
NASA Astrophysics Data System (ADS)
Zhang, Yuhua; Li, Xinxin; Wu, Zhenyong; Huang, Zhenfeng; Mao, Hanling
2017-08-01
The fatigue life prediction of metallic materials is always a tough problem that needs to be solved in the mechanical engineering field because it is very important for the secure service of mechanical components. In this paper, a combined nonlinear ultrasonic parameter based on the collinear wave mixing technique is applied for fatigue life prediction of a metallic material. Sweep experiments are first conducted to explore the influence of driving frequency on the interaction of two driving signals and the fatigue damage of specimens, and the amplitudes of sidebands at the difference frequency and sum frequency are tracked when the driving frequency changes. Then, collinear wave mixing tests are carried out on a pair of cylindrically notched specimens with different fatigue damage to explore the relationship between the fatigue damage and the relative nonlinear parameters. The experimental results show when the fatigue degree is below 65% the relative nonlinear parameter increases quickly, and the growth rate is approximately 130%. If the fatigue degree is above 65%, the increase in the relative nonlinear parameter is slow, which has a close relationship with the microstructure evolution of specimens. A combined nonlinear ultrasonic parameter is proposed to highlight the relationship of the relative nonlinear parameter and fatigue degree of specimens; the fatigue life prediction model is built based on the relationship, and the prediction error is below 3%, which is below the prediction error based on the relative nonlinear parameters at the difference and sum frequencies. Therefore, the combined nonlinear ultrasonic parameter using the collinear wave mixing method can effectively estimate the fatigue degree of specimens, which provides a fast and convenient method for fatigue life prediction.
-
David W. MacFarlane; Neil R. Ver Planck
2012-01-01
Data from hardwood trees in Michigan were analyzed to investigate how differences in whole-tree form and wood density between trees of different stem diameter relate to residual error in standard-type biomass equations. The results suggested that whole-tree wood density, measured at breast height, explained a significant proportion of residual error in standard-type...
-
Effects of modeled tropical sea surface temperature variability on coral reef bleaching predictions
NASA Astrophysics Data System (ADS)
Van Hooidonk, R. J.
2011-12-01
Future widespread coral bleaching and subsequent mortality has been projected with sea surface temperature (SST) data from global, coupled ocean-atmosphere general circulation models (GCMs). While these models possess fidelity in reproducing many aspects of climate, they vary in their ability to correctly capture such parameters as the tropical ocean seasonal cycle and El Niño Southern Oscillation (ENSO) variability. These model weaknesses likely reduce the skill of coral bleaching predictions, but little attention has been paid to the important issue of understanding potential errors and biases, the interaction of these biases with trends and their propagation in predictions. To analyze the relative importance of various types of model errors and biases on coral reef bleaching predictive skill, various intra- and inter-annual frequency bands of observed SSTs were replaced with those frequencies from GCMs 20th century simulations to be included in the Intergovernmental Panel on Climate Change (IPCC) 5th assessment report. Subsequent thermal stress was calculated and predictions of bleaching were made. These predictions were compared with observations of coral bleaching in the period 1982-2007 to calculate skill using an objective measure of forecast quality, the Peirce Skill Score (PSS). This methodology will identify frequency bands that are important to predicting coral bleaching and it will highlight deficiencies in these bands in models. The methodology we describe can be used to improve future climate model derived predictions of coral reef bleaching and it can be used to better characterize the errors and uncertainty in predictions.
-
NASA Technical Reports Server (NTRS)
Consiglio, Maria C.; Hoadley, Sherwood T.; Allen, B. Danette
2009-01-01
Wind prediction errors are known to affect the performance of automated air traffic management tools that rely on aircraft trajectory predictions. In particular, automated separation assurance tools, planned as part of the NextGen concept of operations, must be designed to account and compensate for the impact of wind prediction errors and other system uncertainties. In this paper we describe a high fidelity batch simulation study designed to estimate the separation distance required to compensate for the effects of wind-prediction errors throughout increasing traffic density on an airborne separation assistance system. These experimental runs are part of the Safety Performance of Airborne Separation experiment suite that examines the safety implications of prediction errors and system uncertainties on airborne separation assurance systems. In this experiment, wind-prediction errors were varied between zero and forty knots while traffic density was increased several times current traffic levels. In order to accurately measure the full unmitigated impact of wind-prediction errors, no uncertainty buffers were added to the separation minima. The goal of the study was to measure the impact of wind-prediction errors in order to estimate the additional separation buffers necessary to preserve separation and to provide a baseline for future analyses. Buffer estimations from this study will be used and verified in upcoming safety evaluation experiments under similar simulation conditions. Results suggest that the strategic airborne separation functions exercised in this experiment can sustain wind prediction errors up to 40kts at current day air traffic density with no additional separation distance buffer and at eight times the current day with no more than a 60% increase in separation distance buffer.
-
Sengupta, Auntora; McNally, Gavan P
2014-01-01
Fear learning occurs in response to positive prediction error, when the expected outcome of a conditioning trial exceeds that predicted by the conditioned stimuli present. This role for error in Pavlovian association formation is best exemplified by the phenomenon of associative blocking, whereby prior fear conditioning of conditioned stimulus (CS) A is able to prevent learning to CSB when they are conditioned in compound. The midline and intralaminar thalamic nuclei (MIT) are well-placed to contribute to fear prediction error because they receive extensive projections from the midbrain periaqueductal gray-which has a key role in fear prediction error-and project extensively to prefrontal cortex and amygdala. Here we used an associative blocking design to study the role of MIT in fear learning. In Stage I rats were trained to fear CSA via pairings with shock. In Stage II rats received compound fear conditioning of CSAB paired with shock. On test, rats that received Stage I training expressed less fear to CSB relative to control rats that did not receive this training. Microinjection of bupivacaine into MIT prior to Stage II training had no effect on the expression of fear during Stage II and had no effect on fear learning in controls, but prevented associative blocking and so enabled fear learning to CSB. These results show an important role for MIT in predictive fear learning and are discussed with reference to previous findings implicating the midline and posterior intralaminar thalamus in fear learning and fear responding.
-
Artificial neural network implementation of a near-ideal error prediction controller
NASA Technical Reports Server (NTRS)
Mcvey, Eugene S.; Taylor, Lynore Denise
1992-01-01
A theory has been developed at the University of Virginia which explains the effects of including an ideal predictor in the forward loop of a linear error-sampled system. It has been shown that the presence of this ideal predictor tends to stabilize the class of systems considered. A prediction controller is merely a system which anticipates a signal or part of a signal before it actually occurs. It is understood that an exact prediction controller is physically unrealizable. However, in systems where the input tends to be repetitive or limited, (i.e., not random) near ideal prediction is possible. In order for the controller to act as a stability compensator, the predictor must be designed in a way that allows it to learn the expected error response of the system. In this way, an unstable system will become stable by including the predicted error in the system transfer function. Previous and current prediction controller include pattern recognition developments and fast-time simulation which are applicable to the analysis of linear sampled data type systems. The use of pattern recognition techniques, along with a template matching scheme, has been proposed as one realizable type of near-ideal prediction. Since many, if not most, systems are repeatedly subjected to similar inputs, it was proposed that an adaptive mechanism be used to 'learn' the correct predicted error response. Once the system has learned the response of all the expected inputs, it is necessary only to recognize the type of input with a template matching mechanism and then to use the correct predicted error to drive the system. Suggested here is an alternate approach to the realization of a near-ideal error prediction controller, one designed using Neural Networks. Neural Networks are good at recognizing patterns such as system responses, and the back-propagation architecture makes use of a template matching scheme. In using this type of error prediction, it is assumed that the system error responses be known for a particular input and modeled plant. These responses are used in the error prediction controller. An analysis was done on the general dynamic behavior that results from including a digital error predictor in a control loop and these were compared to those including the near-ideal Neural Network error predictor. This analysis was done for a second and third order system.
-
Lievaart, Marien; van der Veen, Frederik M; Huijding, Jorg; Naeije, Lilian; Hovens, Johannes E; Franken, Ingmar H A
2016-01-01
Effortful control is considered to be an important factor in explaining individual differences in trait anger. In the current study, we sought to investigate the relation between anger-primed effortful control (i.e., inhibitory control and error-processing) and trait anger using an affective Go/NoGo task. Individuals low (LTA; n=45) and high (HTA; n=49) on trait anger were selected for this study. Behavioral performance (accuracy) and Event-Related Potentials (ERPs; i.e., N2, P3, ERN, Pe) were compared between both groups. Contrary to our predictions, we found no group differences regarding inhibitory control. That is, HTA and LTA individuals made comparable numbers of commission errors on NoGo trials and no significant differences were found on the N2 and P3 amplitudes. With respect to error-processing, we found reduced Pe amplitudes following errors in HTA individuals as compared to LTA individuals, whereas the ERN amplitudes were comparable for both groups. These results indicate that high trait anger individuals show deficits in later stages of error-processing, which may explain the continuation of impulsive behaviors in HTA individuals despite their negative consequences. Copyright © 2015 Elsevier B.V. All rights reserved.
-
[Study on the ARIMA model application to predict echinococcosis cases in China].
En-Li, Tan; Zheng-Feng, Wang; Wen-Ce, Zhou; Shi-Zhu, Li; Yan, Lu; Lin, Ai; Yu-Chun, Cai; Xue-Jiao, Teng; Shun-Xian, Zhang; Zhi-Sheng, Dang; Chun-Li, Yang; Jia-Xu, Chen; Wei, Hu; Xiao-Nong, Zhou; Li-Guang, Tian
2018-02-26
To predict the monthly reported echinococcosis cases in China with the autoregressive integrated moving average (ARIMA) model, so as to provide a reference for prevention and control of echinococcosis. SPSS 24.0 software was used to construct the ARIMA models based on the monthly reported echinococcosis cases of time series from 2007 to 2015 and 2007 to 2014, respectively, and the accuracies of the two ARIMA models were compared. The model based on the data of the monthly reported cases of echinococcosis in China from 2007 to 2015 was ARIMA (1, 0, 0) (1, 1, 0) 12 , the relative error among reported cases and predicted cases was -13.97%, AR (1) = 0.367 ( t = 3.816, P < 0.001), SAR (1) = -0.328 ( t = -3.361, P = 0.001), and Ljung-Box Q = 14.119 ( df = 16, P = 0.590) . The model based on the data of the monthly reported cases of echinococcosis in China from 2007 to 2014 was ARIMA (1, 0, 0) (1, 0, 1) 12 , the relative error among reported cases and predicted cases was 0.56%, AR (1) = 0.413 ( t = 4.244, P < 0.001), SAR (1) = 0.809 ( t = 9.584, P < 0.001), SMA (1) = 0.356 ( t = 2.278, P = 0.025), and Ljung-Box Q = 18.924 ( df = 15, P = 0.217). The different time series may have different ARIMA models as for the same infectious diseases. It is needed to be further verified that the more data are accumulated, the shorter time of predication is, and the smaller the average of the relative error is. The establishment and prediction of an ARIMA model is a dynamic process that needs to be adjusted and optimized continuously according to the accumulated data, meantime, we should give full consideration to the intensity of the work related to infectious diseases reported (such as disease census and special investigation).
-
1980-02-01
formula for predictinq the number of errors during system testing. The equation he presents is B V/ ECRIT where B is the number of 19 ’R , errors...expected, V is the volume, and ECRIT is "the mean number of elementary discriminations between potential errors in programming" (p. 85). E CRIT can also...prediction of delivered bugs is: "V VX 2 B = V/ ECRIT -3- 13,824 2.3 McCabe’s Complexity Metric Thomas McCabe (1976) defined complexity in relation to
-
Whittle, Rebecca; Peat, George; Belcher, John; Collins, Gary S; Riley, Richard D
2018-05-18
Measurement error in predictor variables may threaten the validity of clinical prediction models. We sought to evaluate the possible extent of the problem. A secondary objective was to examine whether predictors are measured at the intended moment of model use. A systematic search of Medline was used to identify a sample of articles reporting the development of a clinical prediction model published in 2015. After screening according to a predefined inclusion criteria, information on predictors, strategies to control for measurement error and intended moment of model use were extracted. Susceptibility to measurement error for each predictor was classified into low and high risk. Thirty-three studies were reviewed, including 151 different predictors in the final prediction models. Fifty-one (33.7%) predictors were categorised as high risk of error, however this was not accounted for in the model development. Only 8 (24.2%) studies explicitly stated the intended moment of model use and when the predictors were measured. Reporting of measurement error and intended moment of model use is poor in prediction model studies. There is a need to identify circumstances where ignoring measurement error in prediction models is consequential and whether accounting for the error will improve the predictions. Copyright © 2018. Published by Elsevier Inc.
-
Parental Cognitive Errors Mediate Parental Psychopathology and Ratings of Child Inattention.
Haack, Lauren M; Jiang, Yuan; Delucchi, Kevin; Kaiser, Nina; McBurnett, Keith; Hinshaw, Stephen; Pfiffner, Linda
2017-09-01
We investigate the Depression-Distortion Hypothesis in a sample of 199 school-aged children with ADHD-Predominantly Inattentive presentation (ADHD-I) by examining relations and cross-sectional mediational pathways between parental characteristics (i.e., levels of parental depressive and ADHD symptoms) and parental ratings of child problem behavior (inattention, sluggish cognitive tempo, and functional impairment) via parental cognitive errors. Results demonstrated a positive association between parental factors and parental ratings of inattention, as well as a mediational pathway between parental depressive and ADHD symptoms and parental ratings of inattention via parental cognitive errors. Specifically, higher levels of parental depressive and ADHD symptoms predicted higher levels of cognitive errors, which in turn predicted higher parental ratings of inattention. Findings provide evidence for core tenets of the Depression-Distortion Hypothesis, which state that parents with high rates of psychopathology hold negative schemas for their child's behavior and subsequently, report their child's behavior as more severe. © 2016 Family Process Institute.
-
Maidhof, Clemens; Rieger, Martina; Prinz, Wolfgang; Koelsch, Stefan
2009-01-01
Background One central question in the context of motor control and action monitoring is at what point in time errors can be detected. Previous electrophysiological studies investigating this issue focused on brain potentials elicited after erroneous responses, mainly in simple speeded response tasks. In the present study, we investigated brain potentials before the commission of errors in a natural and complex situation. Methodology/Principal Findings Expert pianists bimanually played scales and patterns while the electroencephalogram (EEG) was recorded. Event-related potentials (ERPs) were computed for correct and incorrect performances. Results revealed differences already 100 ms prior to the onset of a note (i.e., prior to auditory feedback). We further observed that erroneous keystrokes were delayed in time and pressed more slowly. Conclusions Our data reveal neural mechanisms in musicians that are able to detect errors prior to the execution of erroneous movements. The underlying mechanism probably relies on predictive control processes that compare the predicted outcome of an action with the action goal. PMID:19337379
-
Lane, Sandi J; Troyer, Jennifer L; Dienemann, Jacqueline A; Laditka, Sarah B; Blanchette, Christopher M
2014-01-01
Older adults are at greatest risk of medication errors during the transition period of the first 7 days after admission and readmission to a skilled nursing facility (SNF). The aim of this study was to evaluate structure- and process-related factors that contribute to medication errors and harm during transition periods at a SNF. Data for medication errors and potential medication errors during the 7-day transition period for residents entering North Carolina SNFs were from the Medication Error Quality Initiative-Individual Error database from October 2006 to September 2007. The impact of SNF structure and process measures on the number of reported medication errors and harm from errors were examined using bivariate and multivariate model methods. A total of 138 SNFs reported 581 transition period medication errors; 73 (12.6%) caused harm. Chain affiliation was associated with a reduction in the volume of errors during the transition period. One third of all reported transition errors occurred during the medication administration phase of the medication use process, where dose omissions were the most common type of error; however, dose omissions caused harm less often than wrong-dose errors did. Prescribing errors were much less common than administration errors but were much more likely to cause harm. Both structure and process measures of quality were related to the volume of medication errors.However, process quality measures may play a more important role in predicting harm from errors during the transition of a resident into an SNF. Medication errors during transition could be reduced by improving both prescribing processes and transcription and documentation of orders.
-
Bakic, Jasmina; Pourtois, Gilles; Jepma, Marieke; Duprat, Romain; De Raedt, Rudi; Baeken, Chris
2017-01-01
Major depressive disorder (MDD) creates debilitating effects on a wide range of cognitive functions, including reinforcement learning (RL). In this study, we sought to assess whether reward processing as such, or alternatively the complex interplay between motivation and reward might potentially account for the abnormal reward-based learning in MDD. A total of 35 treatment resistant MDD patients and 44 age matched healthy controls (HCs) performed a standard probabilistic learning task. RL was titrated using behavioral, computational modeling and event-related brain potentials (ERPs) data. MDD patients showed comparable learning rate compared to HCs. However, they showed decreased lose-shift responses as well as blunted subjective evaluations of the reinforcers used during the task, relative to HCs. Moreover, MDD patients showed normal internal (at the level of error-related negativity, ERN) but abnormal external (at the level of feedback-related negativity, FRN) reward prediction error (RPE) signals during RL, selectively when additional efforts had to be made to establish learning. Collectively, these results lend support to the assumption that MDD does not impair reward processing per se during RL. Instead, it seems to alter the processing of the emotional value of (external) reinforcers during RL, when additional intrinsic motivational processes have to be engaged. © 2016 Wiley Periodicals, Inc.
-
Parvin, Darius E; McDougle, Samuel D; Taylor, Jordan A; Ivry, Richard B
2018-05-09
Failures to obtain reward can occur from errors in action selection or action execution. Recently, we observed marked differences in choice behavior when the failure to obtain a reward was attributed to errors in action execution compared with errors in action selection (McDougle et al., 2016). Specifically, participants appeared to solve this credit assignment problem by discounting outcomes in which the absence of reward was attributed to errors in action execution. Building on recent evidence indicating relatively direct communication between the cerebellum and basal ganglia, we hypothesized that cerebellar-dependent sensory prediction errors (SPEs), a signal indicating execution failure, could attenuate value updating within a basal ganglia-dependent reinforcement learning system. Here we compared the SPE hypothesis to an alternative, "top-down" hypothesis in which changes in choice behavior reflect participants' sense of agency. In two experiments with male and female human participants, we manipulated the strength of SPEs, along with the participants' sense of agency in the second experiment. The results showed that, whereas the strength of SPE had no effect on choice behavior, participants were much more likely to discount the absence of rewards under conditions in which they believed the reward outcome depended on their ability to produce accurate movements. These results provide strong evidence that SPEs do not directly influence reinforcement learning. Instead, a participant's sense of agency appears to play a significant role in modulating choice behavior when unexpected outcomes can arise from errors in action execution. SIGNIFICANCE STATEMENT When learning from the outcome of actions, the brain faces a credit assignment problem: Failures of reward can be attributed to poor choice selection or poor action execution. Here, we test a specific hypothesis that execution errors are implicitly signaled by cerebellar-based sensory prediction errors. We evaluate this hypothesis and compare it with a more "top-down" hypothesis in which the modulation of choice behavior from execution errors reflects participants' sense of agency. We find that sensory prediction errors have no significant effect on reinforcement learning. Instead, instructions influencing participants' belief of causal outcomes appear to be the main factor influencing their choice behavior. Copyright © 2018 the authors 0270-6474/18/384521-10$15.00/0.
-
Prediction-error in the context of real social relationships modulates reward system activity.
Poore, Joshua C; Pfeifer, Jennifer H; Berkman, Elliot T; Inagaki, Tristen K; Welborn, Benjamin L; Lieberman, Matthew D
2012-01-01
The human reward system is sensitive to both social (e.g., validation) and non-social rewards (e.g., money) and is likely integral for relationship development and reputation building. However, data is sparse on the question of whether implicit social reward processing meaningfully contributes to explicit social representations such as trust and attachment security in pre-existing relationships. This event-related fMRI experiment examined reward system prediction-error activity in response to a potent social reward-social validation-and this activity's relation to both attachment security and trust in the context of real romantic relationships. During the experiment, participants' expectations for their romantic partners' positive regard of them were confirmed (validated) or violated, in either positive or negative directions. Primary analyses were conducted using predefined regions of interest, the locations of which were taken from previously published research. Results indicate that activity for mid-brain and striatal reward system regions of interest was modulated by social reward expectation violation in ways consistent with prior research on reward prediction-error. Additionally, activity in the striatum during viewing of disconfirmatory information was associated with both increases in post-scan reports of attachment anxiety and decreases in post-scan trust, a finding that follows directly from representational models of attachment and trust.
-
Taylor, Jasmine B; Visser, Troy A W; Fueggle, Simone N; Bellgrove, Mark A; Fox, Allison M
2018-04-01
Previous studies have postulated that the error-related negativity (ERN) may reflect individual differences in impulsivity; however, none have used a longitudinal framework or evaluated impulsivity as a multidimensional construct. The current study evaluated whether ERN amplitude, measured in childhood and adolescence, is predictive of impulsiveness during adolescence. Seventy-five children participated in this study, initially at ages 7-9 years and again at 12-18 years. The interval between testing sessions ranged from 5 to 9 years. The ERN was extracted in response to behavioural errors produced during a modified visual flanker task at both time points (i.e. childhood and adolescence). Participants also completed the Barratt Impulsiveness Scale - a measure that considers impulsiveness to comprise three core sub-traits - during adolescence. At adolescence, the ERN amplitude was significantly larger than during childhood. Additionally, ERN amplitude during adolescence significantly predicted motor impulsiveness at that time point, after controlling for age, gender, and the number of trials included in the ERN. In contrast, ERN amplitude during childhood did not uniquely predict impulsiveness during adolescence. These findings provide preliminary evidence that ERN amplitude is an electrophysiological marker of self-reported motor impulsiveness (i.e. acting without thinking) during adolescence. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.
-
Ye, Min; Nagar, Swati; Korzekwa, Ken
2015-01-01
Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data was often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding, and blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for terminal elimination half-life (t1/2, 100% of drugs), peak plasma concentration (Cmax, 100%), area under the plasma concentration-time curve (AUC0–t, 95.4%), clearance (CLh, 95.4%), mean retention time (MRT, 95.4%), and steady state volume (Vss, 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. PMID:26531057
-
Forecasting impact injuries of unrestrained occupants in railway vehicle passenger compartments.
Xie, Suchao; Zhou, Hui
2014-01-01
In order to predict the injury parameters of the occupants corresponding to different experimental parameters and to determine impact injury indices conveniently and efficiently, a model forecasting occupant impact injury was established in this work. The work was based on finite experimental observation values obtained by numerical simulation. First, the various factors influencing the impact injuries caused by the interaction between unrestrained occupants and the compartment's internal structures were collated and the most vulnerable regions of the occupant's body were analyzed. Then, the forecast model was set up based on a genetic algorithm-back propagation (GA-BP) hybrid algorithm, which unified the individual characteristics of the back propagation-artificial neural network (BP-ANN) model and the genetic algorithm (GA). The model was well suited to studies of occupant impact injuries and allowed multiple-parameter forecasts of the occupant impact injuries to be realized assuming values for various influencing factors. Finally, the forecast results for three types of secondary collision were analyzed using forecasting accuracy evaluation methods. All of the results showed the ideal accuracy of the forecast model. When an occupant faced a table, the relative errors between the predicted and experimental values of the respective injury parameters were kept within ± 6.0 percent and the average relative error (ARE) values did not exceed 3.0 percent. When an occupant faced a seat, the relative errors between the predicted and experimental values of the respective injury parameters were kept within ± 5.2 percent and the ARE values did not exceed 3.1 percent. When the occupant faced another occupant, the relative errors between the predicted and experimental values of the respective injury parameters were kept within ± 6.3 percent and the ARE values did not exceed 3.8 percent. The injury forecast model established in this article reduced repeat experiment times and improved the design efficiency of the internal compartment's structure parameters, and it provided a new way for assessing the safety performance of the interior structural parameters in existing, and newly designed, railway vehicle compartments.
-
Li, Qi-Quan; Wang, Chang-Quan; Zhang, Wen-Jiang; Yu, Yong; Li, Bing; Yang, Juan; Bai, Gen-Chuan; Cai, Yan
2013-02-01
In this study, a radial basis function neural network model combined with ordinary kriging (RBFNN_OK) was adopted to predict the spatial distribution of soil nutrients (organic matter and total N) in a typical hilly region of Sichuan Basin, Southwest China, and the performance of this method was compared with that of ordinary kriging (OK) and regression kriging (RK). All the three methods produced the similar soil nutrient maps. However, as compared with those obtained by multiple linear regression model, the correlation coefficients between the measured values and the predicted values of soil organic matter and total N obtained by neural network model increased by 12. 3% and 16. 5% , respectively, suggesting that neural network model could more accurately capture the complicated relationships between soil nutrients and quantitative environmental factors. The error analyses of the prediction values of 469 validation points indicated that the mean absolute error (MAE) , mean relative error (MRE), and root mean squared error (RMSE) of RBFNN_OK were 6.9%, 7.4%, and 5. 1% (for soil organic matter), and 4.9%, 6.1% , and 4.6% (for soil total N) smaller than those of OK (P<0.01), and 2.4%, 2.6% , and 1.8% (for soil organic matter), and 2.1%, 2.8%, and 2.2% (for soil total N) smaller than those of RK, respectively (P<0.05).
-
NASA Astrophysics Data System (ADS)
Cai, Jun; Wang, Kuaishe; Shi, Jiamin; Wang, Wen; Liu, Yingying
2018-01-01
Constitutive analysis for hot working of BFe10-1-2 alloy was carried out by using experimental stress-strain data from isothermal hot compression tests, in a wide range of temperature of 1,023 1,273 K, and strain rate range of 0.001 10 s-1. A constitutive equation based on modified double multiple nonlinear regression was proposed considering the independent effects of strain, strain rate, temperature and their interrelation. The predicted flow stress data calculated from the developed equation was compared with the experimental data. Correlation coefficient (R), average absolute relative error (AARE) and relative errors were introduced to verify the validity of the developed constitutive equation. Subsequently, a comparative study was made on the capability of strain-compensated Arrhenius-type constitutive model. The results showed that the developed constitutive equation based on modified double multiple nonlinear regression could predict flow stress of BFe10-1-2 alloy with good correlation and generalization.
-
NASA Astrophysics Data System (ADS)
Simmons, B. E.
1981-08-01
This report derives equations predicting satellite ephemeris error as a function of measurement errors of space-surveillance sensors. These equations lend themselves to rapid computation with modest computer resources. They are applicable over prediction times such that measurement errors, rather than uncertainties of atmospheric drag and of Earth shape, dominate in producing ephemeris error. This report describes the specialization of these equations underlying the ANSER computer program, SEEM (Satellite Ephemeris Error Model). The intent is that this report be of utility to users of SEEM for interpretive purposes, and to computer programmers who may need a mathematical point of departure for limited generalization of SEEM.
-
Prediction error induced motor contagions in human behaviors.
Ikegami, Tsuyoshi; Ganesh, Gowrishankar; Takeuchi, Tatsuya; Nakamoto, Hiroki
2018-05-29
Motor contagions refer to implicit effects on one's actions induced by observed actions. Motor contagions are believed to be induced simply by action observation and cause an observer's action to become similar to the action observed. In contrast, here we report a new motor contagion that is induced only when the observation is accompanied by prediction errors - differences between actions one observes and those he/she predicts or expects. In two experiments, one on whole-body baseball pitching and another on simple arm reaching, we show that the observation of the same action induces distinct motor contagions, depending on whether prediction errors are present or not. In the absence of prediction errors, as in previous reports, participants' actions changed to become similar to the observed action, while in the presence of prediction errors, their actions changed to diverge away from it, suggesting distinct effects of action observation and action prediction on human actions. © 2018, Ikegami et al.
-
Hill, Kaylin E; Samuel, Douglas B; Foti, Dan
2016-08-01
The error-related negativity (ERN) is a neural measure of error processing that has been implicated as a neurobehavioral trait and has transdiagnostic links with psychopathology. Few studies, however, have contextualized this traitlike component with regard to dimensions of personality that, as intermediate constructs, may aid in contextualizing links with psychopathology. Accordingly, the aim of this study was to examine the interrelationships between error monitoring and dimensions of personality within a large adult sample (N = 208). Building on previous research, we found that the ERN relates to a combination of negative affect, impulsivity, and conscientiousness. At low levels of conscientiousness, negative urgency (i.e., impulsivity in the context of negative affect) predicted an increased ERN; at high levels of conscientiousness, the effect of negative urgency was not significant. This relationship was driven specifically by the conscientiousness facets of competence, order, and deliberation. Links between personality measures and error positivity amplitude were weaker and nonsignificant. Post-error slowing was also related to conscientiousness, as well as a different facet of impulsivity: lack of perseverance. These findings suggest that, in the general population, error processing is modulated by the joint combination of negative affect, impulsivity, and conscientiousness (i.e., the profile across traits), perhaps more so than any one dimension alone. This work may inform future research concerning aberrant error processing in clinical populations. © 2016 Society for Psychophysiological Research.
-
NASA Technical Reports Server (NTRS)
Federhofer, J. A.
1974-01-01
Laboratory data verifying the pulse quaternary modulation (PQM) theoretical predictions is presented. The first laboratory PQM laser communication system was successfully fabricated, integrated, tested and demonstrated. System bit error rate tests were performed and, in general, indicated approximately a 2 db degradation from the theoretically predicted results. These tests indicated that no gross errors were made in the initial theoretical analysis of PQM. The relative ease with which the entire PQM laboratory system was integrated and tested indicates that PQM is a viable candidate modulation scheme for an operational 400 Mbps baseband laser communication system.
-
NASA Astrophysics Data System (ADS)
Xia, Zhiye; Xu, Lisheng; Chen, Hongbin; Wang, Yongqian; Liu, Jinbao; Feng, Wenlan
2017-06-01
Extended range forecasting of 10-30 days, which lies between medium-term and climate prediction in terms of timescale, plays a significant role in decision-making processes for the prevention and mitigation of disastrous meteorological events. The sensitivity of initial error, model parameter error, and random error in a nonlinear crossprediction error (NCPE) model, and their stability in the prediction validity period in 10-30-day extended range forecasting, are analyzed quantitatively. The associated sensitivity of precipitable water, temperature, and geopotential height during cases of heavy rain and hurricane is also discussed. The results are summarized as follows. First, the initial error and random error interact. When the ratio of random error to initial error is small (10-6-10-2), minor variation in random error cannot significantly change the dynamic features of a chaotic system, and therefore random error has minimal effect on the prediction. When the ratio is in the range of 10-1-2 (i.e., random error dominates), attention should be paid to the random error instead of only the initial error. When the ratio is around 10-2-10-1, both influences must be considered. Their mutual effects may bring considerable uncertainty to extended range forecasting, and de-noising is therefore necessary. Second, in terms of model parameter error, the embedding dimension m should be determined by the factual nonlinear time series. The dynamic features of a chaotic system cannot be depicted because of the incomplete structure of the attractor when m is small. When m is large, prediction indicators can vanish because of the scarcity of phase points in phase space. A method for overcoming the cut-off effect ( m > 4) is proposed. Third, for heavy rains, precipitable water is more sensitive to the prediction validity period than temperature or geopotential height; however, for hurricanes, geopotential height is most sensitive, followed by precipitable water.
-
Validating CFD Predictions of Pharmaceutical Aerosol Deposition with In Vivo Data.
Tian, Geng; Hindle, Michael; Lee, Sau; Longest, P Worth
2015-10-01
CFD provides a powerful approach to evaluate the deposition of pharmaceutical aerosols; however, previous studies have not compared CFD results of deposition throughout the lungs with in vivo data. The in vivo datasets selected for comparison with CFD predictions included fast and slow clearance of monodisperse aerosols as well as 2D gamma scintigraphy measurements for a dry powder inhaler (DPI) and softmist inhaler (SMI). The CFD model included the inhaler, a characteristic model of the mouth-throat (MT) and upper tracheobronchial (TB) airways, stochastic individual pathways (SIPs) representing the remaining TB region, and recent CFD-based correlations to predict pharmaceutical aerosol deposition in the alveolar airways. For the monodisperse aerosol, CFD predictions of total lung deposition agreed with in vivo data providing a percent relative error of 6% averaged across aerosol sizes of 1-7 μm. With the DPI and SMI, deposition was evaluated in the MT, central airways (bifurcations B1-B7), and intermediate plus peripheral airways (B8 through alveoli). Across these regions, CFD predictions produced an average relative error <10% for each inhaler. CFD simulations with the SIP modeling approach were shown to accurately predict regional deposition throughout the lungs for multiple aerosol types and different in vivo assessment methods.
-
Validating CFD Predictions of Pharmaceutical Aerosol Deposition with In Vivo Data
Tian, Geng; Hindle, Michael; Lee, Sau; Longest, P. Worth
2015-01-01
Purpose CFD provides a powerful approach to evaluate the deposition of pharmaceutical aerosols; however, previous studies have not compared CFD results of deposition throughout the lungs with in vivo data. Methods The in vivo datasets selected for comparison with CFD predictions included fast and slow clearance of monodisperse aerosols as well as 2D gamma scintigraphy measurements for a dry powder inhaler (DPI) and softmist inhaler (SMI). The CFD model included the inhaler, a characteristic model of the mouth-throat (MT) and upper tracheobronchial (TB) airways, stochastic individual pathways (SIPs) representing the remaining TB region, and recent CFD-based correlations to predict pharmaceutical aerosol deposition in the alveolar airways. Results For the monodisperse aerosol, CFD predictions of total lung deposition agreed with in vivo data providing a percent relative error of 6% averaged across aerosol sizes of 1-7μm. With the DPI and SMI, deposition was evaluated in the MT, central airways (bifurcations B1-B7), and intermediate plus peripheral airways (B8 through alveoli). Across these regions, CFD predictions produced an average relative error <10% for each inhaler. Conclusions CFD simulations with the SIP modeling approach were shown to accurately predict regional deposition throughout the lungs for multiple aerosol types and different in vivo assessment methods. PMID:25944585
-
Fleming, Kevin K; Bandy, Carole L; Kimble, Matthew O
2010-01-01
The decision to shoot a gun engages executive control processes that can be biased by cultural stereotypes and perceived threat. The neural locus of the decision to shoot is likely to be found in the anterior cingulate cortex (ACC), where cognition and affect converge. Male military cadets at Norwich University (N=37) performed a weapon identification task in which they made rapid decisions to shoot when images of guns appeared briefly on a computer screen. Reaction times, error rates, and electroencephalogram (EEG) activity were recorded. Cadets reacted more quickly and accurately when guns were primed by images of Middle-Eastern males wearing traditional clothing. However, cadets also made more false positive errors when tools were primed by these images. Error-related negativity (ERN) was measured for each response. Deeper ERNs were found in the medial-frontal cortex following false positive responses. Cadets who made fewer errors also produced deeper ERNs, indicating stronger executive control. Pupil size was used to measure autonomic arousal related to perceived threat. Images of Middle-Eastern males in traditional clothing produced larger pupil sizes. An image of Osama bin Laden induced the largest pupil size, as would be predicted for the exemplar of Middle East terrorism. Cadets who showed greater increases in pupil size also made more false positive errors. Regression analyses were performed to evaluate predictions based on current models of perceived threat, stereotype activation, and cognitive control. Measures of pupil size (perceived threat) and ERN (cognitive control) explained significant proportions of the variance in false positive errors to Middle-Eastern males in traditional clothing, while measures of reaction time, signal detection response bias, and stimulus discriminability explained most of the remaining variance.
-
Fleming, Kevin K.; Bandy, Carole L.; Kimble, Matthew O.
2014-01-01
The decision to shoot engages executive control processes that can be biased by cultural stereotypes and perceived threat. The neural locus of the decision to shoot is likely to be found in the anterior cingulate cortex (ACC) where cognition and affect converge. Male military cadets at Norwich University (N=37) performed a weapon identification task in which they made rapid decisions to shoot when images of guns appeared briefly on a computer screen. Reaction times, error rates, and EEG activity were recorded. Cadets reacted more quickly and accurately when guns were primed by images of middle-eastern males wearing traditional clothing. However, cadets also made more false positive errors when tools were primed by these images. Error-related negativity (ERN) was measured for each response. Deeper ERN’s were found in the medial-frontal cortex following false positive responses. Cadets who made fewer errors also produced deeper ERN’s, indicating stronger executive control. Pupil size was used to measure autonomic arousal related to perceived threat. Images of middle-eastern males in traditional clothing produced larger pupil sizes. An image of Osama bin Laden induced the largest pupil size, as would be predicted for the exemplar of Middle East terrorism. Cadets who showed greater increases in pupil size also made more false positive errors. Regression analyses were performed to evaluate predictions based on current models of perceived threat, stereotype activation, and cognitive control. Measures of pupil size (perceived threat) and ERN (cognitive control) explained significant proportions of the variance in false positive errors to middle-eastern males in traditional clothing, while measures of reaction time, signal detection response bias, and stimulus discriminability explained most of the remaining variance. PMID:19813139
-
Predictability of CFSv2 in the tropical Indo-Pacific region, at daily and subseasonal time scales
NASA Astrophysics Data System (ADS)
Krishnamurthy, V.
2018-06-01
The predictability of a coupled climate model is evaluated at daily and intraseasonal time scales in the tropical Indo-Pacific region during boreal summer and winter. This study has assessed the daily retrospective forecasts of the Climate Forecast System version 2 from the National Centers of Environmental Prediction for the period 1982-2010. The growth of errors in the forecasts of daily precipitation, monsoon intraseasonal oscillation (MISO) and the Madden-Julian oscillation (MJO) is studied. The seasonal cycle of the daily climatology of precipitation is reasonably well predicted except for the underestimation during the peak of summer. The anomalies follow the typical pattern of error growth in nonlinear systems and show no difference between summer and winter. The initial errors in all the cases are found to be in the nonlinear phase of the error growth. The doubling time of small errors is estimated by applying Lorenz error formula. For summer and winter, the doubling time of the forecast errors is in the range of 4-7 and 5-14 days while the doubling time of the predictability errors is 6-8 and 8-14 days, respectively. The doubling time in MISO during the summer and MJO during the winter is in the range of 12-14 days, indicating higher predictability and providing optimism for long-range prediction. There is no significant difference in the growth of forecasts errors originating from different phases of MISO and MJO, although the prediction of the active phase seems to be slightly better.
-
Keil, Julian; Balz, Johanna; Gallinat, Jürgen; Senkowski, Daniel
2016-01-01
Our brain generates predictions about forthcoming stimuli and compares predicted with incoming input. Failures in predicting events might contribute to hallucinations and delusions in schizophrenia (SZ). When a stimulus violates prediction, neural activity that reflects prediction error (PE) processing is found. While PE processing deficits have been reported in unisensory paradigms, it is unknown whether SZ patients (SZP) show altered crossmodal PE processing. We measured high-density electroencephalography and applied source estimation approaches to investigate crossmodal PE processing generated by audiovisual speech. In SZP and healthy control participants (HC), we used an established paradigm in which high- and low-predictive visual syllables were paired with congruent or incongruent auditory syllables. We examined crossmodal PE processing in SZP and HC by comparing differences in event-related potentials and neural oscillations between incongruent and congruent high- and low-predictive audiovisual syllables. In both groups event-related potentials between 206 and 250 ms were larger in high- compared with low-predictive syllables, suggesting intact audiovisual incongruence detection in the auditory cortex of SZP. The analysis of oscillatory responses revealed theta-band (4–7 Hz) power enhancement in high- compared with low-predictive syllables between 230 and 370 ms in the frontal cortex of HC but not SZP. Thus aberrant frontal theta-band oscillations reflect crossmodal PE processing deficits in SZ. The present study suggests a top-down multisensory processing deficit and highlights the role of dysfunctional frontal oscillations for the SZ psychopathology. PMID:27358314
-
Nonspinning numerical relativity waveform surrogates: assessing the model
NASA Astrophysics Data System (ADS)
Field, Scott; Blackman, Jonathan; Galley, Chad; Scheel, Mark; Szilagyi, Bela; Tiglio, Manuel
2015-04-01
Recently, multi-modal gravitational waveform surrogate models have been built directly from data numerically generated by the Spectral Einstein Code (SpEC). I will describe ways in which the surrogate model error can be quantified. This task, in turn, requires (i) characterizing differences between waveforms computed by SpEC with those predicted by the surrogate model and (ii) estimating errors associated with the SpEC waveforms from which the surrogate is built. Both pieces can have numerous sources of numerical and systematic errors. We make an attempt to study the most dominant error sources and, ultimately, the surrogate model's fidelity. These investigations yield information about the surrogate model's uncertainty as a function of time (or frequency) and parameter, and could be useful in parameter estimation studies which seek to incorporate model error. Finally, I will conclude by comparing the numerical relativity surrogate model to other inspiral-merger-ringdown models. A companion talk will cover the building of multi-modal surrogate models.
-
NASA Astrophysics Data System (ADS)
Hayashi, Yoshikatsu; Tamura, Yurie; Sase, Kazuya; Sugawara, Ken; Sawada, Yasuji
Prediction mechanism is necessary for human visual motion to compensate a delay of sensory-motor system. In a previous study, “proactive control” was discussed as one example of predictive function of human beings, in which motion of hands preceded the virtual moving target in visual tracking experiments. To study the roles of the positional-error correction mechanism and the prediction mechanism, we carried out an intermittently-visual tracking experiment where a circular orbit is segmented into the target-visible regions and the target-invisible regions. Main results found in this research were following. A rhythmic component appeared in the tracer velocity when the target velocity was relatively high. The period of the rhythm in the brain obtained from environmental stimuli is shortened more than 10%. The shortening of the period of rhythm in the brain accelerates the hand motion as soon as the visual information is cut-off, and causes the precedence of hand motion to the target motion. Although the precedence of the hand in the blind region is reset by the environmental information when the target enters the visible region, the hand motion precedes the target in average when the predictive mechanism dominates the error-corrective mechanism.
-
Draft versus finished sequence data for DNA and protein diagnostic signature development
Gardner, Shea N.; Lam, Marisa W.; Smith, Jason R.; Torres, Clinton L.; Slezak, Tom R.
2005-01-01
Sequencing pathogen genomes is costly, demanding careful allocation of limited sequencing resources. We built a computational Sequencing Analysis Pipeline (SAP) to guide decisions regarding the amount of genomic sequencing necessary to develop high-quality diagnostic DNA and protein signatures. SAP uses simulations to estimate the number of target genomes and close phylogenetic relatives (near neighbors or NNs) to sequence. We use SAP to assess whether draft data are sufficient or finished sequencing is required using Marburg and variola virus sequences. Simulations indicate that intermediate to high-quality draft with error rates of 10−3–10−5 (∼8× coverage) of target organisms is suitable for DNA signature prediction. Low-quality draft with error rates of ∼1% (3× to 6× coverage) of target isolates is inadequate for DNA signature prediction, although low-quality draft of NNs is sufficient, as long as the target genomes are of high quality. For protein signature prediction, sequencing errors in target genomes substantially reduce the detection of amino acid sequence conservation, even if the draft is of high quality. In summary, high-quality draft of target and low-quality draft of NNs appears to be a cost-effective investment for DNA signature prediction, but may lead to underestimation of predicted protein signatures. PMID:16243783
-
Investigation of Error Patterns in Geographical Databases
NASA Technical Reports Server (NTRS)
Dryer, David; Jacobs, Derya A.; Karayaz, Gamze; Gronbech, Chris; Jones, Denise R. (Technical Monitor)
2002-01-01
The objective of the research conducted in this project is to develop a methodology to investigate the accuracy of Airport Safety Modeling Data (ASMD) using statistical, visualization, and Artificial Neural Network (ANN) techniques. Such a methodology can contribute to answering the following research questions: Over a representative sampling of ASMD databases, can statistical error analysis techniques be accurately learned and replicated by ANN modeling techniques? This representative ASMD sample should include numerous airports and a variety of terrain characterizations. Is it possible to identify and automate the recognition of patterns of error related to geographical features? Do such patterns of error relate to specific geographical features, such as elevation or terrain slope? Is it possible to combine the errors in small regions into an error prediction for a larger region? What are the data density reduction implications of this work? ASMD may be used as the source of terrain data for a synthetic visual system to be used in the cockpit of aircraft when visual reference to ground features is not possible during conditions of marginal weather or reduced visibility. In this research, United States Geologic Survey (USGS) digital elevation model (DEM) data has been selected as the benchmark. Artificial Neural Networks (ANNS) have been used and tested as alternate methods in place of the statistical methods in similar problems. They often perform better in pattern recognition, prediction and classification and categorization problems. Many studies show that when the data is complex and noisy, the accuracy of ANN models is generally higher than those of comparable traditional methods.
-
NASA Technical Reports Server (NTRS)
Alexander, Tiffaney Miller
2017-01-01
Research results have shown that more than half of aviation, aerospace and aeronautics mishaps incidents are attributed to human error. As a part of Safety within space exploration ground processing operations, the identification and/or classification of underlying contributors and causes of human error must be identified, in order to manage human error. This research provides a framework and methodology using the Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS), as an analysis tool to identify contributing factors, their impact on human error events, and predict the Human Error probabilities (HEPs) of future occurrences. This research methodology was applied (retrospectively) to six (6) NASA ground processing operations scenarios and thirty (30) years of Launch Vehicle related mishap data. This modifiable framework can be used and followed by other space and similar complex operations.
-
NASA Technical Reports Server (NTRS)
Alexander, Tiffaney Miller
2017-01-01
Research results have shown that more than half of aviation, aerospace and aeronautics mishaps/incidents are attributed to human error. As a part of Safety within space exploration ground processing operations, the identification and/or classification of underlying contributors and causes of human error must be identified, in order to manage human error. This research provides a framework and methodology using the Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS), as an analysis tool to identify contributing factors, their impact on human error events, and predict the Human Error probabilities (HEPs) of future occurrences. This research methodology was applied (retrospectively) to six (6) NASA ground processing operations scenarios and thirty (30) years of Launch Vehicle related mishap data. This modifiable framework can be used and followed by other space and similar complex operations.
-
NASA Technical Reports Server (NTRS)
Alexander, Tiffaney Miller
2017-01-01
Research results have shown that more than half of aviation, aerospace and aeronautics mishaps incidents are attributed to human error. As a part of Quality within space exploration ground processing operations, the identification and or classification of underlying contributors and causes of human error must be identified, in order to manage human error.This presentation will provide a framework and methodology using the Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS), as an analysis tool to identify contributing factors, their impact on human error events, and predict the Human Error probabilities (HEPs) of future occurrences. This research methodology was applied (retrospectively) to six (6) NASA ground processing operations scenarios and thirty (30) years of Launch Vehicle related mishap data. This modifiable framework can be used and followed by other space and similar complex operations.
-
DOE Office of Scientific and Technical Information (OSTI.GOV)
Liu, X.; Wilcox, G.L.
1993-12-31
We have implemented large scale back-propagation neural networks on a 544 node Connection Machine, CM-5, using the C language in MIMD mode. The program running on 512 processors performs backpropagation learning at 0.53 Gflops, which provides 76 million connection updates per second. We have applied the network to the prediction of protein tertiary structure from sequence information alone. A neural network with one hidden layer and 40 million connections is trained to learn the relationship between sequence and tertiary structure. The trained network yields predicted structures of some proteins on which it has not been trained given only their sequences.more » Presentation of the Fourier transform of the sequences accentuates periodicity in the sequence and yields good generalization with greatly increased training efficiency. Training simulations with a large, heterologous set of protein structures (111 proteins from CM-5 time) to solutions with under 2% RMS residual error within the training set (random responses give an RMS error of about 20%). Presentation of 15 sequences of related proteins in a testing set of 24 proteins yields predicted structures with less than 8% RMS residual error, indicating good apparent generalization.« less
-
Dissociable effects of surprising rewards on learning and memory.
Rouhani, Nina; Norman, Kenneth A; Niv, Yael
2018-03-19
Reward-prediction errors track the extent to which rewards deviate from expectations, and aid in learning. How do such errors in prediction interact with memory for the rewarding episode? Existing findings point to both cooperative and competitive interactions between learning and memory mechanisms. Here, we investigated whether learning about rewards in a high-risk context, with frequent, large prediction errors, would give rise to higher fidelity memory traces for rewarding events than learning in a low-risk context. Experiment 1 showed that recognition was better for items associated with larger absolute prediction errors during reward learning. Larger prediction errors also led to higher rates of learning about rewards. Interestingly we did not find a relationship between learning rate for reward and recognition-memory accuracy for items, suggesting that these two effects of prediction errors were caused by separate underlying mechanisms. In Experiment 2, we replicated these results with a longer task that posed stronger memory demands and allowed for more learning. We also showed improved source and sequence memory for items within the high-risk context. In Experiment 3, we controlled for the difficulty of reward learning in the risk environments, again replicating the previous results. Moreover, this control revealed that the high-risk context enhanced item-recognition memory beyond the effect of prediction errors. In summary, our results show that prediction errors boost both episodic item memory and incremental reward learning, but the two effects are likely mediated by distinct underlying systems. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
-
Neural evidence for description dependent reward processing in the framing effect
Yu, Rongjun; Zhang, Ping
2014-01-01
Human decision making can be influenced by emotionally valenced contexts, known as the framing effect. We used event-related brain potentials to investigate how framing influences the encoding of reward. We found that the feedback related negativity (FRN), which indexes the “worse than expected” negative prediction error in the anterior cingulate cortex (ACC), was more negative for the negative frame than for the positive frame in the win domain. Consistent with previous findings that the FRN is not sensitive to “better than expected” positive prediction error, the FRN did not differentiate the positive and negative frame in the loss domain. Our results provide neural evidence that the description invariance principle which states that reward representation and decision making are not influenced by how options are presented is violated in the framing effect. PMID:24733998
-
Yu, Chun-tang; Liu, Ying-ying; Xia, Yu-feng
2014-01-01
The stress-strain data of 20MnNiMo alloy were collected from a series of hot compressions on Gleeble-1500 thermal-mechanical simulator in the temperature range of 1173∼1473 K and strain rate range of 0.01∼10 s−1. Based on the experimental data, the improved Arrhenius-type constitutive model and the artificial neural network (ANN) model were established to predict the high temperature flow stress of as-cast 20MnNiMo alloy. The accuracy and reliability of the improved Arrhenius-type model and the trained ANN model were further evaluated in terms of the correlation coefficient (R), the average absolute relative error (AARE), and the relative error (η). For the former, R and AARE were found to be 0.9954 and 5.26%, respectively, while, for the latter, 0.9997 and 1.02%, respectively. The relative errors (η) of the improved Arrhenius-type model and the ANN model were, respectively, in the range of −39.99%∼35.05% and −3.77%∼16.74%. As for the former, only 16.3% of the test data set possesses η-values within ±1%, while, as for the latter, more than 79% possesses. The results indicate that the ANN model presents a higher predictable ability than the improved Arrhenius-type constitutive model. PMID:24688358
-
Cisler, Josh M; Bush, Keith; Scott Steele, J; Lenow, Jennifer K; Smitherman, Sonet; Kilts, Clinton D
2015-04-01
Current neurocircuitry models of PTSD focus on the neural mechanisms that mediate hypervigilance for threat and fear inhibition/extinction learning. Less focus has been directed towards explaining social deficits and heightened risk of revictimization observed among individuals with PTSD related to physical or sexual assault. The purpose of the present study was to foster more comprehensive theoretical models of PTSD by testing the hypothesis that assault-related PTSD is associated with behavioral impairments in a social trust and reciprocity task and corresponding alterations in the neural encoding of social learning mechanisms. Adult women with assault-related PTSD (n = 25) and control women (n = 15) completed a multi-trial trust game outside of the MRI scanner. A subset of these participants (15 with PTSD and 14 controls) also completed a social and non-social reinforcement learning task during 3T fMRI. Brain regions that encoded the computationally modeled parameters of value expectation, prediction error, and volatility (i.e., uncertainty) were defined and compared between groups. The PTSD group demonstrated slower learning rates during the trust game and social prediction errors had a lesser impact on subsequent investment decisions. PTSD was also associated with greater encoding of negative expected social outcomes in perigenual anterior cingulate cortex and bilateral middle frontal gyri, and greater encoding of social prediction errors in the left temporoparietal junction. These data suggest mechanisms of PTSD-related deficits in social functioning and heightened risk for re-victimization in assault victims; however, comorbidity in the PTSD group and the lack of a trauma-exposed control group temper conclusions about PTSD specifically. Copyright © 2015 Elsevier Ltd. All rights reserved.
-
Ye, Min; Nagar, Swati; Korzekwa, Ken
2016-04-01
Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data were often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding and the blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate the model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for the terminal elimination half-life (t1/2 , 100% of drugs), peak plasma concentration (Cmax , 100%), area under the plasma concentration-time curve (AUC0-t , 95.4%), clearance (CLh , 95.4%), mean residence time (MRT, 95.4%) and steady state volume (Vss , 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
-
Grid Quality and Resolution Issues from the Drag Prediction Workshop Series
NASA Technical Reports Server (NTRS)
Mavriplis, Dimitri J.; Vassberg, John C.; Tinoco, Edward N.; Mani, Mori; Brodersen, Olaf P.; Eisfeld, Bernhard; Wahls, Richard A.; Morrison, Joseph H.; Zickuhr, Tom; Levy, David;
2008-01-01
The drag prediction workshop series (DPW), held over the last six years, and sponsored by the AIAA Applied Aerodynamics Committee, has been extremely useful in providing an assessment of the state-of-the-art in computationally based aerodynamic drag prediction. An emerging consensus from the three workshop series has been the identification of spatial discretization errors as a dominant error source in absolute as well as incremental drag prediction. This paper provides an overview of the collective experience from the workshop series regarding the effect of grid-related issues on overall drag prediction accuracy. Examples based on workshop results are used to illustrate the effect of grid resolution and grid quality on drag prediction, and grid convergence behavior is examined in detail. For fully attached flows, various accurate and successful workshop results are demonstrated, while anomalous behavior is identified for a number of cases involving substantial regions of separated flow. Based on collective workshop experiences, recommendations for improvements in mesh generation technology which have the potential to impact the state-of-the-art of aerodynamic drag prediction are given.
-
Deep learning methods for protein torsion angle prediction.
Li, Haiou; Hou, Jie; Adhikari, Badri; Lyu, Qiang; Cheng, Jianlin
2017-09-18
Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.
-
Lee, Wonseok; Bae, Hyoung Won; Lee, Si Hyung; Kim, Chan Yun; Seong, Gong Je
2017-03-01
To assess the accuracy of intraocular lens (IOL) power prediction for cataract surgery with open angle glaucoma (OAG) and to identify preoperative angle parameters correlated with postoperative unpredicted refractive errors. This study comprised 45 eyes from 45 OAG subjects and 63 eyes from 63 non-glaucomatous cataract subjects (controls). We investigated differences in preoperative predicted refractive errors and postoperative refractive errors for each group. Preoperative predicted refractive errors were obtained by biometry (IOL-master) and compared to postoperative refractive errors measured by auto-refractometer 2 months postoperatively. Anterior angle parameters were determined using swept source optical coherence tomography. We investigated correlations between preoperative angle parameters [angle open distance (AOD); trabecular iris surface area (TISA); angle recess area (ARA); trabecular iris angle (TIA)] and postoperative unpredicted refractive errors. In patients with OAG, significant differences were noted between preoperative predicted and postoperative real refractive errors, with more myopia than predicted. No significant differences were recorded in controls. Angle parameters (AOD, ARA, TISA, and TIA) at the superior and inferior quadrant were significantly correlated with differences between predicted and postoperative refractive errors in OAG patients (-0.321 to -0.408, p<0.05). Superior quadrant AOD 500 was significantly correlated with postoperative refractive differences in multivariate linear regression analysis (β=-2.925, R²=0.404). Clinically unpredicted refractive errors after cataract surgery were more common in OAG than in controls. Certain preoperative angle parameters, especially AOD 500 at the superior quadrant, were significantly correlated with these unpredicted errors.
-
Lee, Wonseok; Bae, Hyoung Won; Lee, Si Hyung; Kim, Chan Yun
2017-01-01
Purpose To assess the accuracy of intraocular lens (IOL) power prediction for cataract surgery with open angle glaucoma (OAG) and to identify preoperative angle parameters correlated with postoperative unpredicted refractive errors. Materials and Methods This study comprised 45 eyes from 45 OAG subjects and 63 eyes from 63 non-glaucomatous cataract subjects (controls). We investigated differences in preoperative predicted refractive errors and postoperative refractive errors for each group. Preoperative predicted refractive errors were obtained by biometry (IOL-master) and compared to postoperative refractive errors measured by auto-refractometer 2 months postoperatively. Anterior angle parameters were determined using swept source optical coherence tomography. We investigated correlations between preoperative angle parameters [angle open distance (AOD); trabecular iris surface area (TISA); angle recess area (ARA); trabecular iris angle (TIA)] and postoperative unpredicted refractive errors. Results In patients with OAG, significant differences were noted between preoperative predicted and postoperative real refractive errors, with more myopia than predicted. No significant differences were recorded in controls. Angle parameters (AOD, ARA, TISA, and TIA) at the superior and inferior quadrant were significantly correlated with differences between predicted and postoperative refractive errors in OAG patients (-0.321 to -0.408, p<0.05). Superior quadrant AOD 500 was significantly correlated with postoperative refractive differences in multivariate linear regression analysis (β=-2.925, R2=0.404). Conclusion Clinically unpredicted refractive errors after cataract surgery were more common in OAG than in controls. Certain preoperative angle parameters, especially AOD 500 at the superior quadrant, were significantly correlated with these unpredicted errors. PMID:28120576
-
Long-term orbit prediction for China's Tiangong-1 spacecraft based on mean atmosphere model
NASA Astrophysics Data System (ADS)
Tang, Jingshi; Liu, Lin; Miao, Manqian
Tiangong-1 is China's test module for future space station. It has gone through three successful rendezvous and dockings with Shenzhou spacecrafts from 2011 to 2013. For the long-term management and maintenance, the orbit sometimes needs to be predicted for a long period of time. As Tiangong-1 works in a low-Earth orbit with an altitude of about 300-400 km, the error in the a priori atmosphere model contributes significantly to the rapid increase of the predicted orbit error. When the orbit is predicted for 10-20 days, the error in the a priori atmosphere model, if not properly corrected, could induce the semi-major axis error and the overall position error up to a few kilometers and several thousand kilometers respectively. In this work, we use a mean atmosphere model averaged from NRLMSIS00. The a priori reference mean density can be corrected during precise orbit determination (POD). For applications in the long-term orbit prediction, the observations are first accumulated. With sufficiently long period of observations, we are able to obtain a series of the diurnal mean densities. This series bears the recent variation of the atmosphere density and can be analyzed for various periods. After being properly fitted, the mean density can be predicted and then applied in the orbit prediction. We show that the densities predicted with this approach can serve to increase the accuracy of the predicted orbit. In several 20-day prediction tests, most predicted orbits show semi-major axis errors better than 700m and overall position errors better than 600km.
-
Prediction of discretization error using the error transport equation
NASA Astrophysics Data System (ADS)
Celik, Ismail B.; Parsons, Don Roscoe
2017-06-01
This study focuses on an approach to quantify the discretization error associated with numerical solutions of partial differential equations by solving an error transport equation (ETE). The goal is to develop a method that can be used to adequately predict the discretization error using the numerical solution on only one grid/mesh. The primary problem associated with solving the ETE is the formulation of the error source term which is required for accurately predicting the transport of the error. In this study, a novel approach is considered which involves fitting the numerical solution with a series of locally smooth curves and then blending them together with a weighted spline approach. The result is a continuously differentiable analytic expression that can be used to determine the error source term. Once the source term has been developed, the ETE can easily be solved using the same solver that is used to obtain the original numerical solution. The new methodology is applied to the two-dimensional Navier-Stokes equations in the laminar flow regime. A simple unsteady flow case is also considered. The discretization error predictions based on the methodology presented in this study are in good agreement with the 'true error'. While in most cases the error predictions are not quite as accurate as those from Richardson extrapolation, the results are reasonable and only require one numerical grid. The current results indicate that there is much promise going forward with the newly developed error source term evaluation technique and the ETE.
-
DOE Office of Scientific and Technical Information (OSTI.GOV)
Boehnke, E McKenzie; DeMarco, J; Steers, J
2016-06-15
Purpose: To examine both the IQM’s sensitivity and false positive rate to varying MLC errors. By balancing these two characteristics, an optimal tolerance value can be derived. Methods: An un-modified SBRT Liver IMRT plan containing 7 fields was randomly selected as a representative clinical case. The active MLC positions for all fields were perturbed randomly from a square distribution of varying width (±1mm to ±5mm). These unmodified and modified plans were measured multiple times each by the IQM (a large area ion chamber mounted to a TrueBeam linac head). Measurements were analyzed relative to the initial, unmodified measurement. IQM readingsmore » are analyzed as a function of control points. In order to examine sensitivity to errors along a field’s delivery, each measured field was divided into 5 groups of control points, and the maximum error in each group was recorded. Since the plans have known errors, we compared how well the IQM is able to differentiate between unmodified and error plans. ROC curves and logistic regression were used to analyze this, independent of thresholds. Results: A likelihood-ratio Chi-square test showed that the IQM could significantly predict whether a plan had MLC errors, with the exception of the beginning and ending control points. Upon further examination, we determined there was ramp-up occurring at the beginning of delivery. Once the linac AFC was tuned, the subsequent measurements (relative to a new baseline) showed significant (p <0.005) abilities to predict MLC errors. Using the area under the curve, we show the IQM’s ability to detect errors increases with increasing MLC error (Spearman’s Rho=0.8056, p<0.0001). The optimal IQM count thresholds from the ROC curves are ±3%, ±2%, and ±7% for the beginning, middle 3, and end segments, respectively. Conclusion: The IQM has proven to be able to detect not only MLC errors, but also differences in beam tuning (ramp-up). Partially supported by the Susan Scott Foundation.« less
-
NASA Technical Reports Server (NTRS)
Westphal, Douglas L.; Russell, Philip (Technical Monitor)
1994-01-01
A set of 2,600 6-second, National Weather Service soundings from NASA's FIRE-II Cirrus field experiment are used to illustrate previously known errors and new potential errors in the VIZ and SDD brand relative humidity (RH) sensors and the MicroART processing software. The entire spectrum of RH is potentially affected by at least one of these errors. (These errors occur before being converted to dew point temperature.) Corrections to the errors are discussed. Examples are given of the effect that these errors and biases may have on numerical weather prediction and radiative transfer. The figure shows the OLR calculated for the corrected and uncorrected soundings using an 18-band radiative transfer code. The OLR differences are sufficiently large to warrant consideration when validating line-by-line radiation calculations that use radiosonde data to specify the atmospheric state, or when validating satellite retrievals. In addition, a comparison of observations of RE during FIRE-II derived from GOES satellite, raman lidar, MAPS analyses, NCAR CLASS sondes, and the NWS sondes reveals disagreement in the RH distribution and underlines our lack of an understanding of the climatology of water vapor.
-
NASA Technical Reports Server (NTRS)
Westphal, Douglas L.; Russell, Philip B. (Technical Monitor)
1994-01-01
A set of 2,600 6-second, National Weather Service soundings from NASA's FIRE-II Cirrus field experiment are used to illustrate previously known errors and new potential errors in the VIZ and SDD ) brand relative humidity (RH) sensors and the MicroART processing software. The entire spectrum of RH is potentially affected by at least one of these errors. (These errors occur before being converted to dew point temperature.) Corrections to the errors are discussed. Examples are given of the effect that these errors and biases may have on numerical weather prediction and radiative transfer. The figure shows the OLR calculated for the corrected and uncorrected soundings using an 18-band radiative transfer code. The OLR differences are sufficiently large to warrant consideration when validating line-by-line radiation calculations that use radiosonde data to specify the atmospheric state, or when validating satellite retrievals. in addition, a comparison of observations of RH during FIRE-II derived from GOES satellite, raman lidar, MAPS analyses, NCAR CLASS sondes, and the NWS sondes reveals disagreement in the RH distribution and underlines our lack of an understanding of the climatology of water vapor.
-
Checa, Purificación; Castellanos, M C; Abundis-Gutiérrez, Alicia; Rosario Rueda, M
2014-01-01
Regulation of thoughts and behavior requires attention, particularly when there is conflict between alternative responses or when errors are to be prevented or corrected. Conflict monitoring and error processing are functions of the executive attention network, a neurocognitive system that greatly matures during childhood. In this study, we examined the development of brain mechanisms underlying conflict and error processing with event-related potentials (ERPs), and explored the relationship between brain function and individual differences in the ability to self-regulate behavior. Three groups of children aged 4-6, 7-9, and 10-13 years, and a group of adults performed a child-friendly version of the flanker task while ERPs were registered. Marked developmental changes were observed in both conflict processing and brain reactions to errors. After controlling by age, higher self-regulation skills are associated with smaller amplitude of the conflict effect but greater amplitude of the error-related negativity. Additionally, we found that electrophysiological measures of conflict and error monitoring predict individual differences in impulsivity and the capacity to delay gratification. These findings inform of brain mechanisms underlying the development of cognitive control and self-regulation.
-
Checa, Purificación; Castellanos, M. C.; Abundis-Gutiérrez, Alicia; Rosario Rueda, M.
2014-01-01
Regulation of thoughts and behavior requires attention, particularly when there is conflict between alternative responses or when errors are to be prevented or corrected. Conflict monitoring and error processing are functions of the executive attention network, a neurocognitive system that greatly matures during childhood. In this study, we examined the development of brain mechanisms underlying conflict and error processing with event-related potentials (ERPs), and explored the relationship between brain function and individual differences in the ability to self-regulate behavior. Three groups of children aged 4–6, 7–9, and 10–13 years, and a group of adults performed a child-friendly version of the flanker task while ERPs were registered. Marked developmental changes were observed in both conflict processing and brain reactions to errors. After controlling by age, higher self-regulation skills are associated with smaller amplitude of the conflict effect but greater amplitude of the error-related negativity. Additionally, we found that electrophysiological measures of conflict and error monitoring predict individual differences in impulsivity and the capacity to delay gratification. These findings inform of brain mechanisms underlying the development of cognitive control and self-regulation. PMID:24795676
-
NASA Astrophysics Data System (ADS)
Nooruddin, Hasan A.; Anifowose, Fatai; Abdulraheem, Abdulazeez
2014-03-01
Soft computing techniques are recently becoming very popular in the oil industry. A number of computational intelligence-based predictive methods have been widely applied in the industry with high prediction capabilities. Some of the popular methods include feed-forward neural networks, radial basis function network, generalized regression neural network, functional networks, support vector regression and adaptive network fuzzy inference system. A comparative study among most popular soft computing techniques is presented using a large dataset published in literature describing multimodal pore systems in the Arab D formation. The inputs to the models are air porosity, grain density, and Thomeer parameters obtained using mercury injection capillary pressure profiles. Corrected air permeability is the target variable. Applying developed permeability models in recent reservoir characterization workflow ensures consistency between micro and macro scale information represented mainly by Thomeer parameters and absolute permeability. The dataset was divided into two parts with 80% of data used for training and 20% for testing. The target permeability variable was transformed to the logarithmic scale as a pre-processing step and to show better correlations with the input variables. Statistical and graphical analysis of the results including permeability cross-plots and detailed error measures were created. In general, the comparative study showed very close results among the developed models. The feed-forward neural network permeability model showed the lowest average relative error, average absolute relative error, standard deviations of error and root means squares making it the best model for such problems. Adaptive network fuzzy inference system also showed very good results.
-
NASA Technical Reports Server (NTRS)
Nutter, Paul; Manobianco, John
1998-01-01
This report describes the Applied Meteorology Unit's objective verification of the National Centers for Environmental Prediction 29-km eta model during separate warm and cool season periods from May 1996 through January 1998. The verification of surface and upper-air point forecasts was performed at three selected stations important for 45th Weather Squadron, Spaceflight Meteorology Group, and National Weather Service, Melbourne operational weather concerns. The statistical evaluation identified model biases that may result from inadequate parameterization of physical processes. Since model biases are relatively small compared to the random error component, most of the total model error results from day-to-day variability in the forecasts and/or observations. To some extent, these nonsystematic errors reflect the variability in point observations that sample spatial and temporal scales of atmospheric phenomena that cannot be resolved by the model. On average, Meso-Eta point forecasts provide useful guidance for predicting the evolution of the larger scale environment. A more substantial challenge facing model users in real time is the discrimination of nonsystematic errors that tend to inflate the total forecast error. It is important that model users maintain awareness of ongoing model changes. Such changes are likely to modify the basic error characteristics, particularly near the surface.
-
The Role of Multimodel Combination in Improving Streamflow Prediction
NASA Astrophysics Data System (ADS)
Arumugam, S.; Li, W.
2008-12-01
Model errors are the inevitable part in any prediction exercise. One approach that is currently gaining attention to reduce model errors is by optimally combining multiple models to develop improved predictions. The rationale behind this approach primarily lies on the premise that optimal weights could be derived for each model so that the developed multimodel predictions will result in improved predictability. In this study, we present a new approach to combine multiple hydrological models by evaluating their predictability contingent on the predictor state. We combine two hydrological models, 'abcd' model and Variable Infiltration Capacity (VIC) model, with each model's parameter being estimated by two different objective functions to develop multimodel streamflow predictions. The performance of multimodel predictions is compared with individual model predictions using correlation, root mean square error and Nash-Sutcliffe coefficient. To quantify precisely under what conditions the multimodel predictions result in improved predictions, we evaluate the proposed algorithm by testing it against streamflow generated from a known model ('abcd' model or VIC model) with errors being homoscedastic or heteroscedastic. Results from the study show that streamflow simulated from individual models performed better than multimodels under almost no model error. Under increased model error, the multimodel consistently performed better than the single model prediction in terms of all performance measures. The study also evaluates the proposed algorithm for streamflow predictions in two humid river basins from NC as well as in two arid basins from Arizona. Through detailed validation in these four sites, the study shows that multimodel approach better predicts the observed streamflow in comparison to the single model predictions.
-
Effects of modeled tropical sea surface temperature variability on coral reef bleaching predictions
NASA Astrophysics Data System (ADS)
van Hooidonk, R.; Huber, M.
2012-03-01
Future widespread coral bleaching and subsequent mortality has been projected using sea surface temperature (SST) data derived from global, coupled ocean-atmosphere general circulation models (GCMs). While these models possess fidelity in reproducing many aspects of climate, they vary in their ability to correctly capture such parameters as the tropical ocean seasonal cycle and El Niño Southern Oscillation (ENSO) variability. Such weaknesses most likely reduce the accuracy of predicting coral bleaching, but little attention has been paid to the important issue of understanding potential errors and biases, the interaction of these biases with trends, and their propagation in predictions. To analyze the relative importance of various types of model errors and biases in predicting coral bleaching, various intra- and inter-annual frequency bands of observed SSTs were replaced with those frequencies from 24 GCMs 20th century simulations included in the Intergovernmental Panel on Climate Change (IPCC) 4th assessment report. Subsequent thermal stress was calculated and predictions of bleaching were made. These predictions were compared with observations of coral bleaching in the period 1982-2007 to calculate accuracy using an objective measure of forecast quality, the Peirce skill score (PSS). Major findings are that: (1) predictions are most sensitive to the seasonal cycle and inter-annual variability in the ENSO 24-60 months frequency band and (2) because models tend to understate the seasonal cycle at reef locations, they systematically underestimate future bleaching. The methodology we describe can be used to improve the accuracy of bleaching predictions by characterizing the errors and uncertainties involved in the predictions.
-
Expectation and Surprise Determine Neural Population Responses in the Ventral Visual Stream
Egner, Tobias; Monti, Jim M.; Summerfield, Christopher
2014-01-01
Visual cortex is traditionally viewed as a hierarchy of neural feature detectors, with neural population responses being driven by bottom-up stimulus features. Conversely, “predictive coding” models propose that each stage of the visual hierarchy harbors two computationally distinct classes of processing unit: representational units that encode the conditional probability of a stimulus and provide predictions to the next lower level; and error units that encode the mismatch between predictions and bottom-up evidence, and forward prediction error to the next higher level. Predictive coding therefore suggests that neural population responses in category-selective visual regions, like the fusiform face area (FFA), reflect a summation of activity related to prediction (“face expectation”) and prediction error (“face surprise”), rather than a homogenous feature detection response. We tested the rival hypotheses of the feature detection and predictive coding models by collecting functional magnetic resonance imaging data from the FFA while independently varying both stimulus features (faces vs houses) and subjects’ perceptual expectations regarding those features (low vs medium vs high face expectation). The effects of stimulus and expectation factors interacted, whereby FFA activity elicited by face and house stimuli was indistinguishable under high face expectation and maximally differentiated under low face expectation. Using computational modeling, we show that these data can be explained by predictive coding but not by feature detection models, even when the latter are augmented with attentional mechanisms. Thus, population responses in the ventral visual stream appear to be determined by feature expectation and surprise rather than by stimulus features per se. PMID:21147999
-
Application of Exactly Linearized Error Transport Equations to AIAA CFD Prediction Workshops
NASA Technical Reports Server (NTRS)
Derlaga, Joseph M.; Park, Michael A.; Rallabhandi, Sriram
2017-01-01
The computational fluid dynamics (CFD) prediction workshops sponsored by the AIAA have created invaluable opportunities in which to discuss the predictive capabilities of CFD in areas in which it has struggled, e.g., cruise drag, high-lift, and sonic boom pre diction. While there are many factors that contribute to disagreement between simulated and experimental results, such as modeling or discretization error, quantifying the errors contained in a simulation is important for those who make decisions based on the computational results. The linearized error transport equations (ETE) combined with a truncation error estimate is a method to quantify one source of errors. The ETE are implemented with a complex-step method to provide an exact linearization with minimal source code modifications to CFD and multidisciplinary analysis methods. The equivalency of adjoint and linearized ETE functional error correction is demonstrated. Uniformly refined grids from a series of AIAA prediction workshops demonstrate the utility of ETE for multidisciplinary analysis with a connection between estimated discretization error and (resolved or under-resolved) flow features.
-
Two States Mapping Based Time Series Neural Network Model for Compensation Prediction Residual Error
NASA Astrophysics Data System (ADS)
Jung, Insung; Koo, Lockjo; Wang, Gi-Nam
2008-11-01
The objective of this paper was to design a model of human bio signal data prediction system for decreasing of prediction error using two states mapping based time series neural network BP (back-propagation) model. Normally, a lot of the industry has been applied neural network model by training them in a supervised manner with the error back-propagation algorithm for time series prediction systems. However, it still has got a residual error between real value and prediction result. Therefore, we designed two states of neural network model for compensation residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We determined that most of the simulation cases were satisfied by the two states mapping based time series prediction model. In particular, small sample size of times series were more accurate than the standard MLP model.
-
NASA Astrophysics Data System (ADS)
Judt, Falko
2017-04-01
A tremendous increase in computing power has facilitated the advent of global convection-resolving numerical weather prediction (NWP) models. Although this technological breakthrough allows for the seamless prediction of weather from local to global scales, the predictability of multiscale weather phenomena in these models is not very well known. To address this issue, we conducted a global high-resolution (4-km) predictability experiment using the Model for Prediction Across Scales (MPAS), a state-of-the-art global NWP model developed at the National Center for Atmospheric Research. The goals of this experiment are to investigate error growth from convective to planetary scales and to quantify the intrinsic, scale-dependent predictability limits of atmospheric motions. The globally uniform resolution of 4 km allows for the explicit treatment of organized deep moist convection, alleviating grave limitations of previous predictability studies that either used high-resolution limited-area models or global simulations with coarser grids and cumulus parameterization. Error growth is analyzed within the context of an "identical twin" experiment setup: the error is defined as the difference between a 20-day long "nature run" and a simulation that was perturbed with small-amplitude noise, but is otherwise identical. It is found that in convectively active regions, errors grow by several orders of magnitude within the first 24 h ("super-exponential growth"). The errors then spread to larger scales and begin a phase of exponential growth after 2-3 days when contaminating the baroclinic zones. After 16 days, the globally averaged error saturates—suggesting that the intrinsic limit of atmospheric predictability (in a general sense) is about two weeks, which is in line with earlier estimates. However, error growth rates differ between the tropics and mid-latitudes as well as between the troposphere and stratosphere, highlighting that atmospheric predictability is a complex problem. The comparatively slower error growth in the tropics and in the stratosphere indicates that certain weather phenomena could potentially have longer predictability than currently thought.
-
Forces associated with pneumatic power screwdriver operation: statics and dynamics.
Lin, Jia-Hua; Radwin, Robert G; Fronczak, Frank J; Richard, Terry G
2003-10-10
The statics and dynamics of pneumatic power screwdriver operation were investigated in the context of predicting forces acting against the human operator. A static force model is described in the paper, based on tool geometry, mass, orientation in space, feed force, torque build up, and stall torque. Three common power hand tool shapes are considered, including pistol grip, right angle, and in-line. The static model estimates handle force needed to support a power nutrunner when it acts against the tightened fastener with a constant torque. A system of equations for static force and moment equilibrium conditions are established, and the resultant handle force (resolved in orthogonal directions) is calculated in matrix form. A dynamic model is formulated to describe pneumatic motor torque build-up characteristics dependent on threaded fastener joint hardness. Six pneumatic tools were tested to validate the deterministic model. The average torque prediction error was 6.6% (SD = 5.4%) and the average handle force prediction error was 6.7% (SD = 6.4%) for a medium-soft threaded fastener joint. The average torque prediction error was 5.2% (SD = 5.3%) and the average handle force prediction error was 3.6% (SD = 3.2%) for a hard threaded fastener joint. Use of these equations for estimating handle forces based on passive mechanical elements representing the human operator is also described. These models together should be useful for considering tool handle force in the selection and design of power screwdrivers, particularly for minimizing handle forces in the prevention of injuries and work related musculoskeletal disorders.
-
Prediction of the compression ratio for municipal solid waste using decision tree.
Heshmati R, Ali Akbar; Mokhtari, Maryam; Shakiba Rad, Saeed
2014-01-01
The compression ratio of municipal solid waste (MSW) is an essential parameter for evaluation of waste settlement and landfill design. However, no appropriate model has been proposed to estimate the waste compression ratio so far. In this study, a decision tree method was utilized to predict the waste compression ratio (C'c). The tree was constructed using Quinlan's M5 algorithm. A reliable database retrieved from the literature was used to develop a practical model that relates C'c to waste composition and properties, including dry density, dry weight water content, and percentage of biodegradable organic waste using the decision tree method. The performance of the developed model was examined in terms of different statistical criteria, including correlation coefficient, root mean squared error, mean absolute error and mean bias error, recommended by researchers. The obtained results demonstrate that the suggested model is able to evaluate the compression ratio of MSW effectively.
-
EUV local CDU healing performance and modeling capability towards 5nm node
NASA Astrophysics Data System (ADS)
Jee, Tae Kwon; Timoshkov, Vadim; Choi, Peter; Rio, David; Tsai, Yu-Cheng; Yaegashi, Hidetami; Koike, Kyohei; Fonseca, Carlos; Schoofs, Stijn
2017-10-01
Both local variability and optical proximity correction (OPC) errors are big contributors to the edge placement error (EPE) budget which is closely related to the device yield. The post-litho contact hole healing will be demonstrated to meet after-etch local variability specifications using a low dose, 30mJ/cm2 dose-to-size, positive tone developed (PTD) resist with relevant throughput in high volume manufacturing (HVM). The total local variability of the node 5nm (N5) contact holes will be characterized in terms of local CD uniformity (LCDU), local placement error (LPE), and contact edge roughness (CER) using a statistical methodology. The CD healing process has complex etch proximity effects, so the OPC prediction accuracy is challenging to meet EPE requirements for the N5. Thus, the prediction accuracy of an after-etch model will be investigated and discussed using ASML Tachyon OPC model.
-
Prediction of final error level in learning and repetitive control
NASA Astrophysics Data System (ADS)
Levoci, Peter A.
Repetitive control (RC) is a field that creates controllers to eliminate the effects of periodic disturbances on a feedback control system. The methods have applications in spacecraft problems, to isolate fine pointing equipment from periodic vibration disturbances such as slight imbalances in momentum wheels or cryogenic pumps. A closely related field of control design is iterative learning control (ILC) which aims to eliminate tracking error in a task that repeats, each time starting from the same initial condition. Experiments done on a robot at NASA Langley Research Center showed that the final error levels produced by different candidate repetitive and learning controllers can be very different, even when each controller is analytically proven to converge to zero error in the deterministic case. Real world plant and measurement noise and quantization noise (from analog to digital and digital to analog converters) in these control methods are acted on as if they were error sources that will repeat and should be cancelled, which implies that the algorithms amplify such errors. Methods are developed that predict the final error levels of general first order ILC, of higher order ILC including current cycle learning, and of general RC, in the presence of noise, using frequency response methods. The method involves much less computation than the corresponding time domain approach that involves large matrices. The time domain approach was previously developed for ILC and handles a certain class of ILC methods. Here methods are created to include zero-phase filtering that is very important in creating practical designs. Also, time domain methods are developed for higher order ILC and for repetitive control. Since RC and ILC must be implemented digitally, all of these methods predict final error levels at the sample times. It is shown here that RC can easily converge to small error levels between sample times, but that ILC in most applications will have large and diverging intersample error if in fact zero error is reached at the sample times. This is independent of the ILC law used, and is purely a property of the physical system. Methods are developed to address this issue.
-
Nee, Derek Evan; Kastner, Sabine; Brown, Joshua W
2011-01-01
The last decade has seen considerable discussion regarding a theoretical account of medial prefrontal cortex (mPFC) function with particular focus on the anterior cingulate cortex. The proposed theories have included conflict detection, error likelihood prediction, volatility monitoring, and several distinct theories of error detection. Arguments for and against particular theories often treat mPFC as functionally homogeneous, or at least nearly so, despite some evidence for distinct functional subregions. Here we used functional magnetic resonance imaging (fMRI) to simultaneously contrast multiple effects of error, conflict, and task-switching that have been individually construed in support of various theories. We found overlapping yet functionally distinct subregions of mPFC, with activations related to dominant error, conflict, and task-switching effects successively found along a rostral-ventral to caudal-dorsal gradient within medial prefrontal cortex. Activations in the rostral cingulate zone (RCZ) were strongly correlated with the unexpectedness of outcomes suggesting a role in outcome prediction and preparing control systems to deal with anticipated outcomes. The results as a whole support a resolution of some ongoing debates in that distinct theories may each pertain to corresponding distinct yet overlapping subregions of mPFC. Copyright © 2010 Elsevier Inc. All rights reserved.
-
NASA Astrophysics Data System (ADS)
Mazurowski, Maciej A.; Zhang, Jing; Lo, Joseph Y.; Kuzmiak, Cherie M.; Ghate, Sujata V.; Yoon, Sora
2014-03-01
Providing high quality mammography education to radiology trainees is essential, as good interpretation skills potentially ensure the highest benefit of screening mammography for patients. We have previously proposed a computer-aided education system that utilizes trainee models, which relate human-assessed image characteristics to interpretation error. We proposed that these models be used to identify the most difficult and therefore the most educationally useful cases for each trainee. In this study, as a next step in our research, we propose to build trainee models that utilize features that are automatically extracted from images using computer vision algorithms. To predict error, we used a logistic regression which accepts imaging features as input and returns error as output. Reader data from 3 experts and 3 trainees were used. Receiver operating characteristic analysis was applied to evaluate the proposed trainee models. Our experiments showed that, for three trainees, our models were able to predict error better than chance. This is an important step in the development of adaptive computer-aided education systems since computer-extracted features will allow for faster and more extensive search of imaging databases in order to identify the most educationally beneficial cases.
-
Context Specificity of Post-Error and Post-Conflict Cognitive Control Adjustments
Forster, Sarah E.; Cho, Raymond Y.
2014-01-01
There has been accumulating evidence that cognitive control can be adaptively regulated by monitoring for processing conflict as an index of online control demands. However, it is not yet known whether top-down control mechanisms respond to processing conflict in a manner specific to the operative task context or confer a more generalized benefit. While previous studies have examined the taskset-specificity of conflict adaptation effects, yielding inconsistent results, control-related performance adjustments following errors have been largely overlooked. This gap in the literature underscores recent debate as to whether post-error performance represents a strategic, control-mediated mechanism or a nonstrategic consequence of attentional orienting. In the present study, evidence of generalized control following both high conflict correct trials and errors was explored in a task-switching paradigm. Conflict adaptation effects were not found to generalize across tasksets, despite a shared response set. In contrast, post-error slowing effects were found to extend to the inactive taskset and were predictive of enhanced post-error accuracy. In addition, post-error performance adjustments were found to persist for several trials and across multiple task switches, a finding inconsistent with attentional orienting accounts of post-error slowing. These findings indicate that error-related control adjustments confer a generalized performance benefit and suggest dissociable mechanisms of post-conflict and post-error control. PMID:24603900
-
Very-short-term wind power prediction by a hybrid model with single- and multi-step approaches
NASA Astrophysics Data System (ADS)
Mohammed, E.; Wang, S.; Yu, J.
2017-05-01
Very-short-term wind power prediction (VSTWPP) has played an essential role for the operation of electric power systems. This paper aims at improving and applying a hybrid method of VSTWPP based on historical data. The hybrid method is combined by multiple linear regressions and least square (MLR&LS), which is intended for reducing prediction errors. The predicted values are obtained through two sub-processes:1) transform the time-series data of actual wind power into the power ratio, and then predict the power ratio;2) use the predicted power ratio to predict the wind power. Besides, the proposed method can include two prediction approaches: single-step prediction (SSP) and multi-step prediction (MSP). WPP is tested comparatively by auto-regressive moving average (ARMA) model from the predicted values and errors. The validity of the proposed hybrid method is confirmed in terms of error analysis by using probability density function (PDF), mean absolute percent error (MAPE) and means square error (MSE). Meanwhile, comparison of the correlation coefficients between the actual values and the predicted values for different prediction times and window has confirmed that MSP approach by using the hybrid model is the most accurate while comparing to SSP approach and ARMA. The MLR&LS is accurate and promising for solving problems in WPP.
-
Functional Independent Scaling Relation for ORR/OER Catalysts
Christensen, Rune; Hansen, Heine A.; Dickens, Colin F.; ...
2016-10-11
A widely used adsorption energy scaling relation between OH* and OOH* intermediates in the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), has previously been determined using density functional theory and shown to dictate a minimum thermodynamic overpotential for both reactions. Here, we show that the oxygen–oxygen bond in the OOH* intermediate is, however, not well described with the previously used class of exchange-correlation functionals. By quantifying and correcting the systematic error, an improved description of gaseous peroxide species versus experimental data and a reduction in calculational uncertainty is obtained. For adsorbates, we find that the systematic error largelymore » cancels the vdW interaction missing in the original determination of the scaling relation. An improved scaling relation, which is fully independent of the applied exchange–correlation functional, is obtained and found to differ by 0.1 eV from the original. Lastly, this largely confirms that, although obtained with a method suffering from systematic errors, the previously obtained scaling relation is applicable for predictions of catalytic activity.« less
-
Goo, Yeung-Ja James; Chi, Der-Jang; Shen, Zong-De
2016-01-01
The purpose of this study is to establish rigorous and reliable going concern doubt (GCD) prediction models. This study first uses the least absolute shrinkage and selection operator (LASSO) to select variables and then applies data mining techniques to establish prediction models, such as neural network (NN), classification and regression tree (CART), and support vector machine (SVM). The samples of this study include 48 GCD listed companies and 124 NGCD (non-GCD) listed companies from 2002 to 2013 in the TEJ database. We conduct fivefold cross validation in order to identify the prediction accuracy. According to the empirical results, the prediction accuracy of the LASSO-NN model is 88.96 % (Type I error rate is 12.22 %; Type II error rate is 7.50 %), the prediction accuracy of the LASSO-CART model is 88.75 % (Type I error rate is 13.61 %; Type II error rate is 14.17 %), and the prediction accuracy of the LASSO-SVM model is 89.79 % (Type I error rate is 10.00 %; Type II error rate is 15.83 %).
-
DOE Office of Scientific and Technical Information (OSTI.GOV)
Newman, Jennifer F.; Clifton, Andrew
Currently, cup anemometers on meteorological towers are used to measure wind speeds and turbulence intensity to make decisions about wind turbine class and site suitability; however, as modern turbine hub heights increase and wind energy expands to complex and remote sites, it becomes more difficult and costly to install meteorological towers at potential sites. As a result, remote-sensing devices (e.g., lidars) are now commonly used by wind farm managers and researchers to estimate the flow field at heights spanned by a turbine. Although lidars can accurately estimate mean wind speeds and wind directions, there is still a large amount ofmore » uncertainty surrounding the measurement of turbulence using these devices. Errors in lidar turbulence estimates are caused by a variety of factors, including instrument noise, volume averaging, and variance contamination, in which the magnitude of these factors is highly dependent on measurement height and atmospheric stability. As turbulence has a large impact on wind power production, errors in turbulence measurements will translate into errors in wind power prediction. The impact of using lidars rather than cup anemometers for wind power prediction must be understood if lidars are to be considered a viable alternative to cup anemometers.In this poster, the sensitivity of power prediction error to typical lidar turbulence measurement errors is assessed. Turbulence estimates from a vertically profiling WINDCUBE v2 lidar are compared to high-resolution sonic anemometer measurements at field sites in Oklahoma and Colorado to determine the degree of lidar turbulence error that can be expected under different atmospheric conditions. These errors are then incorporated into a power prediction model to estimate the sensitivity of power prediction error to turbulence measurement error. Power prediction models, including the standard binning method and a random forest method, were developed using data from the aeroelastic simulator FAST for a 1.5 MW turbine. The impact of lidar turbulence error on the predicted power from these different models is examined to determine the degree of turbulence measurement accuracy needed for accurate power prediction.« less
-
Wind Prediction Accuracy for Air Traffic Management Decision Support Tools
NASA Technical Reports Server (NTRS)
Cole, Rod; Green, Steve; Jardin, Matt; Schwartz, Barry; Benjamin, Stan
2000-01-01
The performance of Air Traffic Management and flight deck decision support tools depends in large part on the accuracy of the supporting 4D trajectory predictions. This is particularly relevant to conflict prediction and active advisories for the resolution of conflicts and the conformance with of traffic-flow management flow-rate constraints (e.g., arrival metering / required time of arrival). Flight test results have indicated that wind prediction errors may represent the largest source of trajectory prediction error. The tests also discovered relatively large errors (e.g., greater than 20 knots), existing in pockets of space and time critical to ATM DST performance (one or more sectors, greater than 20 minutes), are inadequately represented by the classic RMS aggregate prediction-accuracy studies of the past. To facilitate the identification and reduction of DST-critical wind-prediction errors, NASA has lead a collaborative research and development activity with MIT Lincoln Laboratories and the Forecast Systems Lab of the National Oceanographic and Atmospheric Administration (NOAA). This activity, begun in 1996, has focussed on the development of key metrics for ATM DST performance, assessment of wind-prediction skill for state of the art systems, and development/validation of system enhancements to improve skill. A 13 month study was conducted for the Denver Center airspace in 1997. Two complementary wind-prediction systems were analyzed and compared to the forecast performance of the then standard 60 km Rapid Update Cycle - version 1 (RUC-1). One system, developed by NOAA, was the prototype 40-km RUC-2 that became operational at NCEP in 1999. RUC-2 introduced a faster cycle (1 hr vs. 3 hr) and improved mesoscale physics. The second system, Augmented Winds (AW), is a prototype en route wind application developed by MITLL based on the Integrated Terminal Wind System (ITWS). AW is run at a local facility (Center) level, and updates RUC predictions based on an optimal interpolation of the latest ACARS reports since the RUC run. This paper presents an overview of the study's results including the identification and use of new large mor wind-prediction accuracy metrics that are key to ATM DST performance.
-
Error disclosure: a new domain for safety culture assessment.
Etchegaray, Jason M; Gallagher, Thomas H; Bell, Sigall K; Dunlap, Ben; Thomas, Eric J
2012-07-01
To (1) develop and test survey items that measure error disclosure culture, (2) examine relationships among error disclosure culture, teamwork culture and safety culture and (3) establish predictive validity for survey items measuring error disclosure culture. All clinical faculty from six health institutions (four medical schools, one cancer centre and one health science centre) in The University of Texas System were invited to anonymously complete an electronic survey containing questions about safety culture and error disclosure. The authors found two factors to measure error disclosure culture: one factor is focused on the general culture of error disclosure and the second factor is focused on trust. Both error disclosure culture factors were unique from safety culture and teamwork culture (correlations were less than r=0.85). Also, error disclosure general culture and error disclosure trust culture predicted intent to disclose a hypothetical error to a patient (r=0.25, p<0.001 and r=0.16, p<0.001, respectively) while teamwork and safety culture did not predict such an intent (r=0.09, p=NS and r=0.12, p=NS). Those who received prior error disclosure training reported significantly higher levels of error disclosure general culture (t=3.7, p<0.05) and error disclosure trust culture (t=2.9, p<0.05). The authors created and validated a new measure of error disclosure culture that predicts intent to disclose an error better than other measures of healthcare culture. This measure fills an existing gap in organisational assessments by assessing transparent communication after medical error, an important aspect of culture.
-
Modeling Errors in Daily Precipitation Measurements: Additive or Multiplicative?
NASA Technical Reports Server (NTRS)
Tian, Yudong; Huffman, George J.; Adler, Robert F.; Tang, Ling; Sapiano, Matthew; Maggioni, Viviana; Wu, Huan
2013-01-01
The definition and quantification of uncertainty depend on the error model used. For uncertainties in precipitation measurements, two types of error models have been widely adopted: the additive error model and the multiplicative error model. This leads to incompatible specifications of uncertainties and impedes intercomparison and application.In this letter, we assess the suitability of both models for satellite-based daily precipitation measurements in an effort to clarify the uncertainty representation. Three criteria were employed to evaluate the applicability of either model: (1) better separation of the systematic and random errors; (2) applicability to the large range of variability in daily precipitation; and (3) better predictive skills. It is found that the multiplicative error model is a much better choice under all three criteria. It extracted the systematic errors more cleanly, was more consistent with the large variability of precipitation measurements, and produced superior predictions of the error characteristics. The additive error model had several weaknesses, such as non constant variance resulting from systematic errors leaking into random errors, and the lack of prediction capability. Therefore, the multiplicative error model is a better choice.
-
How learning shapes the empathic brain.
Hein, Grit; Engelmann, Jan B; Vollberg, Marius C; Tobler, Philippe N
2016-01-05
Deficits in empathy enhance conflicts and human suffering. Thus, it is crucial to understand how empathy can be learned and how learning experiences shape empathy-related processes in the human brain. As a model of empathy deficits, we used the well-established suppression of empathy-related brain responses for the suffering of out-groups and tested whether and how out-group empathy is boosted by a learning intervention. During this intervention, participants received costly help equally often from an out-group member (experimental group) or an in-group member (control group). We show that receiving help from an out-group member elicits a classical learning signal (prediction error) in the anterior insular cortex. This signal in turn predicts a subsequent increase of empathy for a different out-group member (generalization). The enhancement of empathy-related insula responses by the neural prediction error signal was mediated by an establishment of positive emotions toward the out-group member. Finally, we show that surprisingly few positive learning experiences are sufficient to increase empathy. Our results specify the neural and psychological mechanisms through which learning interacts with empathy, and thus provide a neurobiological account for the plasticity of empathic reactions.
-
How learning shapes the empathic brain
Hein, Grit; Vollberg, Marius C.; Tobler, Philippe N.
2016-01-01
Deficits in empathy enhance conflicts and human suffering. Thus, it is crucial to understand how empathy can be learned and how learning experiences shape empathy-related processes in the human brain. As a model of empathy deficits, we used the well-established suppression of empathy-related brain responses for the suffering of out-groups and tested whether and how out-group empathy is boosted by a learning intervention. During this intervention, participants received costly help equally often from an out-group member (experimental group) or an in-group member (control group). We show that receiving help from an out-group member elicits a classical learning signal (prediction error) in the anterior insular cortex. This signal in turn predicts a subsequent increase of empathy for a different out-group member (generalization). The enhancement of empathy-related insula responses by the neural prediction error signal was mediated by an establishment of positive emotions toward the out-group member. Finally, we show that surprisingly few positive learning experiences are sufficient to increase empathy. Our results specify the neural and psychological mechanisms through which learning interacts with empathy, and thus provide a neurobiological account for the plasticity of empathic reactions. PMID:26699464
-
Statistical analysis of modeling error in structural dynamic systems
NASA Technical Reports Server (NTRS)
Hasselman, T. K.; Chrostowski, J. D.
1990-01-01
The paper presents a generic statistical model of the (total) modeling error for conventional space structures in their launch configuration. Modeling error is defined as the difference between analytical prediction and experimental measurement. It is represented by the differences between predicted and measured real eigenvalues and eigenvectors. Comparisons are made between pre-test and post-test models. Total modeling error is then subdivided into measurement error, experimental error and 'pure' modeling error, and comparisons made between measurement error and total modeling error. The generic statistical model presented in this paper is based on the first four global (primary structure) modes of four different structures belonging to the generic category of Conventional Space Structures (specifically excluding large truss-type space structures). As such, it may be used to evaluate the uncertainty of predicted mode shapes and frequencies, sinusoidal response, or the transient response of other structures belonging to the same generic category.
-
Zhu, Lusha; Mathewson, Kyle E.; Hsu, Ming
2012-01-01
Decision-making in the presence of other competitive intelligent agents is fundamental for social and economic behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions of others competing for the same rewards. However, whereas we know much about strategic learning at both theoretical and behavioral levels, we know relatively little about the underlying neural mechanisms. Here, we show using a multi-strategy competitive learning paradigm that strategic choices can be characterized by extending the reinforcement learning (RL) framework to incorporate agents’ beliefs about the actions of their opponents. Furthermore, using this characterization to generate putative internal values, we used model-based functional magnetic resonance imaging to investigate neural computations underlying strategic learning. We found that the distinct notions of prediction errors derived from our computational model are processed in a partially overlapping but distinct set of brain regions. Specifically, we found that the RL prediction error was correlated with activity in the ventral striatum. In contrast, activity in the ventral striatum, as well as the rostral anterior cingulate (rACC), was correlated with a previously uncharacterized belief-based prediction error. Furthermore, activity in rACC reflected individual differences in degree of engagement in belief learning. These results suggest a model of strategic behavior where learning arises from interaction of dissociable reinforcement and belief-based inputs. PMID:22307594
-
Zhu, Lusha; Mathewson, Kyle E; Hsu, Ming
2012-01-31
Decision-making in the presence of other competitive intelligent agents is fundamental for social and economic behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions of others competing for the same rewards. However, whereas we know much about strategic learning at both theoretical and behavioral levels, we know relatively little about the underlying neural mechanisms. Here, we show using a multi-strategy competitive learning paradigm that strategic choices can be characterized by extending the reinforcement learning (RL) framework to incorporate agents' beliefs about the actions of their opponents. Furthermore, using this characterization to generate putative internal values, we used model-based functional magnetic resonance imaging to investigate neural computations underlying strategic learning. We found that the distinct notions of prediction errors derived from our computational model are processed in a partially overlapping but distinct set of brain regions. Specifically, we found that the RL prediction error was correlated with activity in the ventral striatum. In contrast, activity in the ventral striatum, as well as the rostral anterior cingulate (rACC), was correlated with a previously uncharacterized belief-based prediction error. Furthermore, activity in rACC reflected individual differences in degree of engagement in belief learning. These results suggest a model of strategic behavior where learning arises from interaction of dissociable reinforcement and belief-based inputs.
-
Altered neural encoding of prediction errors in assault-related posttraumatic stress disorder.
Ross, Marisa C; Lenow, Jennifer K; Kilts, Clinton D; Cisler, Josh M
2018-05-12
Posttraumatic stress disorder (PTSD) is widely associated with deficits in extinguishing learned fear responses, which relies on mechanisms of reinforcement learning (e.g., updating expectations based on prediction errors). However, the degree to which PTSD is associated with impairments in general reinforcement learning (i.e., outside of the context of fear stimuli) remains poorly understood. Here, we investigate brain and behavioral differences in general reinforcement learning between adult women with and without a current diagnosis of PTSD. 29 adult females (15 PTSD with exposure to assaultive violence, 14 controls) underwent a neutral reinforcement-learning task (i.e., two arm bandit task) during fMRI. We modeled participant behavior using different adaptations of the Rescorla-Wagner (RW) model and used Independent Component Analysis to identify timecourses for large-scale a priori brain networks. We found that an anticorrelated and risk sensitive RW model best fit participant behavior, with no differences in computational parameters between groups. Women in the PTSD group demonstrated significantly less neural encoding of prediction errors in both a ventral striatum/mPFC and anterior insula network compared to healthy controls. Weakened encoding of prediction errors in the ventral striatum/mPFC and anterior insula during a general reinforcement learning task, outside of the context of fear stimuli, suggests the possibility of a broader conceptualization of learning differences in PTSD than currently proposed in current neurocircuitry models of PTSD. Copyright © 2018 Elsevier Ltd. All rights reserved.
-
Prediction error and somatosensory insula activation in women recovered from anorexia nervosa.
Frank, Guido K W; Collier, Shaleise; Shott, Megan E; O'Reilly, Randall C
2016-08-01
Previous research in patients with anorexia nervosa showed heightened brain response during a taste reward conditioning task and heightened sensitivity to rewarding and punishing stimuli. Here we tested the hypothesis that individuals recovered from anorexia nervosa would also experience greater brain activation during this task as well as higher sensitivity to salient stimuli than controls. Women recovered from restricting-type anorexia nervosa and healthy control women underwent fMRI during application of a prediction error taste reward learning paradigm. Twenty-four women recovered from anorexia nervosa (mean age 30.3 ± 8.1 yr) and 24 control women (mean age 27.4 ± 6.3 yr) took part in this study. The recovered anorexia nervosa group showed greater left posterior insula activation for the prediction error model analysis than the control group (family-wise error- and small volume-corrected p < 0.05). A group × condition analysis found greater posterior insula response in women recovered from anorexia nervosa than controls for unexpected stimulus omission, but not for unexpected receipt. Sensitivity to punishment was elevated in women recovered from anorexia nervosa. This was a cross-sectional study, and the sample size was modest. Anorexia nervosa after recovery is associated with heightened prediction error-related brain response in the posterior insula as well as greater response to unexpected reward stimulus omission. This finding, together with behaviourally increased sensitivity to punishment, could indicate that individuals recovered from anorexia nervosa are particularly responsive to punishment. The posterior insula processes somatosensory stimuli, including unexpected bodily states, and greater response could indicate altered perception or integration of unexpected or maybe unwanted bodily feelings. Whether those findings develop during the ill state or whether they are biological traits requires further study.
-
Autonomous vision-based navigation for proximity operations around binary asteroids
NASA Astrophysics Data System (ADS)
Gil-Fernandez, Jesus; Ortega-Hernando, Guillermo
2018-02-01
Future missions to small bodies demand higher level of autonomy in the Guidance, Navigation and Control system for higher scientific return and lower operational costs. Different navigation strategies have been assessed for ESA's asteroid impact mission (AIM). The main objective of AIM is the detailed characterization of binary asteroid Didymos. The trajectories for the proximity operations shall be intrinsically safe, i.e., no collision in presence of failures (e.g., spacecraft entering safe mode), perturbations (e.g., non-spherical gravity field), and errors (e.g., maneuver execution error). Hyperbolic arcs with sufficient hyperbolic excess velocity are designed to fulfil the safety, scientific, and operational requirements. The trajectory relative to the asteroid is determined using visual camera images. The ground-based trajectory prediction error at some points is comparable to the camera Field Of View (FOV). Therefore, some images do not contain the entire asteroid. Autonomous navigation can update the state of the spacecraft relative to the asteroid at higher frequency. The objective of the autonomous navigation is to improve the on-board knowledge compared to the ground prediction. The algorithms shall fit in off-the-shelf, space-qualified avionics. This note presents suitable image processing and relative-state filter algorithms for autonomous navigation in proximity operations around binary asteroids.
-
Jacobson, Peggy F; Walden, Patrick R
2013-08-01
This study explored the utility of language sample analysis for evaluating language ability in school-age Spanish-English sequential bilingual children. Specifically, the relative potential of lexical diversity and word/morpheme omission as predictors of typical or atypical language status was evaluated. Narrative samples were obtained from 48 bilingual children in both of their languages using the suggested narrative retell protocol and coding conventions as per Systematic Analysis of Language Transcripts (SALT; Miller & Iglesias, 2008) software. An additional lexical diversity measure, VocD, was also calculated. A series of logistical hierarchical regressions explored the utility of the number of different words, VocD statistic, and word and morpheme omissions in each language for predicting language status. Omission errors turned out to be the best predictors of bilingual language impairment at all ages, and this held true across languages. Although lexical diversity measures did not predict typical or atypical language status, the measures were significantly related to oral language proficiency in English and Spanish. The results underscore the significance of omission errors in bilingual language impairment while simultaneously revealing the limitations of lexical diversity measures as indicators of impairment. The relationship between lexical diversity and oral language proficiency highlights the importance of considering relative language proficiency in bilingual assessment.
-
Autonomous vision-based navigation for proximity operations around binary asteroids
NASA Astrophysics Data System (ADS)
Gil-Fernandez, Jesus; Ortega-Hernando, Guillermo
2018-06-01
Future missions to small bodies demand higher level of autonomy in the Guidance, Navigation and Control system for higher scientific return and lower operational costs. Different navigation strategies have been assessed for ESA's asteroid impact mission (AIM). The main objective of AIM is the detailed characterization of binary asteroid Didymos. The trajectories for the proximity operations shall be intrinsically safe, i.e., no collision in presence of failures (e.g., spacecraft entering safe mode), perturbations (e.g., non-spherical gravity field), and errors (e.g., maneuver execution error). Hyperbolic arcs with sufficient hyperbolic excess velocity are designed to fulfil the safety, scientific, and operational requirements. The trajectory relative to the asteroid is determined using visual camera images. The ground-based trajectory prediction error at some points is comparable to the camera Field Of View (FOV). Therefore, some images do not contain the entire asteroid. Autonomous navigation can update the state of the spacecraft relative to the asteroid at higher frequency. The objective of the autonomous navigation is to improve the on-board knowledge compared to the ground prediction. The algorithms shall fit in off-the-shelf, space-qualified avionics. This note presents suitable image processing and relative-state filter algorithms for autonomous navigation in proximity operations around binary asteroids.
-
The role of strategies in motor learning
Taylor, Jordan A.; Ivry, Richard B.
2015-01-01
There has been renewed interest in the role of strategies in sensorimotor learning. The combination of new behavioral methods and computational methods has begun to unravel the interaction between processes related to strategic control and processes related to motor adaptation. These processes may operate on very different error signals. Strategy learning is sensitive to goal-based performance error. In contrast, adaptation is sensitive to prediction errors between the desired and actual consequences of a planned movement. The former guides what the desired movement should be, whereas the latter guides how to implement the desired movement. Whereas traditional approaches have favored serial models in which an initial strategy-based phase gives way to more automatized forms of control, it now seems that strategic and adaptive processes operate with considerable independence throughout learning, although the relative weight given the two processes will shift with changes in performance. As such, skill acquisition involves the synergistic engagement of strategic and adaptive processes. PMID:22329960
-
Brain potentials measured during a Go/NoGo task predict completion of substance abuse treatment.
Steele, Vaughn R; Fink, Brandi C; Maurer, J Michael; Arbabshirani, Mohammad R; Wilber, Charles H; Jaffe, Adam J; Sidz, Anna; Pearlson, Godfrey D; Calhoun, Vince D; Clark, Vincent P; Kiehl, Kent A
2014-07-01
U.S. nationwide estimates indicate that 50% to 80% of prisoners have a history of substance abuse or dependence. Tailoring substance abuse treatment to specific needs of incarcerated individuals could improve effectiveness of treating substance dependence and preventing drug abuse relapse. We tested whether pretreatment neural measures of a response inhibition (Go/NoGo) task would predict which individuals would or would not complete a 12-week cognitive behavioral substance abuse treatment program. Adult incarcerated participants (n = 89; women n = 55) who volunteered for substance abuse treatment performed a Go/NoGo task while event-related potentials (ERPs) were recorded. Stimulus- and response-locked ERPs were compared between participants who completed (n = 68; women = 45) and discontinued (n = 21; women = 10) treatment. As predicted, stimulus-locked P2, response-locked error-related negativity (ERN/Ne), and response-locked error positivity (Pe), measured with windowed time-domain and principal component analysis, differed between groups. Using logistic regression and support-vector machine (i.e., pattern classifiers) models, P2 and Pe predicted treatment completion above and beyond other measures (i.e., N2, P300, ERN/Ne, age, sex, IQ, impulsivity, depression, anxiety, motivation for change, and years of drug abuse). Participants who discontinued treatment exhibited deficiencies in sensory gating, as indexed by smaller P2; error-monitoring, as indexed by smaller ERN/Ne; and adjusting response strategy posterror, as indexed by larger Pe. The combination of P2 and Pe reliably predicted 83.33% of individuals who discontinued treatment. These results may help in the development of individualized therapies, which could lead to more favorable, long-term outcomes. © 2013 Society of Biological Psychiatry Published by Society of Biological Psychiatry All rights reserved.
-
Dallmann, André; Ince, Ibrahim; Coboeken, Katrin; Eissing, Thomas; Hempel, Georg
2017-09-18
Physiologically based pharmacokinetic modeling is considered a valuable tool for predicting pharmacokinetic changes in pregnancy to subsequently guide in-vivo pharmacokinetic trials in pregnant women. The objective of this study was to extend and verify a previously developed physiologically based pharmacokinetic model for pregnant women for the prediction of pharmacokinetics of drugs metabolized via several cytochrome P450 enzymes. Quantitative information on gestation-specific changes in enzyme activity available in the literature was incorporated in a pregnancy physiologically based pharmacokinetic model and the pharmacokinetics of eight drugs metabolized via one or multiple cytochrome P450 enzymes was predicted. The tested drugs were caffeine, midazolam, nifedipine, metoprolol, ondansetron, granisetron, diazepam, and metronidazole. Pharmacokinetic predictions were evaluated by comparison with in-vivo pharmacokinetic data obtained from the literature. The pregnancy physiologically based pharmacokinetic model successfully predicted the pharmacokinetics of all tested drugs. The observed pregnancy-induced pharmacokinetic changes were qualitatively and quantitatively reasonably well predicted for all drugs. Ninety-seven percent of the mean plasma concentrations predicted in pregnant women fell within a twofold error range and 63% within a 1.25-fold error range. For all drugs, the predicted area under the concentration-time curve was within a 1.25-fold error range. The presented pregnancy physiologically based pharmacokinetic model can quantitatively predict the pharmacokinetics of drugs that are metabolized via one or multiple cytochrome P450 enzymes by integrating prior knowledge of the pregnancy-related effect on these enzymes. This pregnancy physiologically based pharmacokinetic model may thus be used to identify potential exposure changes in pregnant women a priori and to eventually support informed decision making when clinical trials are designed in this special population.
-
Evaluation of wave runup predictions from numerical and parametric models
Stockdon, Hilary F.; Thompson, David M.; Plant, Nathaniel G.; Long, Joseph W.
2014-01-01
Wave runup during storms is a primary driver of coastal evolution, including shoreline and dune erosion and barrier island overwash. Runup and its components, setup and swash, can be predicted from a parameterized model that was developed by comparing runup observations to offshore wave height, wave period, and local beach slope. Because observations during extreme storms are often unavailable, a numerical model is used to simulate the storm-driven runup to compare to the parameterized model and then develop an approach to improve the accuracy of the parameterization. Numerically simulated and parameterized runup were compared to observations to evaluate model accuracies. The analysis demonstrated that setup was accurately predicted by both the parameterized model and numerical simulations. Infragravity swash heights were most accurately predicted by the parameterized model. The numerical model suffered from bias and gain errors that depended on whether a one-dimensional or two-dimensional spatial domain was used. Nonetheless, all of the predictions were significantly correlated to the observations, implying that the systematic errors can be corrected. The numerical simulations did not resolve the incident-band swash motions, as expected, and the parameterized model performed best at predicting incident-band swash heights. An assimilated prediction using a weighted average of the parameterized model and the numerical simulations resulted in a reduction in prediction error variance. Finally, the numerical simulations were extended to include storm conditions that have not been previously observed. These results indicated that the parameterized predictions of setup may need modification for extreme conditions; numerical simulations can be used to extend the validity of the parameterized predictions of infragravity swash; and numerical simulations systematically underpredict incident swash, which is relatively unimportant under extreme conditions.
-
NASA Technical Reports Server (NTRS)
Holms, A. G.
1980-01-01
Population model coefficients were chosen to simulate a saturated 2 to the 4th fixed-effects experiment having an unfavorable distribution of relative values. Using random number studies, deletion strategies were compared that were based on the F-distribution, on an order statistics distribution of Cochran's, and on a combination of the two. The strategies were compared under the criterion of minimizing the maximum prediction error, wherever it occurred, among the two-level factorial points. The strategies were evaluated for each of the conditions of 0, 1, 2, 3, 4, 5, or 6 center points. Three classes of strategies were identified as being appropriate, depending on the extent of the experimenter's prior knowledge. In almost every case the best strategy was found to be unique according to the number of center points. Among the three classes of strategies, a security regret class of strategy was demonstrated as being widely useful in that over a range of coefficients of variation from 4 to 65%, the maximum predictive error was never increased by more than 12% over what it would have been if the best strategy had been used for the particular coefficient of variation. The relative efficiency of the experiment, when using the security regret strategy, was examined as a function of the number of center points, and was found to be best when the design used one center point.
-
NASA Astrophysics Data System (ADS)
Bukhari, W.; Hong, S.-M.
2016-03-01
The prediction as well as the gating of respiratory motion have received much attention over the last two decades for reducing the targeting error of the radiation treatment beam due to respiratory motion. In this article, we present a real-time algorithm for predicting respiratory motion in 3D space and realizing a gating function without pre-specifying a particular phase of the patient’s breathing cycle. The algorithm, named EKF-GPRN+ , first employs an extended Kalman filter (EKF) independently along each coordinate to predict the respiratory motion and then uses a Gaussian process regression network (GPRN) to correct the prediction error of the EKF in 3D space. The GPRN is a nonparametric Bayesian algorithm for modeling input-dependent correlations between the output variables in multi-output regression. Inference in GPRN is intractable and we employ variational inference with mean field approximation to compute an approximate predictive mean and predictive covariance matrix. The approximate predictive mean is used to correct the prediction error of the EKF. The trace of the approximate predictive covariance matrix is utilized to capture the uncertainty in EKF-GPRN+ prediction error and systematically identify breathing points with a higher probability of large prediction error in advance. This identification enables us to pause the treatment beam over such instances. EKF-GPRN+ implements a gating function by using simple calculations based on the trace of the predictive covariance matrix. Extensive numerical experiments are performed based on a large database of 304 respiratory motion traces to evaluate EKF-GPRN+ . The experimental results show that the EKF-GPRN+ algorithm reduces the patient-wise prediction error to 38%, 40% and 40% in root-mean-square, compared to no prediction, at lookahead lengths of 192 ms, 384 ms and 576 ms, respectively. The EKF-GPRN+ algorithm can further reduce the prediction error by employing the gating function, albeit at the cost of reduced duty cycle. The error reduction allows the clinical target volume to planning target volume (CTV-PTV) margin to be reduced, leading to decreased normal-tissue toxicity and possible dose escalation. The CTV-PTV margin is also evaluated to quantify clinical benefits of EKF-GPRN+ prediction.
-
TOPEX/POSEIDON orbit maintenance maneuver design
NASA Technical Reports Server (NTRS)
Bhat, R. S.; Frauenholz, R. B.; Cannell, Patrick E.
1990-01-01
The Ocean Topography Experiment (TOPEX/POSEIDON) mission orbit requirements are outlined, as well as its control and maneuver spacing requirements including longitude and time targeting. A ground-track prediction model dealing with geopotential, luni-solar gravity, and atmospheric-drag perturbations is considered. Targeting with all modeled perturbations is discussed, and such ground-track prediction errors as initial semimajor axis, orbit-determination, maneuver-execution, and atmospheric-density modeling errors are assessed. A longitude targeting strategy for two extreme situations is investigated employing all modeled perturbations and prediction errors. It is concluded that atmospheric-drag modeling errors are the prevailing ground-track prediction error source early in the mission during high solar flux, and that low solar-flux levels expected late in the experiment stipulate smaller maneuver magnitudes.
-
NASA Technical Reports Server (NTRS)
Tuttle, M. E.; Brinson, H. F.
1986-01-01
The impact of flight error in measured viscoelastic parameters on subsequent long-term viscoelastic predictions is numerically evaluated using the Schapery nonlinear viscoelastic model. Of the seven Schapery parameters, the results indicated that long-term predictions were most sensitive to errors in the power law parameter n. Although errors in the other parameters were significant as well, errors in n dominated all other factors at long times. The process of selecting an appropriate short-term test cycle so as to insure an accurate long-term prediction was considered, and a short-term test cycle was selected using material properties typical for T300/5208 graphite-epoxy at 149 C. The process of selection is described, and its individual steps are itemized.
-
Dopamine prediction error responses integrate subjective value from different reward dimensions
Lak, Armin; Stauffer, William R.; Schultz, Wolfram
2014-01-01
Prediction error signals enable us to learn through experience. These experiences include economic choices between different rewards that vary along multiple dimensions. Therefore, an ideal way to reinforce economic choice is to encode a prediction error that reflects the subjective value integrated across these reward dimensions. Previous studies demonstrated that dopamine prediction error responses reflect the value of singular reward attributes that include magnitude, probability, and delay. Obviously, preferences between rewards that vary along one dimension are completely determined by the manipulated variable. However, it is unknown whether dopamine prediction error responses reflect the subjective value integrated from different reward dimensions. Here, we measured the preferences between rewards that varied along multiple dimensions, and as such could not be ranked according to objective metrics. Monkeys chose between rewards that differed in amount, risk, and type. Because their choices were complete and transitive, the monkeys chose “as if” they integrated different rewards and attributes into a common scale of value. The prediction error responses of single dopamine neurons reflected the integrated subjective value inferred from the choices, rather than the singular reward attributes. Specifically, amount, risk, and reward type modulated dopamine responses exactly to the extent that they influenced economic choices, even when rewards were vastly different, such as liquid and food. This prediction error response could provide a direct updating signal for economic values. PMID:24453218
-
Estimation of viscous dissipation in nanodroplet impact and spreading
NASA Astrophysics Data System (ADS)
Li, Xin-Hao; Zhang, Xiang-Xiong; Chen, Min
2015-05-01
The developments in nanocoating and nanospray technology have resulted in the increasing importance of the impact of micro-/nanoscale liquid droplets on solid surface. In this paper, the impact of a nanodroplet on a flat solid surface is examined using molecular dynamics simulations. The impact velocity ranges from 58 m/s to 1044 m/s, in accordance with the Weber number ranging from 0.62 to 200.02 and the Reynolds number ranging from 0.89 to 16.14. The obtained maximum spreading factors are compared with previous models in the literature. The predicted results from the previous models largely deviate from our simulation results, with mean relative errors up to 58.12%. The estimated viscous dissipation is refined to present a modified theoretical model, which reduces the mean relative error to 15.12% in predicting the maximum spreading factor for cases of nanodroplet impact.
-
Effects of a cochlear implant simulation on immediate memory in normal-hearing adults
Burkholder, Rose A.; Pisoni, David B.; Svirsky, Mario A.
2012-01-01
This study assessed the effects of stimulus misidentification and memory processing errors on immediate memory span in 25 normal-hearing adults exposed to degraded auditory input simulating signals provided by a cochlear implant. The identification accuracy of degraded digits in isolation was measured before digit span testing. Forward and backward digit spans were shorter when digits were degraded than when they were normal. Participants’ normal digit spans and their accuracy in identifying isolated digits were used to predict digit spans in the degraded speech condition. The observed digit spans in degraded conditions did not differ significantly from predicted digit spans. This suggests that the decrease in memory span is related primarily to misidentification of digits rather than memory processing errors related to cognitive load. These findings provide complementary information to earlier research on auditory memory span of listeners exposed to degraded speech either experimentally or as a consequence of a hearing-impairment. PMID:16317807
-
NASA Astrophysics Data System (ADS)
de Podesta, Michael; Bell, Stephanie; Underwood, Robin
2018-04-01
In both meteorological and metrological applications, it is well known that air temperature sensors are susceptible to radiative errors. However, it is not widely known that the radiative error measured by an air temperature sensor in flowing air depends upon the sensor diameter, with smaller sensors reporting values closer to true air temperature. This is not a transient effect related to sensor heat capacity, but a fluid-dynamical effect arising from heat and mass flow in cylindrical geometries. This result has been known historically and is in meteorology text books. However, its significance does not appear to be widely appreciated and, as a consequence, air temperature can be—and probably is being—widely mis-estimated. In this paper, we first review prior descriptions of the ‘sensor size’ effect from the metrological and meteorological literature. We develop a heat transfer model to describe the process for cylindrical sensors, and evaluate the predicted temperature error for a range of sensor sizes and air speeds. We compare these predictions with published predictions and measurements. We report measurements demonstrating this effect in two laboratories at NPL in which the air flow and temperature are exceptionally closely controlled. The results are consistent with the heat-transfer model, and show that the air temperature error is proportional to the square root of the sensor diameter and that, even under good laboratory conditions, it can exceed 0.1 °C for a 6 mm diameter sensor. We then consider the implications of this result. In metrological applications, errors of the order of 0.1 °C are significant, representing limiting uncertainties in dimensional and mass measurements. In meteorological applications, radiative errors can easily be much larger. But in both cases, an understanding of the diameter dependence allows assessment and correction of the radiative error using a multi-sensor technique.
-
Effects of parallel planning on agreement production.
Veenstra, Alma; Meyer, Antje S; Acheson, Daniel J
2015-11-01
An important issue in current psycholinguistics is how the time course of utterance planning affects the generation of grammatical structures. The current study investigated the influence of parallel activation of the components of complex noun phrases on the generation of subject-verb agreement. Specifically, the lexical interference account (Gillespie & Pearlmutter, 2011b; Solomon & Pearlmutter, 2004) predicts more agreement errors (i.e., attraction) for subject phrases in which the head and local noun mismatch in number (e.g., the apple next to the pears) when nouns are planned in parallel than when they are planned in sequence. We used a speeded picture description task that yielded sentences such as the apple next to the pears is red. The objects mentioned in the noun phrase were either semantically related or unrelated. To induce agreement errors, pictures sometimes mismatched in number. In order to manipulate the likelihood of parallel processing of the objects and to test the hypothesized relationship between parallel processing and the rate of agreement errors, the pictures were either placed close together or far apart. Analyses of the participants' eye movements and speech onset latencies indicated slower processing of the first object and stronger interference from the related (compared to the unrelated) second object in the close than in the far condition. Analyses of the agreement errors yielded an attraction effect, with more errors in mismatching than in matching conditions. However, the magnitude of the attraction effect did not differ across the close and far conditions. Thus, spatial proximity encouraged parallel processing of the pictures, which led to interference of the associated conceptual and/or lexical representation, but, contrary to the prediction, it did not lead to more attraction errors. Copyright © 2015 Elsevier B.V. All rights reserved.
-
McGregor, Heather R.; Pun, Henry C. H.; Buckingham, Gavin; Gribble, Paul L.
2016-01-01
The human sensorimotor system is routinely capable of making accurate predictions about an object's weight, which allows for energetically efficient lifts and prevents objects from being dropped. Often, however, poor predictions arise when the weight of an object can vary and sensory cues about object weight are sparse (e.g., picking up an opaque water bottle). The question arises, what strategies does the sensorimotor system use to make weight predictions when one is dealing with an object whose weight may vary? For example, does the sensorimotor system use a strategy that minimizes prediction error (minimal squared error) or one that selects the weight that is most likely to be correct (maximum a posteriori)? In this study we dissociated the predictions of these two strategies by having participants lift an object whose weight varied according to a skewed probability distribution. We found, using a small range of weight uncertainty, that four indexes of sensorimotor prediction (grip force rate, grip force, load force rate, and load force) were consistent with a feedforward strategy that minimizes the square of prediction errors. These findings match research in the visuomotor system, suggesting parallels in underlying processes. We interpret our findings within a Bayesian framework and discuss the potential benefits of using a minimal squared error strategy. NEW & NOTEWORTHY Using a novel experimental model of object lifting, we tested whether the sensorimotor system models the weight of objects by minimizing lifting errors or by selecting the statistically most likely weight. We found that the sensorimotor system minimizes the square of prediction errors for object lifting. This parallels the results of studies that investigated visually guided reaching, suggesting an overlap in the underlying mechanisms between tasks that involve different sensory systems. PMID:27760821
-
Holmes, John B; Dodds, Ken G; Lee, Michael A
2017-03-02
An important issue in genetic evaluation is the comparability of random effects (breeding values), particularly between pairs of animals in different contemporary groups. This is usually referred to as genetic connectedness. While various measures of connectedness have been proposed in the literature, there is general agreement that the most appropriate measure is some function of the prediction error variance-covariance matrix. However, obtaining the prediction error variance-covariance matrix is computationally demanding for large-scale genetic evaluations. Many alternative statistics have been proposed that avoid the computational cost of obtaining the prediction error variance-covariance matrix, such as counts of genetic links between contemporary groups, gene flow matrices, and functions of the variance-covariance matrix of estimated contemporary group fixed effects. In this paper, we show that a correction to the variance-covariance matrix of estimated contemporary group fixed effects will produce the exact prediction error variance-covariance matrix averaged by contemporary group for univariate models in the presence of single or multiple fixed effects and one random effect. We demonstrate the correction for a series of models and show that approximations to the prediction error matrix based solely on the variance-covariance matrix of estimated contemporary group fixed effects are inappropriate in certain circumstances. Our method allows for the calculation of a connectedness measure based on the prediction error variance-covariance matrix by calculating only the variance-covariance matrix of estimated fixed effects. Since the number of fixed effects in genetic evaluation is usually orders of magnitudes smaller than the number of random effect levels, the computational requirements for our method should be reduced.
-
Response Surface Modeling Using Multivariate Orthogonal Functions
NASA Technical Reports Server (NTRS)
Morelli, Eugene A.; DeLoach, Richard
2001-01-01
A nonlinear modeling technique was used to characterize response surfaces for non-dimensional longitudinal aerodynamic force and moment coefficients, based on wind tunnel data from a commercial jet transport model. Data were collected using two experimental procedures - one based on modem design of experiments (MDOE), and one using a classical one factor at a time (OFAT) approach. The nonlinear modeling technique used multivariate orthogonal functions generated from the independent variable data as modeling functions in a least squares context to characterize the response surfaces. Model terms were selected automatically using a prediction error metric. Prediction error bounds computed from the modeling data alone were found to be- a good measure of actual prediction error for prediction points within the inference space. Root-mean-square model fit error and prediction error were less than 4 percent of the mean response value in all cases. Efficacy and prediction performance of the response surface models identified from both MDOE and OFAT experiments were investigated.
-
García-García, Isabel; Zeighami, Yashar; Dagher, Alain
2017-06-01
Surprises are important sources of learning. Cognitive scientists often refer to surprises as "reward prediction errors," a parameter that captures discrepancies between expectations and actual outcomes. Here, we integrate neurophysiological and functional magnetic resonance imaging (fMRI) results addressing the processing of reward prediction errors and how they might be altered in drug addiction and Parkinson's disease. By increasing phasic dopamine responses, drugs might accentuate prediction error signals, causing increases in fMRI activity in mesolimbic areas in response to drugs. Chronic substance dependence, by contrast, has been linked with compromised dopaminergic function, which might be associated with blunted fMRI responses to pleasant non-drug stimuli in mesocorticolimbic areas. In Parkinson's disease, dopamine replacement therapies seem to induce impairments in learning from negative outcomes. The present review provides a holistic overview of reward prediction errors across different pathologies and might inform future clinical strategies targeting impulsive/compulsive disorders.
-
ERIC Educational Resources Information Center
Farmer, Richard F.; Rucklidge, Julia J.
2006-01-01
Several hypotheses related to Newman's (e.g., Patterson & Newman, 1993) response modulation hypothesis were examined among adolescents with attention-deficit/hyperactivity disorder (ADHD; n = 18) and normal controls (n = 23). Consistent with predictions, youth with ADHD committed more passive avoidance errors (PAEs) than controls during the latter…
-
Li, Wen-bing; Yao, Lin-tao; Liu, Mu-hua; Huang, Lin; Yao, Ming-yin; Chen, Tian-bing; He, Xiu-wen; Yang, Ping; Hu, Hui-qin; Nie, Jiang-hui
2015-05-01
Cu in navel orange was detected rapidly by laser-induced breakdown spectroscopy (LIBS) combined with partial least squares (PLS) for quantitative analysis, then the effect on the detection accuracy of the model with different spectral data ptetreatment methods was explored. Spectral data for the 52 Gannan navel orange samples were pretreated by different data smoothing, mean centralized and standard normal variable transform. Then 319~338 nm wavelength section containing characteristic spectral lines of Cu was selected to build PLS models, the main evaluation indexes of models such as regression coefficient (r), root mean square error of cross validation (RMSECV) and the root mean square error of prediction (RMSEP) were compared and analyzed. Three indicators of PLS model after 13 points smoothing and processing of the mean center were found reaching 0. 992 8, 3. 43 and 3. 4 respectively, the average relative error of prediction model is only 5. 55%, and in one word, the quality of calibration and prediction of this model are the best results. The results show that selecting the appropriate data pre-processing method, the prediction accuracy of PLS quantitative model of fruits and vegetables detected by LIBS can be improved effectively, providing a new method for fast and accurate detection of fruits and vegetables by LIBS.
-
The role of prediction in social neuroscience
Brown, Elliot C.; Brüne, Martin
2012-01-01
Research has shown that the brain is constantly making predictions about future events. Theories of prediction in perception, action and learning suggest that the brain serves to reduce the discrepancies between expectation and actual experience, i.e., by reducing the prediction error. Forward models of action and perception propose the generation of a predictive internal representation of the expected sensory outcome, which is matched to the actual sensory feedback. Shared neural representations have been found when experiencing one's own and observing other's actions, rewards, errors, and emotions such as fear and pain. These general principles of the “predictive brain” are well established and have already begun to be applied to social aspects of cognition. The application and relevance of these predictive principles to social cognition are discussed in this article. Evidence is presented to argue that simple non-social cognitive processes can be extended to explain complex cognitive processes required for social interaction, with common neural activity seen for both social and non-social cognitions. A number of studies are included which demonstrate that bottom-up sensory input and top-down expectancies can be modulated by social information. The concept of competing social forward models and a partially distinct category of social prediction errors are introduced. The evolutionary implications of a “social predictive brain” are also mentioned, along with the implications on psychopathology. The review presents a number of testable hypotheses and novel comparisons that aim to stimulate further discussion and integration between currently disparate fields of research, with regard to computational models, behavioral and neurophysiological data. This promotes a relatively new platform for inquiry in social neuroscience with implications in social learning, theory of mind, empathy, the evolution of the social brain, and potential strategies for treating social cognitive deficits. PMID:22654749
-
NASA Astrophysics Data System (ADS)
Qian, Xiaoshan
2018-01-01
The traditional model of evaporation process parameters have continuity and cumulative characteristics of the prediction error larger issues, based on the basis of the process proposed an adaptive particle swarm neural network forecasting method parameters established on the autoregressive moving average (ARMA) error correction procedure compensated prediction model to predict the results of the neural network to improve prediction accuracy. Taking a alumina plant evaporation process to analyze production data validation, and compared with the traditional model, the new model prediction accuracy greatly improved, can be used to predict the dynamic process of evaporation of sodium aluminate solution components.
-
Bonfiglio, Luca; Minichilli, Fabrizio; Cantore, Nicoletta; Carboncini, Maria Chiara; Piccotti, Emily; Rossi, Bruno
2016-01-01
Modulation of frontal midline theta (fmθ) is observed during error commission, but little is known about the role of theta oscillations in correcting motor behaviours. We investigate EEG activity of healthy partipants executing a reaching task under variable degrees of prism-induced visuo-motor distortion and visual occlusion of the initial arm trajectory. This task introduces directional errors of different magnitudes. The discrepancy between predicted and actual movement directions (i.e. the error), at the time when visual feedback (hand appearance) became available, elicits a signal that triggers on-line movement correction. Analysis were performed on 25 EEG channels. For each participant, the median value of the angular error of all reaching trials was used to partition the EEG epochs into high- and low-error conditions. We computed event-related spectral perturbations (ERSP) time-locked either to visual feedback or to the onset of movement correction. ERSP time-locked to the onset of visual feedback showed that fmθ increased in the high- but not in the low-error condition with an approximate time lag of 200 ms. Moreover, when single epochs were sorted by the degree of motor error, fmθ started to increase when a certain level of error was exceeded and, then, scaled with error magnitude. When ERSP were time-locked to the onset of movement correction, the fmθ increase anticipated this event with an approximate time lead of 50 ms. During successive trials, an error reduction was observed which was associated with indices of adaptations (i.e., aftereffects) suggesting the need to explore if theta oscillations may facilitate learning. To our knowledge this is the first study where the EEG signal recorded during reaching movements was time-locked to the onset of the error visual feedback. This allowed us to conclude that theta oscillations putatively generated by anterior cingulate cortex activation are implicated in error processing in semi-naturalistic motor behaviours. PMID:26963919
-
Arrighi, Pieranna; Bonfiglio, Luca; Minichilli, Fabrizio; Cantore, Nicoletta; Carboncini, Maria Chiara; Piccotti, Emily; Rossi, Bruno; Andre, Paolo
2016-01-01
Modulation of frontal midline theta (fmθ) is observed during error commission, but little is known about the role of theta oscillations in correcting motor behaviours. We investigate EEG activity of healthy partipants executing a reaching task under variable degrees of prism-induced visuo-motor distortion and visual occlusion of the initial arm trajectory. This task introduces directional errors of different magnitudes. The discrepancy between predicted and actual movement directions (i.e. the error), at the time when visual feedback (hand appearance) became available, elicits a signal that triggers on-line movement correction. Analysis were performed on 25 EEG channels. For each participant, the median value of the angular error of all reaching trials was used to partition the EEG epochs into high- and low-error conditions. We computed event-related spectral perturbations (ERSP) time-locked either to visual feedback or to the onset of movement correction. ERSP time-locked to the onset of visual feedback showed that fmθ increased in the high- but not in the low-error condition with an approximate time lag of 200 ms. Moreover, when single epochs were sorted by the degree of motor error, fmθ started to increase when a certain level of error was exceeded and, then, scaled with error magnitude. When ERSP were time-locked to the onset of movement correction, the fmθ increase anticipated this event with an approximate time lead of 50 ms. During successive trials, an error reduction was observed which was associated with indices of adaptations (i.e., aftereffects) suggesting the need to explore if theta oscillations may facilitate learning. To our knowledge this is the first study where the EEG signal recorded during reaching movements was time-locked to the onset of the error visual feedback. This allowed us to conclude that theta oscillations putatively generated by anterior cingulate cortex activation are implicated in error processing in semi-naturalistic motor behaviours.
-
Prediction-error variance in Bayesian model updating: a comparative study
NASA Astrophysics Data System (ADS)
Asadollahi, Parisa; Li, Jian; Huang, Yong
2017-04-01
In Bayesian model updating, the likelihood function is commonly formulated by stochastic embedding in which the maximum information entropy probability model of prediction error variances plays an important role and it is Gaussian distribution subject to the first two moments as constraints. The selection of prediction error variances can be formulated as a model class selection problem, which automatically involves a trade-off between the average data-fit of the model class and the information it extracts from the data. Therefore, it is critical for the robustness in the updating of the structural model especially in the presence of modeling errors. To date, three ways of considering prediction error variances have been seem in the literature: 1) setting constant values empirically, 2) estimating them based on the goodness-of-fit of the measured data, and 3) updating them as uncertain parameters by applying Bayes' Theorem at the model class level. In this paper, the effect of different strategies to deal with the prediction error variances on the model updating performance is investigated explicitly. A six-story shear building model with six uncertain stiffness parameters is employed as an illustrative example. Transitional Markov Chain Monte Carlo is used to draw samples of the posterior probability density function of the structure model parameters as well as the uncertain prediction variances. The different levels of modeling uncertainty and complexity are modeled through three FE models, including a true model, a model with more complexity, and a model with modeling error. Bayesian updating is performed for the three FE models considering the three aforementioned treatments of the prediction error variances. The effect of number of measurements on the model updating performance is also examined in the study. The results are compared based on model class assessment and indicate that updating the prediction error variances as uncertain parameters at the model class level produces more robust results especially when the number of measurement is small.
-
Muscle Synergies May Improve Optimization Prediction of Knee Contact Forces During Walking
Walter, Jonathan P.; Kinney, Allison L.; Banks, Scott A.; D'Lima, Darryl D.; Besier, Thor F.; Lloyd, David G.; Fregly, Benjamin J.
2014-01-01
The ability to predict patient-specific joint contact and muscle forces accurately could improve the treatment of walking-related disorders. Muscle synergy analysis, which decomposes a large number of muscle electromyographic (EMG) signals into a small number of synergy control signals, could reduce the dimensionality and thus redundancy of the muscle and contact force prediction process. This study investigated whether use of subject-specific synergy controls can improve optimization prediction of knee contact forces during walking. To generate the predictions, we performed mixed dynamic muscle force optimizations (i.e., inverse skeletal dynamics with forward muscle activation and contraction dynamics) using data collected from a subject implanted with a force-measuring knee replacement. Twelve optimization problems (three cases with four subcases each) that minimized the sum of squares of muscle excitations were formulated to investigate how synergy controls affect knee contact force predictions. The three cases were: (1) Calibrate+Match where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously matched, (2) Precalibrate+Predict where experimental knee contact forces were predicted using precalibrated muscle model parameters values from the first case, and (3) Calibrate+Predict where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously predicted, all while matching inverse dynamic loads at the hip, knee, and ankle. The four subcases used either 44 independent controls or five synergy controls with and without EMG shape tracking. For the Calibrate+Match case, all four subcases closely reproduced the measured medial and lateral knee contact forces (R2 ≥ 0.94, root-mean-square (RMS) error < 66 N), indicating sufficient model fidelity for contact force prediction. For the Precalibrate+Predict and Calibrate+Predict cases, synergy controls yielded better contact force predictions (0.61 < R2 < 0.90, 83 N < RMS error < 161 N) than did independent controls (-0.15 < R2 < 0.79, 124 N < RMS error < 343 N) for corresponding subcases. For independent controls, contact force predictions improved when precalibrated model parameter values or EMG shape tracking was used. For synergy controls, contact force predictions were relatively insensitive to how model parameter values were calibrated, while EMG shape tracking made lateral (but not medial) contact force predictions worse. For the subject and optimization cost function analyzed in this study, use of subject-specific synergy controls improved the accuracy of knee contact force predictions, especially for lateral contact force when EMG shape tracking was omitted, and reduced prediction sensitivity to uncertainties in muscle model parameter values. PMID:24402438
-
Muscle synergies may improve optimization prediction of knee contact forces during walking.
Walter, Jonathan P; Kinney, Allison L; Banks, Scott A; D'Lima, Darryl D; Besier, Thor F; Lloyd, David G; Fregly, Benjamin J
2014-02-01
The ability to predict patient-specific joint contact and muscle forces accurately could improve the treatment of walking-related disorders. Muscle synergy analysis, which decomposes a large number of muscle electromyographic (EMG) signals into a small number of synergy control signals, could reduce the dimensionality and thus redundancy of the muscle and contact force prediction process. This study investigated whether use of subject-specific synergy controls can improve optimization prediction of knee contact forces during walking. To generate the predictions, we performed mixed dynamic muscle force optimizations (i.e., inverse skeletal dynamics with forward muscle activation and contraction dynamics) using data collected from a subject implanted with a force-measuring knee replacement. Twelve optimization problems (three cases with four subcases each) that minimized the sum of squares of muscle excitations were formulated to investigate how synergy controls affect knee contact force predictions. The three cases were: (1) Calibrate+Match where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously matched, (2) Precalibrate+Predict where experimental knee contact forces were predicted using precalibrated muscle model parameters values from the first case, and (3) Calibrate+Predict where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously predicted, all while matching inverse dynamic loads at the hip, knee, and ankle. The four subcases used either 44 independent controls or five synergy controls with and without EMG shape tracking. For the Calibrate+Match case, all four subcases closely reproduced the measured medial and lateral knee contact forces (R2 ≥ 0.94, root-mean-square (RMS) error < 66 N), indicating sufficient model fidelity for contact force prediction. For the Precalibrate+Predict and Calibrate+Predict cases, synergy controls yielded better contact force predictions (0.61 < R2 < 0.90, 83 N < RMS error < 161 N) than did independent controls (-0.15 < R2 < 0.79, 124 N < RMS error < 343 N) for corresponding subcases. For independent controls, contact force predictions improved when precalibrated model parameter values or EMG shape tracking was used. For synergy controls, contact force predictions were relatively insensitive to how model parameter values were calibrated, while EMG shape tracking made lateral (but not medial) contact force predictions worse. For the subject and optimization cost function analyzed in this study, use of subject-specific synergy controls improved the accuracy of knee contact force predictions, especially for lateral contact force when EMG shape tracking was omitted, and reduced prediction sensitivity to uncertainties in muscle model parameter values.
-
Seasonal to interannual Arctic sea ice predictability in current global climate models
NASA Astrophysics Data System (ADS)
Tietsche, S.; Day, J. J.; Guemas, V.; Hurlin, W. J.; Keeley, S. P. E.; Matei, D.; Msadek, R.; Collins, M.; Hawkins, E.
2014-02-01
We establish the first intermodel comparison of seasonal to interannual predictability of present-day Arctic climate by performing coordinated sets of idealized ensemble predictions with four state-of-the-art global climate models. For Arctic sea ice extent and volume, there is potential predictive skill for lead times of up to 3 years, and potential prediction errors have similar growth rates and magnitudes across the models. Spatial patterns of potential prediction errors differ substantially between the models, but some features are robust. Sea ice concentration errors are largest in the marginal ice zone, and in winter they are almost zero away from the ice edge. Sea ice thickness errors are amplified along the coasts of the Arctic Ocean, an effect that is dominated by sea ice advection. These results give an upper bound on the ability of current global climate models to predict important aspects of Arctic climate.
-
Evaluation and Applications of the Prediction of Intensity Model Error (PRIME) Model
NASA Astrophysics Data System (ADS)
Bhatia, K. T.; Nolan, D. S.; Demaria, M.; Schumacher, A.
2015-12-01
Forecasters and end users of tropical cyclone (TC) intensity forecasts would greatly benefit from a reliable expectation of model error to counteract the lack of consistency in TC intensity forecast performance. As a first step towards producing error predictions to accompany each TC intensity forecast, Bhatia and Nolan (2013) studied the relationship between synoptic parameters, TC attributes, and forecast errors. In this study, we build on previous results of Bhatia and Nolan (2013) by testing the ability of the Prediction of Intensity Model Error (PRIME) model to forecast the absolute error and bias of four leading intensity models available for guidance in the Atlantic basin. PRIME forecasts are independently evaluated at each 12-hour interval from 12 to 120 hours during the 2007-2014 Atlantic hurricane seasons. The absolute error and bias predictions of PRIME are compared to their respective climatologies to determine their skill. In addition to these results, we will present the performance of the operational version of PRIME run during the 2015 hurricane season. PRIME verification results show that it can reliably anticipate situations where particular models excel, and therefore could lead to a more informed protocol for hurricane evacuations and storm preparations. These positive conclusions suggest that PRIME forecasts also have the potential to lower the error in the original intensity forecasts of each model. As a result, two techniques are proposed to develop a post-processing procedure for a multimodel ensemble based on PRIME. The first approach is to inverse-weight models using PRIME absolute error predictions (higher predicted absolute error corresponds to lower weights). The second multimodel ensemble applies PRIME bias predictions to each model's intensity forecast and the mean of the corrected models is evaluated. The forecasts of both of these experimental ensembles are compared to those of the equal-weight ICON ensemble, which currently provides the most reliable forecasts in the Atlantic basin.
-
Aberg, Kristoffer Carl; Doell, Kimberly C; Schwartz, Sophie
2015-10-28
Some individuals are better at learning about rewarding situations, whereas others are inclined to avoid punishments (i.e., enhanced approach or avoidance learning, respectively). In reinforcement learning, action values are increased when outcomes are better than predicted (positive prediction errors [PEs]) and decreased for worse than predicted outcomes (negative PEs). Because actions with high and low values are approached and avoided, respectively, individual differences in the neural encoding of PEs may influence the balance between approach-avoidance learning. Recent correlational approaches also indicate that biases in approach-avoidance learning involve hemispheric asymmetries in dopamine function. However, the computational and neural mechanisms underpinning such learning biases remain unknown. Here we assessed hemispheric reward asymmetry in striatal activity in 34 human participants who performed a task involving rewards and punishments. We show that the relative difference in reward response between hemispheres relates to individual biases in approach-avoidance learning. Moreover, using a computational modeling approach, we demonstrate that better encoding of positive (vs negative) PEs in dopaminergic midbrain regions is associated with better approach (vs avoidance) learning, specifically in participants with larger reward responses in the left (vs right) ventral striatum. Thus, individual dispositions or traits may be determined by neural processes acting to constrain learning about specific aspects of the world. Copyright © 2015 the authors 0270-6474/15/3514491-10$15.00/0.
-
Local-search based prediction of medical image registration error
NASA Astrophysics Data System (ADS)
Saygili, Görkem
2018-03-01
Medical image registration is a crucial task in many different medical imaging applications. Hence, considerable amount of work has been published recently that aim to predict the error in a registration without any human effort. If provided, these error predictions can be used as a feedback to the registration algorithm to further improve its performance. Recent methods generally start with extracting image-based and deformation-based features, then apply feature pooling and finally train a Random Forest (RF) regressor to predict the real registration error. Image-based features can be calculated after applying a single registration but provide limited accuracy whereas deformation-based features such as variation of deformation vector field may require up to 20 registrations which is a considerably high time-consuming task. This paper proposes to use extracted features from a local search algorithm as image-based features to estimate the error of a registration. The proposed method comprises a local search algorithm to find corresponding voxels between registered image pairs and based on the amount of shifts and stereo confidence measures, it predicts the amount of registration error in millimetres densely using a RF regressor. Compared to other algorithms in the literature, the proposed algorithm does not require multiple registrations, can be efficiently implemented on a Graphical Processing Unit (GPU) and can still provide highly accurate error predictions in existence of large registration error. Experimental results with real registrations on a public dataset indicate a substantially high accuracy achieved by using features from the local search algorithm.
-
Moisen, Gretchen G.; Freeman, E.A.; Blackard, J.A.; Frescino, T.S.; Zimmermann, N.E.; Edwards, T.C.
2006-01-01
Many efforts are underway to produce broad-scale forest attribute maps by modelling forest class and structure variables collected in forest inventories as functions of satellite-based and biophysical information. Typically, variants of classification and regression trees implemented in Rulequest's?? See5 and Cubist (for binary and continuous responses, respectively) are the tools of choice in many of these applications. These tools are widely used in large remote sensing applications, but are not easily interpretable, do not have ties with survey estimation methods, and use proprietary unpublished algorithms. Consequently, three alternative modelling techniques were compared for mapping presence and basal area of 13 species located in the mountain ranges of Utah, USA. The modelling techniques compared included the widely used See5/Cubist, generalized additive models (GAMs), and stochastic gradient boosting (SGB). Model performance was evaluated using independent test data sets. Evaluation criteria for mapping species presence included specificity, sensitivity, Kappa, and area under the curve (AUC). Evaluation criteria for the continuous basal area variables included correlation and relative mean squared error. For predicting species presence (setting thresholds to maximize Kappa), SGB had higher values for the majority of the species for specificity and Kappa, while GAMs had higher values for the majority of the species for sensitivity. In evaluating resultant AUC values, GAM and/or SGB models had significantly better results than the See5 models where significant differences could be detected between models. For nine out of 13 species, basal area prediction results for all modelling techniques were poor (correlations less than 0.5 and relative mean squared errors greater than 0.8), but SGB provided the most stable predictions in these instances. SGB and Cubist performed equally well for modelling basal area for three species with moderate prediction success, while all three modelling tools produced comparably good predictions (correlation of 0.68 and relative mean squared error of 0.56) for one species. ?? 2006 Elsevier B.V. All rights reserved.
-
Miaw, Carolina Sheng Whei; Assis, Camila; Silva, Alessandro Rangel Carolino Sales; Cunha, Maria Luísa; Sena, Marcelo Martins; de Souza, Scheilla Vitorino Carvalho
2018-07-15
Grape, orange, peach and passion fruit nectars were formulated and adulterated by dilution with syrup, apple and cashew juices at 10 levels for each adulterant. Attenuated total reflectance Fourier transform mid infrared (ATR-FTIR) spectra were obtained. Partial least squares (PLS) multivariate calibration models allied to different variable selection methods, such as interval partial least squares (iPLS), ordered predictors selection (OPS) and genetic algorithm (GA), were used to quantify the main fruits. PLS improved by iPLS-OPS variable selection showed the highest predictive capacity to quantify the main fruit contents. The selected variables in the final models varied from 72 to 100; the root mean square errors of prediction were estimated from 0.5 to 2.6%; the correlation coefficients of prediction ranged from 0.948 to 0.990; and, the mean relative errors of prediction varied from 3.0 to 6.7%. All of the developed models were validated. Copyright © 2018 Elsevier Ltd. All rights reserved.
-
Reliability study of biometrics "do not contact" in myopia.
Migliorini, R; Fratipietro, M; Comberiati, A M; Pattavina, L; Arrico, L
The aim of the study is a comparison between the actually achieved after surgery condition versus the expected refractive condition of the eye as calculated via a biometer. The study was conducted in a random group of 38 eyes of patients undergoing surgery by phacoemulsification. The mean absolute error was calculated between the predicted values from the measurements with the optical biometer and those obtained in the post-operative error which was at around 0.47% Our study shows results not far from those reported in the literature, and in relation, to the mean absolute error is among the lowest values at 0.47 ± 0.11 SEM.
-
Error reduction and parameter optimization of the TAPIR method for fast T1 mapping.
Zaitsev, M; Steinhoff, S; Shah, N J
2003-06-01
A methodology is presented for the reduction of both systematic and random errors in T(1) determination using TAPIR, a Look-Locker-based fast T(1) mapping technique. The relations between various sequence parameters were carefully investigated in order to develop recipes for choosing optimal sequence parameters. Theoretical predictions for the optimal flip angle were verified experimentally. Inversion pulse imperfections were identified as the main source of systematic errors in T(1) determination with TAPIR. An effective remedy is demonstrated which includes extension of the measurement protocol to include a special sequence for mapping the inversion efficiency itself. Copyright 2003 Wiley-Liss, Inc.
-
The Dopamine Prediction Error: Contributions to Associative Models of Reward Learning
Nasser, Helen M.; Calu, Donna J.; Schoenbaum, Geoffrey; Sharpe, Melissa J.
2017-01-01
Phasic activity of midbrain dopamine neurons is currently thought to encapsulate the prediction-error signal described in Sutton and Barto’s (1981) model-free reinforcement learning algorithm. This phasic signal is thought to contain information about the quantitative value of reward, which transfers to the reward-predictive cue after learning. This is argued to endow the reward-predictive cue with the value inherent in the reward, motivating behavior toward cues signaling the presence of reward. Yet theoretical and empirical research has implicated prediction-error signaling in learning that extends far beyond a transfer of quantitative value to a reward-predictive cue. Here, we review the research which demonstrates the complexity of how dopaminergic prediction errors facilitate learning. After briefly discussing the literature demonstrating that phasic dopaminergic signals can act in the manner described by Sutton and Barto (1981), we consider how these signals may also influence attentional processing across multiple attentional systems in distinct brain circuits. Then, we discuss how prediction errors encode and promote the development of context-specific associations between cues and rewards. Finally, we consider recent evidence that shows dopaminergic activity contains information about causal relationships between cues and rewards that reflect information garnered from rich associative models of the world that can be adapted in the absence of direct experience. In discussing this research we hope to support the expansion of how dopaminergic prediction errors are thought to contribute to the learning process beyond the traditional concept of transferring quantitative value. PMID:28275359
-
Kaus, Joseph W; Harder, Edward; Lin, Teng; Abel, Robert; McCammon, J Andrew; Wang, Lingle
2015-06-09
Recent advances in improved force fields and sampling methods have made it possible for the accurate calculation of protein–ligand binding free energies. Alchemical free energy perturbation (FEP) using an explicit solvent model is one of the most rigorous methods to calculate relative binding free energies. However, for cases where there are high energy barriers separating the relevant conformations that are important for ligand binding, the calculated free energy may depend on the initial conformation used in the simulation due to the lack of complete sampling of all the important regions in phase space. This is particularly true for ligands with multiple possible binding modes separated by high energy barriers, making it difficult to sample all relevant binding modes even with modern enhanced sampling methods. In this paper, we apply a previously developed method that provides a corrected binding free energy for ligands with multiple binding modes by combining the free energy results from multiple alchemical FEP calculations starting from all enumerated poses, and the results are compared with Glide docking and MM-GBSA calculations. From these calculations, the dominant ligand binding mode can also be predicted. We apply this method to a series of ligands that bind to c-Jun N-terminal kinase-1 (JNK1) and obtain improved free energy results. The dominant ligand binding modes predicted by this method agree with the available crystallography, while both Glide docking and MM-GBSA calculations incorrectly predict the binding modes for some ligands. The method also helps separate the force field error from the ligand sampling error, such that deviations in the predicted binding free energy from the experimental values likely indicate possible inaccuracies in the force field. An error in the force field for a subset of the ligands studied was identified using this method, and improved free energy results were obtained by correcting the partial charges assigned to the ligands. This improved the root-mean-square error (RMSE) for the predicted binding free energy from 1.9 kcal/mol with the original partial charges to 1.3 kcal/mol with the corrected partial charges.
-
2016-01-01
Recent advances in improved force fields and sampling methods have made it possible for the accurate calculation of protein–ligand binding free energies. Alchemical free energy perturbation (FEP) using an explicit solvent model is one of the most rigorous methods to calculate relative binding free energies. However, for cases where there are high energy barriers separating the relevant conformations that are important for ligand binding, the calculated free energy may depend on the initial conformation used in the simulation due to the lack of complete sampling of all the important regions in phase space. This is particularly true for ligands with multiple possible binding modes separated by high energy barriers, making it difficult to sample all relevant binding modes even with modern enhanced sampling methods. In this paper, we apply a previously developed method that provides a corrected binding free energy for ligands with multiple binding modes by combining the free energy results from multiple alchemical FEP calculations starting from all enumerated poses, and the results are compared with Glide docking and MM-GBSA calculations. From these calculations, the dominant ligand binding mode can also be predicted. We apply this method to a series of ligands that bind to c-Jun N-terminal kinase-1 (JNK1) and obtain improved free energy results. The dominant ligand binding modes predicted by this method agree with the available crystallography, while both Glide docking and MM-GBSA calculations incorrectly predict the binding modes for some ligands. The method also helps separate the force field error from the ligand sampling error, such that deviations in the predicted binding free energy from the experimental values likely indicate possible inaccuracies in the force field. An error in the force field for a subset of the ligands studied was identified using this method, and improved free energy results were obtained by correcting the partial charges assigned to the ligands. This improved the root-mean-square error (RMSE) for the predicted binding free energy from 1.9 kcal/mol with the original partial charges to 1.3 kcal/mol with the corrected partial charges. PMID:26085821
-
NASA Astrophysics Data System (ADS)
Singh, K. S.; Bhaskaran, Prasad K.
2017-12-01
This study evaluates the performance of the Advanced Research Weather Research and Forecasting (WRF-ARW) model for prediction of land-falling Bay of Bengal (BoB) tropical cyclones (TCs). Model integration was performed using two-way interactive double nested domains at 27 and 9 km resolutions. The present study comprises two major components. Firstly, the study explores the impact of five different planetary boundary layer (PBL) and six cumulus convection (CC) schemes on seven land-falling BoB TCs. A total of 85 numerical simulations were studied in detail, and the results signify that the model simulated better both the track and intensity by using a combination of Yonsei University (YSU) PBL and the old simplified Arakawa-Schubert CC scheme. Secondly, the study also investigated the model performance based on the best possible combinations of model physics on the real-time forecasts of four BoB cyclones (Phailin, Helen, Lehar, and Madi) that made landfall during 2013 based on another 15 numerical simulations. The predicted mean track error during 2013 was about 71 km, 114 km, 133 km, 148 km, and 130 km respectively from day-1 to day-5. The Root Mean Square Error (RMSE) for Minimum Central Pressure (MCP) was about 6 hPa and the same noticed for Maximum Surface Wind (MSW) was about 4.5 m s-1 noticed during the entire simulation period. In addition the study also reveals that the predicted track errors during 2013 cyclones improved respectively by 43%, 44%, and 52% from day-1 to day-3 as compared to cyclones simulated during the period 2006-2011. The improvements noticed can be attributed due to relatively better quality data that was specified for the initial mean position error (about 48 km) during 2013. Overall the study signifies that the track and intensity forecast for 2013 cyclones using the specified combinations listed in the first part of this study performed relatively better than the other NWP (Numerical Weather Prediction) models, and thereby finds application in real-time forecast.
-
NASA Astrophysics Data System (ADS)
Xu, Y.; Jones, A. D.; Rhoades, A.
2017-12-01
Precipitation is a key component in hydrologic cycles, and changing precipitation regimes contribute to more intense and frequent drought and flood events around the world. Numerical climate modeling is a powerful tool to study climatology and to predict future changes. Despite the continuous improvement in numerical models, long-term precipitation prediction remains a challenge especially at regional scales. To improve numerical simulations of precipitation, it is important to find out where the uncertainty in precipitation simulations comes from. There are two types of uncertainty in numerical model predictions. One is related to uncertainty in the input data, such as model's boundary and initial conditions. These uncertainties would propagate to the final model outcomes even if the numerical model has exactly replicated the true world. But a numerical model cannot exactly replicate the true world. Therefore, the other type of model uncertainty is related the errors in the model physics, such as the parameterization of sub-grid scale processes, i.e., given precise input conditions, how much error could be generated by the in-precise model. Here, we build two statistical models based on a neural network algorithm to predict long-term variation of precipitation over California: one uses "true world" information derived from observations, and the other uses "modeled world" information using model inputs and outputs from the North America Coordinated Regional Downscaling Project (NA CORDEX). We derive multiple climate feature metrics as the predictors for the statistical model to represent the impact of global climate on local hydrology, and include topography as a predictor to represent the local control. We first compare the predictors between the true world and the modeled world to determine the errors contained in the input data. By perturbing the predictors in the statistical model, we estimate how much uncertainty in the model's final outcomes is accounted for by each predictor. By comparing the statistical model derived from true world information and modeled world information, we assess the errors lying in the physics of the numerical models. This work provides a unique insight to assess the performance of numerical climate models, and can be used to guide improvement of precipitation prediction.
-
Dell, Gary S.; Martin, Nadine; Schwartz, Myrna F.
2010-01-01
Lexical access in language production, and particularly pathologies of lexical access, are often investigated by examining errors in picture naming and word repetition. In this article, we test a computational approach to lexical access, the two-step interactive model, by examining whether the model can quantitatively predict the repetition-error patterns of 65 aphasic subjects from their naming errors. The model’s characterizations of the subjects’ naming errors were taken from the companion paper to this one (Schwartz, Dell, N. Martin, Gahl & Sobel, 2006), and their repetition was predicted from the model on the assumption that naming involves two error prone steps, word and phonological retrieval, whereas repetition only creates errors in the second of these steps. A version of the model in which lexical-semantic and lexical-phonological connections could be independently lesioned was generally successful in predicting repetition for the aphasics. An analysis of the few cases in which model predictions were inaccurate revealed the role of input phonology in the repetition task. PMID:21085621
-
Gray, Rob; Orn, Anders; Woodman, Tim
2017-02-01
Are pressure-induced performance errors in experts associated with novice-like skill execution (as predicted by reinvestment/conscious processing theories) or expert execution toward a result that the performer typically intends to avoid (as predicted by ironic processes theory)? The present study directly compared these predictions using a baseball pitching task with two groups of experienced pitchers. One group was shown only their target, while the other group was shown the target and an ironic (avoid) zone. Both groups demonstrated significantly fewer target hits under pressure. For the target-only group, this was accompanied by significant changes in expertise-related kinematic variables. In the ironic group, the number of pitches thrown in the ironic zone was significantly higher under pressure, and there were no significant changes in kinematics. These results suggest that information about an opponent can influence the mechanisms underlying pressure-induced performance errors.
-
Models for H₃ receptor antagonist activity of sulfonylurea derivatives.
Khatri, Naveen; Madan, A K
2014-03-01
The histamine H₃ receptor has been perceived as an auspicious target for the treatment of various central and peripheral nervous system diseases. In present study, a wide variety of 60 2D and 3D molecular descriptors (MDs) were successfully utilized for the development of models for the prediction of antagonist activity of sulfonylurea derivatives for histamine H₃ receptors. Models were developed through decision tree (DT), random forest (RF) and moving average analysis (MAA). Dragon software version 6.0.28 was employed for calculation of values of diverse MDs of each analogue involved in the data set. The DT classified and correctly predicted the input data with an impressive non-error rate of 94% in the training set and 82.5% during cross validation. RF correctly classified the analogues into active and inactive with a non-error rate of 79.3%. The MAA based models predicted the antagonist histamine H₃ receptor activity with non-error rate up to 90%. Active ranges of the proposed MAA based models not only exhibited high potency but also showed improved safety as indicated by relatively high values of selectivity index. The statistical significance of the models was assessed through sensitivity, specificity, non-error rate, Matthew's correlation coefficient and intercorrelation analysis. Proposed models offer vast potential for providing lead structures for development of potent but safe H₃ receptor antagonist sulfonylurea derivatives. Copyright © 2013 Elsevier Inc. All rights reserved.
-
Parametric decadal climate forecast recalibration (DeFoReSt 1.0)
NASA Astrophysics Data System (ADS)
Pasternack, Alexander; Bhend, Jonas; Liniger, Mark A.; Rust, Henning W.; Müller, Wolfgang A.; Ulbrich, Uwe
2018-01-01
Near-term climate predictions such as decadal climate forecasts are increasingly being used to guide adaptation measures. For near-term probabilistic predictions to be useful, systematic errors of the forecasting systems have to be corrected. While methods for the calibration of probabilistic forecasts are readily available, these have to be adapted to the specifics of decadal climate forecasts including the long time horizon of decadal climate forecasts, lead-time-dependent systematic errors (drift) and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typically pairs of reforecasts and observations are available to estimate calibration parameters. We introduce the Decadal Climate Forecast Recalibration Strategy (DeFoReSt), a parametric approach to recalibrate decadal ensemble forecasts that takes the above specifics into account. DeFoReSt optimizes forecast quality as measured by the continuous ranked probability score (CRPS). Using a toy model to generate synthetic forecast observation pairs, we demonstrate the positive effect on forecast quality in situations with pronounced and limited predictability. Finally, we apply DeFoReSt to decadal surface temperature forecasts from the MiKlip prototype system and find consistent, and sometimes considerable, improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.
-
NASA Astrophysics Data System (ADS)
Kierkels, R. G. J.; den Otter, L. A.; Korevaar, E. W.; Langendijk, J. A.; van der Schaaf, A.; Knopf, A. C.; Sijtsema, N. M.
2018-02-01
A prerequisite for adaptive dose-tracking in radiotherapy is the assessment of the deformable image registration (DIR) quality. In this work, various metrics that quantify DIR uncertainties are investigated using realistic deformation fields of 26 head and neck and 12 lung cancer patients. Metrics related to the physiologically feasibility (the Jacobian determinant, harmonic energy (HE), and octahedral shear strain (OSS)) and numerically robustness of the deformation (the inverse consistency error (ICE), transitivity error (TE), and distance discordance metric (DDM)) were investigated. The deformable registrations were performed using a B-spline transformation model. The DIR error metrics were log-transformed and correlated (Pearson) against the log-transformed ground-truth error on a voxel level. Correlations of r ⩾ 0.5 were found for the DDM and HE. Given a DIR tolerance threshold of 2.0 mm and a negative predictive value of 0.90, the DDM and HE thresholds were 0.49 mm and 0.014, respectively. In conclusion, the log-transformed DDM and HE can be used to identify voxels at risk for large DIR errors with a large negative predictive value. The HE and/or DDM can therefore be used to perform automated quality assurance of each CT-based DIR for head and neck and lung cancer patients.
-
Allan Cheyne, J; Solman, Grayden J F; Carriere, Jonathan S A; Smilek, Daniel
2009-04-01
We present arguments and evidence for a three-state attentional model of task engagement/disengagement. The model postulates three states of mind-wandering: occurrent task inattention, generic task inattention, and response disengagement. We hypothesize that all three states are both causes and consequences of task performance outcomes and apply across a variety of experimental and real-world tasks. We apply this model to the analysis of a widely used GO/NOGO task, the Sustained Attention to Response Task (SART). We identify three performance characteristics of the SART that map onto the three states of the model: RT variability, anticipations, and omissions. Predictions based on the model are tested, and largely corroborated, via regression and lag-sequential analyses of both successful and unsuccessful withholding on NOGO trials as well as self-reported mind-wandering and everyday cognitive errors. The results revealed theoretically consistent temporal associations among the state indicators and between these and SART errors as well as with self-report measures. Lag analysis was consistent with the hypotheses that temporal transitions among states are often extremely abrupt and that the association between mind-wandering and performance is bidirectional. The bidirectional effects suggest that errors constitute important occasions for reactive mind-wandering. The model also enables concrete phenomenological, behavioral, and physiological predictions for future research.
-
Vlasceanu, Madalina; Drach, Rae; Coman, Alin
2018-05-03
The mind is a prediction machine. In most situations, it has expectations as to what might happen. But when predictions are invalidated by experience (i.e., prediction errors), the memories that generate these predictions are suppressed. Here, we explore the effect of prediction error on listeners' memories following social interaction. We find that listening to a speaker recounting experiences similar to one's own triggers prediction errors on the part of the listener that lead to the suppression of her memories. This effect, we show, is sensitive to a perspective-taking manipulation, such that individuals who are instructed to take the perspective of the speaker experience memory suppression, whereas individuals who undergo a low-perspective-taking manipulation fail to show a mnemonic suppression effect. We discuss the relevance of these findings for our understanding of the bidirectional influences between cognition and social contexts, as well as for the real-world situations that involve memory-based predictions.
-
NASA Astrophysics Data System (ADS)
Szeląg, Bartosz; Barbusiński, Krzysztof; Studziński, Jan; Bartkiewicz, Lidia
2017-11-01
In the study, models developed using data mining methods are proposed for predicting wastewater quality indicators: biochemical and chemical oxygen demand, total suspended solids, total nitrogen and total phosphorus at the inflow to wastewater treatment plant (WWTP). The models are based on values measured in previous time steps and daily wastewater inflows. Also, independent prediction systems that can be used in case of monitoring devices malfunction are provided. Models of wastewater quality indicators were developed using MARS (multivariate adaptive regression spline) method, artificial neural networks (ANN) of the multilayer perceptron type combined with the classification model (SOM) and cascade neural networks (CNN). The lowest values of absolute and relative errors were obtained using ANN+SOM, whereas the MARS method produced the highest error values. It was shown that for the analysed WWTP it is possible to obtain continuous prediction of selected wastewater quality indicators using the two developed independent prediction systems. Such models can ensure reliable WWTP work when wastewater quality monitoring systems become inoperable, or are under maintenance.
-
Performance of univariate forecasting on seasonal diseases: the case of tuberculosis.
Permanasari, Adhistya Erna; Rambli, Dayang Rohaya Awang; Dominic, P Dhanapal Durai
2011-01-01
The annual disease incident worldwide is desirable to be predicted for taking appropriate policy to prevent disease outbreak. This chapter considers the performance of different forecasting method to predict the future number of disease incidence, especially for seasonal disease. Six forecasting methods, namely linear regression, moving average, decomposition, Holt-Winter's, ARIMA, and artificial neural network (ANN), were used for disease forecasting on tuberculosis monthly data. The model derived met the requirement of time series with seasonality pattern and downward trend. The forecasting performance was compared using similar error measure in the base of the last 5 years forecast result. The findings indicate that ARIMA model was the most appropriate model since it obtained the less relatively error than the other model.
-
Peak flood estimation using gene expression programming
NASA Astrophysics Data System (ADS)
Zorn, Conrad R.; Shamseldin, Asaad Y.
2015-12-01
As a case study for the Auckland Region of New Zealand, this paper investigates the potential use of gene-expression programming (GEP) in predicting specific return period events in comparison to the established and widely used Regional Flood Estimation (RFE) method. Initially calibrated to 14 gauged sites, the GEP derived model was further validated to 10 and 100 year flood events with a relative errors of 29% and 18%, respectively. This is compared to the RFE method providing 48% and 44% errors for the same flood events. While the effectiveness of GEP in predicting specific return period events is made apparent, it is argued that the derived equations should be used in conjunction with those existing methodologies rather than as a replacement.
-
Assumption-free estimation of the genetic contribution to refractive error across childhood.
Guggenheim, Jeremy A; St Pourcain, Beate; McMahon, George; Timpson, Nicholas J; Evans, David M; Williams, Cathy
2015-01-01
Studies in relatives have generally yielded high heritability estimates for refractive error: twins 75-90%, families 15-70%. However, because related individuals often share a common environment, these estimates are inflated (via misallocation of unique/common environment variance). We calculated a lower-bound heritability estimate for refractive error free from such bias. Between the ages 7 and 15 years, participants in the Avon Longitudinal Study of Parents and Children (ALSPAC) underwent non-cycloplegic autorefraction at regular research clinics. At each age, an estimate of the variance in refractive error explained by single nucleotide polymorphism (SNP) genetic variants was calculated using genome-wide complex trait analysis (GCTA) using high-density genome-wide SNP genotype information (minimum N at each age=3,404). The variance in refractive error explained by the SNPs ("SNP heritability") was stable over childhood: Across age 7-15 years, SNP heritability averaged 0.28 (SE=0.08, p<0.001). The genetic correlation for refractive error between visits varied from 0.77 to 1.00 (all p<0.001) demonstrating that a common set of SNPs was responsible for the genetic contribution to refractive error across this period of childhood. Simulations suggested lack of cycloplegia during autorefraction led to a small underestimation of SNP heritability (adjusted SNP heritability=0.35; SE=0.09). To put these results in context, the variance in refractive error explained (or predicted) by the time participants spent outdoors was <0.005 and by the time spent reading was <0.01, based on a parental questionnaire completed when the child was aged 8-9 years old. Genetic variation captured by common SNPs explained approximately 35% of the variation in refractive error between unrelated subjects. This value sets an upper limit for predicting refractive error using existing SNP genotyping arrays, although higher-density genotyping in larger samples and inclusion of interaction effects is expected to raise this figure toward twin- and family-based heritability estimates. The same SNPs influenced refractive error across much of childhood. Notwithstanding the strong evidence of association between time outdoors and myopia, and time reading and myopia, less than 1% of the variance in myopia at age 15 was explained by crude measures of these two risk factors, indicating that their effects may be limited, at least when averaged over the whole population.
-
Fisher, Moria E; Huang, Felix C; Wright, Zachary A; Patton, James L
2014-01-01
Manipulation of error feedback has been of great interest to recent studies in motor control and rehabilitation. Typically, motor adaptation is shown as a change in performance with a single scalar metric for each trial, yet such an approach might overlook details about how error evolves through the movement. We believe that statistical distributions of movement error through the extent of the trajectory can reveal unique patterns of adaption and possibly reveal clues to how the motor system processes information about error. This paper describes different possible ordinate domains, focusing on representations in time and state-space, used to quantify reaching errors. We hypothesized that the domain with the lowest amount of variability would lead to a predictive model of reaching error with the highest accuracy. Here we showed that errors represented in a time domain demonstrate the least variance and allow for the highest predictive model of reaching errors. These predictive models will give rise to more specialized methods of robotic feedback and improve previous techniques of error augmentation.
-
NASA Astrophysics Data System (ADS)
Romano, M.; Mays, M. L.; Taktakishvili, A.; MacNeice, P. J.; Zheng, Y.; Pulkkinen, A. A.; Kuznetsova, M. M.; Odstrcil, D.
2013-12-01
Modeling coronal mass ejections (CMEs) is of great interest to the space weather research and forecasting communities. We present recent validation work of real-time CME arrival time predictions at different satellites using the WSA-ENLIL+Cone three-dimensional MHD heliospheric model available at the Community Coordinated Modeling Center (CCMC) and performed by the Space Weather Research Center (SWRC). SWRC is an in-house research-based operations team at the CCMC which provides interplanetary space weather forecasting for NASA's robotic missions and performs real-time model validation. The quality of model operation is evaluated by comparing its output to a measurable parameter of interest such as the CME arrival time and geomagnetic storm strength. The Kp index is calculated from the relation given in Newell et al. (2007), using solar wind parameters predicted by the WSA-ENLIL+Cone model at Earth. The CME arrival time error is defined as the difference between the predicted arrival time and the observed in-situ CME shock arrival time at the ACE, STEREO A, or STEREO B spacecraft. This study includes all real-time WSA-ENLIL+Cone model simulations performed between June 2011-2013 (over 400 runs) at the CCMC/SWRC. We report hit, miss, false alarm, and correct rejection statistics for all three spacecraft. For hits we show the average absolute CME arrival time error, and the dependence of this error on CME input parameters such as speed, width, and direction. We also present the predicted geomagnetic storm strength (using the Kp index) error for Earth-directed CMEs.
-
Poster - 49: Assessment of Synchrony respiratory compensation error for CyberKnife liver treatment
DOE Office of Scientific and Technical Information (OSTI.GOV)
Liu, Ming; Cygler,
The goal of this work is to quantify respiratory motion compensation errors for liver tumor patients treated by the CyberKnife system with Synchrony tracking, to identify patients with the smallest tracking errors and to eventually help coach patient’s breathing patterns to minimize dose delivery errors. The accuracy of CyberKnife Synchrony respiratory motion compensation was assessed for 37 patients treated for liver lesions by analyzing data from system logfiles. A predictive model is used to modulate the direction of individual beams during dose delivery based on the positions of internally implanted fiducials determined using an orthogonal x-ray imaging system and themore » current location of LED external markers. For each x-ray pair acquired, system logfiles report the prediction error, the difference between the measured and predicted fiducial positions, and the delivery error, which is an estimate of the statistical error in the model overcoming the latency between x-ray acquisition and robotic repositioning. The total error was calculated at the time of each x-ray pair, for the number of treatment fractions and the number of patients, giving the average respiratory motion compensation error in three dimensions. The 99{sup th} percentile for the total radial error is 3.85 mm, with the highest contribution of 2.79 mm in superior/inferior (S/I) direction. The absolute mean compensation error is 1.78 mm radially with a 1.27 mm contribution in the S/I direction. Regions of high total error may provide insight into features predicting groups of patients with larger or smaller total errors.« less
-
Plessen, Kerstin J.; Allen, Elena A.; Eichele, Heike; van Wageningen, Heidi; Høvik, Marie Farstad; Sørensen, Lin; Worren, Marius Kalsås; Hugdahl, Kenneth; Eichele, Tom
2016-01-01
Background We examined the blood-oxygen level–dependent (BOLD) activation in brain regions that signal errors and their association with intraindividual behavioural variability and adaptation to errors in children with attention-deficit/hyperactivity disorder (ADHD). Methods We acquired functional MRI data during a Flanker task in medication-naive children with ADHD and healthy controls aged 8–12 years and analyzed the data using independent component analysis. For components corresponding to performance monitoring networks, we compared activations across groups and conditions and correlated them with reaction times (RT). Additionally, we analyzed post-error adaptations in behaviour and motor component activations. Results We included 25 children with ADHD and 29 controls in our analysis. Children with ADHD displayed reduced activation to errors in cingulo-opercular regions and higher RT variability, but no differences of interference control. Larger BOLD amplitude to error trials significantly predicted reduced RT variability across all participants. Neither group showed evidence of post-error response slowing; however, post-error adaptation in motor networks was significantly reduced in children with ADHD. This adaptation was inversely related to activation of the right-lateralized ventral attention network (VAN) on error trials and to task-driven connectivity between the cingulo-opercular system and the VAN. Limitations Our study was limited by the modest sample size and imperfect matching across groups. Conclusion Our findings show a deficit in cingulo-opercular activation in children with ADHD that could relate to reduced signalling for errors. Moreover, the reduced orienting of the VAN signal may mediate deficient post-error motor adaptions. Pinpointing general performance monitoring problems to specific brain regions and operations in error processing may help to guide the targets of future treatments for ADHD. PMID:26441332
-
Whittington, James C. R.; Bogacz, Rafal
2017-01-01
To efficiently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error backpropagation algorithm. However, in this algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of presynaptic and postsynaptic neurons. Several models have been proposed that approximate the backpropagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. Furthermore, for certain parameters, the weight change in the predictive coding model converges to that of the backpropagation algorithm. This suggests that it is possible for cortical networks with simple Hebbian synaptic plasticity to implement efficient learning algorithms in which synapses in areas on multiple levels of hierarchy are modified to minimize the error on the output. PMID:28333583
-
Whittington, James C R; Bogacz, Rafal
2017-05-01
To efficiently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error backpropagation algorithm. However, in this algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of presynaptic and postsynaptic neurons. Several models have been proposed that approximate the backpropagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. Furthermore, for certain parameters, the weight change in the predictive coding model converges to that of the backpropagation algorithm. This suggests that it is possible for cortical networks with simple Hebbian synaptic plasticity to implement efficient learning algorithms in which synapses in areas on multiple levels of hierarchy are modified to minimize the error on the output.
-
Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?
NASA Technical Reports Server (NTRS)
Wallach, Daniel; Thorburn, Peter; Asseng, Senthold; Challinor, Andrew J.; Ewert, Frank; Jones, James W.; Rotter, Reimund; Ruane, Alexander
2016-01-01
Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.
-
Troutman, Brent M.
1982-01-01
Errors in runoff prediction caused by input data errors are analyzed by treating precipitation-runoff models as regression (conditional expectation) models. Independent variables of the regression consist of precipitation and other input measurements; the dependent variable is runoff. In models using erroneous input data, prediction errors are inflated and estimates of expected storm runoff for given observed input variables are biased. This bias in expected runoff estimation results in biased parameter estimates if these parameter estimates are obtained by a least squares fit of predicted to observed runoff values. The problems of error inflation and bias are examined in detail for a simple linear regression of runoff on rainfall and for a nonlinear U.S. Geological Survey precipitation-runoff model. Some implications for flood frequency analysis are considered. A case study using a set of data from Turtle Creek near Dallas, Texas illustrates the problems of model input errors.
-
Joshi, Shuchi N; Srinivas, Nuggehally R; Parmar, Deven V
2018-03-01
Our aim was to develop and validate the extrapolative performance of a regression model using a limited sampling strategy for accurate estimation of the area under the plasma concentration versus time curve for saroglitazar. Healthy subject pharmacokinetic data from a well-powered food-effect study (fasted vs fed treatments; n = 50) was used in this work. The first 25 subjects' serial plasma concentration data up to 72 hours and corresponding AUC 0-t (ie, 72 hours) from the fasting group comprised a training dataset to develop the limited sampling model. The internal datasets for prediction included the remaining 25 subjects from the fasting group and all 50 subjects from the fed condition of the same study. The external datasets included pharmacokinetic data for saroglitazar from previous single-dose clinical studies. Limited sampling models were composed of 1-, 2-, and 3-concentration-time points' correlation with AUC 0-t of saroglitazar. Only models with regression coefficients (R 2 ) >0.90 were screened for further evaluation. The best R 2 model was validated for its utility based on mean prediction error, mean absolute prediction error, and root mean square error. Both correlations between predicted and observed AUC 0-t of saroglitazar and verification of precision and bias using Bland-Altman plot were carried out. None of the evaluated 1- and 2-concentration-time points models achieved R 2 > 0.90. Among the various 3-concentration-time points models, only 4 equations passed the predefined criterion of R 2 > 0.90. Limited sampling models with time points 0.5, 2, and 8 hours (R 2 = 0.9323) and 0.75, 2, and 8 hours (R 2 = 0.9375) were validated. Mean prediction error, mean absolute prediction error, and root mean square error were <30% (predefined criterion) and correlation (r) was at least 0.7950 for the consolidated internal and external datasets of 102 healthy subjects for the AUC 0-t prediction of saroglitazar. The same models, when applied to the AUC 0-t prediction of saroglitazar sulfoxide, showed mean prediction error, mean absolute prediction error, and root mean square error <30% and correlation (r) was at least 0.9339 in the same pool of healthy subjects. A 3-concentration-time points limited sampling model predicts the exposure of saroglitazar (ie, AUC 0-t ) within predefined acceptable bias and imprecision limit. Same model was also used to predict AUC 0-∞ . The same limited sampling model was found to predict the exposure of saroglitazar sulfoxide within predefined criteria. This model can find utility during late-phase clinical development of saroglitazar in the patient population. Copyright © 2018 Elsevier HS Journals, Inc. All rights reserved.
-
Complementary roles for amygdala and periaqueductal gray in temporal-difference fear learning.
Cole, Sindy; McNally, Gavan P
2009-01-01
Pavlovian fear conditioning is not a unitary process. At the neurobiological level multiple brain regions and neurotransmitters contribute to fear learning. At the behavioral level many variables contribute to fear learning including the physical salience of the events being learned about, the direction and magnitude of predictive error, and the rate at which these are learned about. These experiments used a serial compound conditioning design to determine the roles of basolateral amygdala (BLA) NMDA receptors and ventrolateral midbrain periaqueductal gray (vlPAG) mu-opioid receptors (MOR) in predictive fear learning. Rats received a three-stage design, which arranged for both positive and negative prediction errors producing bidirectional changes in fear learning within the same subjects during the test stage. Intra-BLA infusion of the NR2B receptor antagonist Ifenprodil prevented all learning. In contrast, intra-vlPAG infusion of the MOR antagonist CTAP enhanced learning in response to positive predictive error but impaired learning in response to negative predictive error--a pattern similar to Hebbian learning and an indication that fear learning had been divorced from predictive error. These findings identify complementary but dissociable roles for amygdala NMDA receptors and vlPAG MOR in temporal-difference predictive fear learning.
-
Error estimation for CFD aeroheating prediction under rarefied flow condition
NASA Astrophysics Data System (ADS)
Jiang, Yazhong; Gao, Zhenxun; Jiang, Chongwen; Lee, Chunhian
2014-12-01
Both direct simulation Monte Carlo (DSMC) and Computational Fluid Dynamics (CFD) methods have become widely used for aerodynamic prediction when reentry vehicles experience different flow regimes during flight. The implementation of slip boundary conditions in the traditional CFD method under Navier-Stokes-Fourier (NSF) framework can extend the validity of this approach further into transitional regime, with the benefit that much less computational cost is demanded compared to DSMC simulation. Correspondingly, an increasing error arises in aeroheating calculation as the flow becomes more rarefied. To estimate the relative error of heat flux when applying this method for a rarefied flow in transitional regime, theoretical derivation is conducted and a dimensionless parameter ɛ is proposed by approximately analyzing the ratio of the second order term to first order term in the heat flux expression in Burnett equation. DSMC simulation for hypersonic flow over a cylinder in transitional regime is performed to test the performance of parameter ɛ, compared with two other parameters, Knρ and MaṡKnρ.
-
Grinband, Jack; Savitskaya, Judith; Wager, Tor D; Teichert, Tobias; Ferrera, Vincent P; Hirsch, Joy
2011-07-15
The dorsal medial frontal cortex (dMFC) is highly active during choice behavior. Though many models have been proposed to explain dMFC function, the conflict monitoring model is the most influential. It posits that dMFC is primarily involved in detecting interference between competing responses thus signaling the need for control. It accurately predicts increased neural activity and response time (RT) for incompatible (high-interference) vs. compatible (low-interference) decisions. However, it has been shown that neural activity can increase with time on task, even when no decisions are made. Thus, the greater dMFC activity on incompatible trials may stem from longer RTs rather than response conflict. This study shows that (1) the conflict monitoring model fails to predict the relationship between error likelihood and RT, and (2) the dMFC activity is not sensitive to congruency, error likelihood, or response conflict, but is monotonically related to time on task. Copyright © 2010 Elsevier Inc. All rights reserved.
-
Analytical investigation of adaptive control of radiated inlet noise from turbofan engines
NASA Technical Reports Server (NTRS)
Risi, John D.; Burdisso, Ricardo A.
1994-01-01
An analytical model has been developed to predict the resulting far field radiation from a turbofan engine inlet. A feedforward control algorithm was simulated to predict the controlled far field radiation from the destructive combination of fan noise and secondary control sources. Numerical results were developed for two system configurations, with the resulting controlled far field radiation patterns showing varying degrees of attenuation and spillover. With one axial station of twelve control sources and error sensors with equal relative angular positions, nearly global attenuation is achieved. Shifting the angular position of one error sensor resulted in an increase of spillover to the extreme sidelines. The complex control inputs for each configuration was investigated to identify the structure of the wave pattern created by the control sources, giving an indication of performance of the system configuration. It is deduced that the locations of the error sensors and the control source configuration are equally critical to the operation of the active noise control system.
-
Wong, Aaron L; Shelhamer, Mark
2014-05-01
Adaptive processes are crucial in maintaining the accuracy of body movements and rely on error storage and processing mechanisms. Although classically studied with adaptation paradigms, evidence of these ongoing error-correction mechanisms should also be detectable in other movements. Despite this connection, current adaptation models are challenged when forecasting adaptation ability with measures of baseline behavior. On the other hand, we have previously identified an error-correction process present in a particular form of baseline behavior, the generation of predictive saccades. This process exhibits long-term intertrial correlations that decay gradually (as a power law) and are best characterized with the tools of fractal time series analysis. Since this baseline task and adaptation both involve error storage and processing, we sought to find a link between the intertrial correlations of the error-correction process in predictive saccades and the ability of subjects to alter their saccade amplitudes during an adaptation task. Here we find just such a relationship: the stronger the intertrial correlations during prediction, the more rapid the acquisition of adaptation. This reinforces the links found previously between prediction and adaptation in motor control and suggests that current adaptation models are inadequate to capture the complete dynamics of these error-correction processes. A better understanding of the similarities in error processing between prediction and adaptation might provide the means to forecast adaptation ability with a baseline task. This would have many potential uses in physical therapy and the general design of paradigms of motor adaptation. Copyright © 2014 the American Physiological Society.
-
Adjoints and Low-rank Covariance Representation
NASA Technical Reports Server (NTRS)
Tippett, Michael K.; Cohn, Stephen E.
2000-01-01
Quantitative measures of the uncertainty of Earth System estimates can be as important as the estimates themselves. Second moments of estimation errors are described by the covariance matrix, whose direct calculation is impractical when the number of degrees of freedom of the system state is large. Ensemble and reduced-state approaches to prediction and data assimilation replace full estimation error covariance matrices by low-rank approximations. The appropriateness of such approximations depends on the spectrum of the full error covariance matrix, whose calculation is also often impractical. Here we examine the situation where the error covariance is a linear transformation of a forcing error covariance. We use operator norms and adjoints to relate the appropriateness of low-rank representations to the conditioning of this transformation. The analysis is used to investigate low-rank representations of the steady-state response to random forcing of an idealized discrete-time dynamical system.
-
García-González, Miguel A; Fernández-Chimeno, Mireya; Ramos-Castro, Juan
2009-02-01
An analysis of the errors due to the finite resolution of RR time series in the estimation of the approximate entropy (ApEn) is described. The quantification errors in the discrete RR time series produce considerable errors in the ApEn estimation (bias and variance) when the signal variability or the sampling frequency is low. Similar errors can be found in indices related to the quantification of recurrence plots. An easy way to calculate a figure of merit [the signal to resolution of the neighborhood ratio (SRN)] is proposed in order to predict when the bias in the indices could be high. When SRN is close to an integer value n, the bias is higher than when near n - 1/2 or n + 1/2. Moreover, if SRN is close to an integer value, the lower this value, the greater the bias is.
-
NASA Astrophysics Data System (ADS)
Sabato, L.; Arpaia, P.; Cianchi, A.; Liccardo, A.; Mostacci, A.; Palumbo, L.; Variola, A.
2018-02-01
In high-brightness LINear ACcelerators (LINACs), electron bunch length can be measured indirectly by a radio frequency deflector (RFD). In this paper, the accuracy loss arising from non-negligible correlations between particle longitudinal positions and the transverse plane (in particular the vertical one) at RFD entrance is analytically assessed. Theoretical predictions are compared with simulation results, obtained by means of ELEctron Generation ANd Tracking (ELEGANT) code, in the case study of the gamma beam system (GBS) at the extreme light infrastructure—nuclear physics (ELI-NP). In particular, the relative error affecting the bunch length measurement, for bunches characterized by both energy chirp and fixed correlation coefficients between longitudinal particle positions and the vertical plane, is reported. Moreover, the relative error versus the correlation coefficients is shown for fixed RFD phase 0 rad and π rad. The relationship between relative error and correlations factors can help the decision of using the bunch length measurement technique with one or two vertical spot size measurements in order to cancel the correlations contribution. In the case of the GBS electron LINAC, the misalignment of one of the quadrupoles before the RFD between -2 mm and 2 mm leads to a relative error less than 5%. The misalignment of the first C-band accelerating section between -2 mm and 2 mm could lead to a relative error up to 10%.
-
Bohil, Corey J; Higgins, Nicholas A; Keebler, Joseph R
2014-01-01
We compared methods for predicting and understanding the source of confusion errors during military vehicle identification training. Participants completed training to identify main battle tanks. They also completed card-sorting and similarity-rating tasks to express their mental representation of resemblance across the set of training items. We expected participants to selectively attend to a subset of vehicle features during these tasks, and we hypothesised that we could predict identification confusion errors based on the outcomes of the card-sort and similarity-rating tasks. Based on card-sorting results, we were able to predict about 45% of observed identification confusions. Based on multidimensional scaling of the similarity-rating data, we could predict more than 80% of identification confusions. These methods also enabled us to infer the dimensions receiving significant attention from each participant. This understanding of mental representation may be crucial in creating personalised training that directs attention to features that are critical for accurate identification. Participants completed military vehicle identification training and testing, along with card-sorting and similarity-rating tasks. The data enabled us to predict up to 84% of identification confusion errors and to understand the mental representation underlying these errors. These methods have potential to improve training and reduce identification errors leading to fratricide.
-
Psychological and Neural Mechanisms of Experimental Extinction: A Selective Review
Delamater, Andrew R.; Westbrook, R. Frederick
2013-01-01
The present review examines key psychological concepts in the study of experimental extinction and implications these have for an understanding of the underlying neurobiology of extinction learning. We suggest that many of the signature characteristics of extinction learning (spontaneous recovery, renewal, reinstatement, rapid reacquisition) can be accommodated by the standard associative learning theory assumption that extinction results in partial erasure of the original learning together with new inhibitory learning. Moreover, we consider recent behavioral and neural evidence that supports the partial erasure view of extinction, but also note shortcomings in our understanding of extinction circuits as these relate to the negative prediction error concept. Recent work suggests that common prediction error and stimulus-specific prediction error terms both may be required to explain neural plasticity both in acquisition and extinction learning. In addition, we suggest that many issues in the content of extinction learning have not been fully addressed in current research, but that neurobiological approaches should be especially helpful in addressing such issues. These include questions about the nature of extinction learning (excitatory CS-No US, inhibitory CS-US learning, occasion setting processes), especially as this relates to studies of the micro-circuitry of extinction, as well as its representational content (sensory, motivational, response). An additional understudied problem in extinction research is the role played by attention processes and their underlying neural networks, although some research and theory converge on the idea that extinction is accompanied by attention decrements (i.e., habituation-like processes). PMID:24104049
-
Psychological and neural mechanisms of experimental extinction: a selective review.
Delamater, Andrew R; Westbrook, R Frederick
2014-02-01
The present review examines key psychological concepts in the study of experimental extinction and implications these have for an understanding of the underlying neurobiology of extinction learning. We suggest that many of the signature characteristics of extinction learning (spontaneous recovery, renewal, reinstatement, rapid reacquisition) can be accommodated by the standard associative learning theory assumption that extinction results in partial erasure of the original learning together with new inhibitory learning. Moreover, we consider recent behavioral and neural evidence that supports the partial erasure view of extinction, but also note shortcomings in our understanding of extinction circuits as these relate to the negative prediction error concept. Recent work suggests that common prediction error and stimulus-specific prediction error terms both may be required to explain neural plasticity both in acquisition and extinction learning. In addition, we suggest that many issues in the content of extinction learning have not been fully addressed in current research, but that neurobiological approaches should be especially helpful in addressing such issues. These include questions about the nature of extinction learning (excitatory CS-No US, inhibitory CS-US learning, occasion setting processes), especially as this relates to studies of the micro-circuitry of extinction, as well as its representational content (sensory, motivational, response). An additional understudied problem in extinction research is the role played by attention processes and their underlying neural networks, although some research and theory converge on the idea that extinction is accompanied by attention decrements (i.e., habituation-like processes). Copyright © 2013 Elsevier Inc. All rights reserved.
-
Episodic Memory Encoding Interferes with Reward Learning and Decreases Striatal Prediction Errors
Braun, Erin Kendall; Daw, Nathaniel D.
2014-01-01
Learning is essential for adaptive decision making. The striatum and its dopaminergic inputs are known to support incremental reward-based learning, while the hippocampus is known to support encoding of single events (episodic memory). Although traditionally studied separately, in even simple experiences, these two types of learning are likely to co-occur and may interact. Here we sought to understand the nature of this interaction by examining how incremental reward learning is related to concurrent episodic memory encoding. During the experiment, human participants made choices between two options (colored squares), each associated with a drifting probability of reward, with the goal of earning as much money as possible. Incidental, trial-unique object pictures, unrelated to the choice, were overlaid on each option. The next day, participants were given a surprise memory test for these pictures. We found that better episodic memory was related to a decreased influence of recent reward experience on choice, both within and across participants. fMRI analyses further revealed that during learning the canonical striatal reward prediction error signal was significantly weaker when episodic memory was stronger. This decrease in reward prediction error signals in the striatum was associated with enhanced functional connectivity between the hippocampus and striatum at the time of choice. Our results suggest a mechanism by which memory encoding may compete for striatal processing and provide insight into how interactions between different forms of learning guide reward-based decision making. PMID:25378157
-
Suppression of Striatal Prediction Errors by the Prefrontal Cortex in Placebo Hypoalgesia.
Schenk, Lieven A; Sprenger, Christian; Onat, Selim; Colloca, Luana; Büchel, Christian
2017-10-04
Classical learning theories predict extinction after the discontinuation of reinforcement through prediction errors. However, placebo hypoalgesia, although mediated by associative learning, has been shown to be resistant to extinction. We tested the hypothesis that this is mediated by the suppression of prediction error processing through the prefrontal cortex (PFC). We compared pain modulation through treatment cues (placebo hypoalgesia, treatment context) with pain modulation through stimulus intensity cues (stimulus context) during functional magnetic resonance imaging in 48 male and female healthy volunteers. During acquisition, our data show that expectations are correctly learned and that this is associated with prediction error signals in the ventral striatum (VS) in both contexts. However, in the nonreinforced test phase, pain modulation and expectations of pain relief persisted to a larger degree in the treatment context, indicating that the expectations were not correctly updated in the treatment context. Consistently, we observed significantly stronger neural prediction error signals in the VS in the stimulus context compared with the treatment context. A connectivity analysis revealed negative coupling between the anterior PFC and the VS in the treatment context, suggesting that the PFC can suppress the expression of prediction errors in the VS. Consistent with this, a participant's conceptual views and beliefs about treatments influenced the pain modulation only in the treatment context. Our results indicate that in placebo hypoalgesia contextual treatment information engages prefrontal conceptual processes, which can suppress prediction error processing in the VS and lead to reduced updating of treatment expectancies, resulting in less extinction of placebo hypoalgesia. SIGNIFICANCE STATEMENT In aversive and appetitive reinforcement learning, learned effects show extinction when reinforcement is discontinued. This is thought to be mediated by prediction errors (i.e., the difference between expectations and outcome). Although reinforcement learning has been central in explaining placebo hypoalgesia, placebo hypoalgesic effects show little extinction and persist after the discontinuation of reinforcement. Our results support the idea that conceptual treatment beliefs bias the neural processing of expectations in a treatment context compared with a more stimulus-driven processing of expectations with stimulus intensity cues. We provide evidence that this is associated with the suppression of prediction error processing in the ventral striatum by the prefrontal cortex. This provides a neural basis for persisting effects in reinforcement learning and placebo hypoalgesia. Copyright © 2017 the authors 0270-6474/17/379715-09$15.00/0.
-
Accuracy of measurement of star images on a pixel array
NASA Technical Reports Server (NTRS)
King, I. R.
1983-01-01
Algorithms are developed for predicting the accuracy with which the brightness of a star can be determined from its image on a digital detector array, as a function of the brightness of the background. The assumption is made that a known profile is being fitted by least squares. The two profiles used correspond to ST images and to ground-based observations. The first result is an approximate rule of thumb for equivalent noise area. More rigorous results are then given in tabular form. The size of the pixels, relative to the image size, is taken into account. Astronometric accuracy is also discussed briefly; the error, relative to image size, is very similar to the photometric error relative to brightness.
-
Latin hypercube approach to estimate uncertainty in ground water vulnerability
Gurdak, J.J.; McCray, J.E.; Thyne, G.; Qi, S.L.
2007-01-01
A methodology is proposed to quantify prediction uncertainty associated with ground water vulnerability models that were developed through an approach that coupled multivariate logistic regression with a geographic information system (GIS). This method uses Latin hypercube sampling (LHS) to illustrate the propagation of input error and estimate uncertainty associated with the logistic regression predictions of ground water vulnerability. Central to the proposed method is the assumption that prediction uncertainty in ground water vulnerability models is a function of input error propagation from uncertainty in the estimated logistic regression model coefficients (model error) and the values of explanatory variables represented in the GIS (data error). Input probability distributions that represent both model and data error sources of uncertainty were simultaneously sampled using a Latin hypercube approach with logistic regression calculations of probability of elevated nonpoint source contaminants in ground water. The resulting probability distribution represents the prediction intervals and associated uncertainty of the ground water vulnerability predictions. The method is illustrated through a ground water vulnerability assessment of the High Plains regional aquifer. Results of the LHS simulations reveal significant prediction uncertainties that vary spatially across the regional aquifer. Additionally, the proposed method enables a spatial deconstruction of the prediction uncertainty that can lead to improved prediction of ground water vulnerability. ?? 2007 National Ground Water Association.
-
Reliability and validity of two isometric squat tests.
Blazevich, Anthony J; Gill, Nicholas; Newton, Robert U
2002-05-01
The purpose of the present study was first to examine the reliability of isometric squat (IS) and isometric forward hack squat (IFHS) tests to determine if repeated measures on the same subjects yielded reliable results. The second purpose was to examine the relation between isometric and dynamic measures of strength to assess validity. Fourteen male subjects performed maximal IS and IFHS tests on 2 occasions and 1 repetition maximum (1-RM) free-weight squat and forward hack squat (FHS) tests on 1 occasion. The 2 tests were found to be highly reliable (intraclass correlation coefficient [ICC](IS) = 0.97 and ICC(IFHS) = 1.00). There was a strong relation between average IS and 1-RM squat performance, and between IFHS and 1-RM FHS performance (r(squat) = 0.77, r(FHS) = 0.76; p < 0.01), but a weak relation between squat and FHS test performances (r < 0.55). There was also no difference between observed 1-RM values and those predicted by our regression equations. Errors in predicting 1-RM performance were in the order of 8.5% (standard error of the estimate [SEE] = 13.8 kg) and 7.3% (SEE = 19.4 kg) for IS and IFHS respectively. Correlations between isometric and 1-RM tests were not of sufficient size to indicate high validity of the isometric tests. Together the results suggest that IS and IFHS tests could detect small differences in multijoint isometric strength between subjects, or performance changes over time, and that the scores in the isometric tests are well related to 1-RM performance. However, there was a small error when predicting 1-RM performance from isometric performance, and these tests have not been shown to discriminate between small changes in dynamic strength. The weak relation between squat and FHS test performance can be attributed to differences in the movement patterns of the tests
-
Prediction and typicality in multiverse cosmology
NASA Astrophysics Data System (ADS)
Azhar, Feraz
2014-02-01
In the absence of a fundamental theory that precisely predicts values for observable parameters, anthropic reasoning attempts to constrain probability distributions over those parameters in order to facilitate the extraction of testable predictions. The utility of this approach has been vigorously debated of late, particularly in light of theories that claim we live in a multiverse, where parameters may take differing values in regions lying outside our observable horizon. Within this cosmological framework, we investigate the efficacy of top-down anthropic reasoning based on the weak anthropic principle. We argue contrary to recent claims that it is not clear one can either dispense with notions of typicality altogether or presume typicality, in comparing resulting probability distributions with observations. We show in a concrete, top-down setting related to dark matter, that assumptions about typicality can dramatically affect predictions, thereby providing a guide to how errors in reasoning regarding typicality translate to errors in the assessment of predictive power. We conjecture that this dependence on typicality is an integral feature of anthropic reasoning in broader cosmological contexts, and argue in favour of the explicit inclusion of measures of typicality in schemes invoking anthropic reasoning, with a view to extracting predictions from multiverse scenarios.
-
Space-Time Earthquake Prediction: The Error Diagrams
NASA Astrophysics Data System (ADS)
Molchan, G.
2010-08-01
The quality of earthquake prediction is usually characterized by a two-dimensional diagram n versus τ, where n is the rate of failures-to-predict and τ is a characteristic of space-time alarm. Unlike the time prediction case, the quantity τ is not defined uniquely. We start from the case in which τ is a vector with components related to the local alarm times and find a simple structure of the space-time diagram in terms of local time diagrams. This key result is used to analyze the usual 2-d error sets { n, τ w } in which τ w is a weighted mean of the τ components and w is the weight vector. We suggest a simple algorithm to find the ( n, τ w ) representation of all random guess strategies, the set D, and prove that there exists the unique case of w when D degenerates to the diagonal n + τ w = 1. We find also a confidence zone of D on the ( n, τ w ) plane when the local target rates are known roughly. These facts are important for correct interpretation of ( n, τ w ) diagrams when we discuss the prediction capability of the data or prediction methods.
-
Running Speed Can Be Predicted from Foot Contact Time during Outdoor over Ground Running.
de Ruiter, Cornelis J; van Oeveren, Ben; Francke, Agnieta; Zijlstra, Patrick; van Dieen, Jaap H
2016-01-01
The number of validation studies of commercially available foot pods that provide estimates of running speed is limited and these studies have been conducted under laboratory conditions. Moreover, internal data handling and algorithms used to derive speed from these pods are proprietary and thereby unclear. The present study investigates the use of foot contact time (CT) for running speed estimations, which potentially can be used in addition to the global positioning system (GPS) in situations where GPS performance is limited. CT was measured with tri axial inertial sensors attached to the feet of 14 runners, during natural over ground outdoor running, under optimized conditions for GPS. The individual relationships between running speed and CT were established during short runs at different speeds on two days. These relations were subsequently used to predict instantaneous speed during a straight line 4 km run with a single turning point halfway. Stopwatch derived speed, measured for each of 32 consecutive 125m intervals during the 4 km runs, was used as reference. Individual speed-CT relations were strong (r2 >0.96 for all trials) and consistent between days. During the 4km runs, median error (ranges) in predicted speed from CT 2.5% (5.2) was higher (P<0.05) than for GPS 1.6% (0.8). However, around the turning point and during the first and last 125m interval, error for GPS-speed increased to 5.0% (4.5) and became greater (P<0.05) than the error predicted from CT: 2.7% (4.4). Small speed fluctuations during 4km runs were adequately monitored with both methods: CT and GPS respectively explained 85% and 73% of the total speed variance during 4km runs. In conclusion, running speed estimates bases on speed-CT relations, have acceptable accuracy and could serve to backup or substitute for GPS during tarmac running on flat terrain whenever GPS performance is limited.
-
Sleep and errors in a group of Australian hospital nurses at work and during the commute.
Dorrian, Jillian; Tolley, Carolyn; Lamond, Nicole; van den Heuvel, Cameron; Pincombe, Jan; Rogers, Ann E; Drew, Dawson
2008-09-01
There is a paucity of information regarding Australian nurses' sleep and fatigue levels, and whether they result in impairment. Forty-one Australian hospital nurses completed daily logbooks for one month recording work hours, sleep, sleepiness, stress, errors, near errors and observed errors (made by others). Nurses reported exhaustion, stress and struggling to remain (STR) awake at work during one in three shifts. Sleep was significantly reduced on workdays in general, and workdays when an error was reported relative to days off. The primary predictor of error was STR, followed by stress. The primary predictor of extreme drowsiness during the commute was also STR awake, followed by exhaustion, and consecutive shifts. In turn, STR awake was predicted by exhaustion, prior sleep and shift length. Findings highlight the need for further attention to these issues to optimise the safety of nurses and patients in our hospitals, and the community at large on our roads.
-
Knowledge acquisition is governed by striatal prediction errors.
Pine, Alex; Sadeh, Noa; Ben-Yakov, Aya; Dudai, Yadin; Mendelsohn, Avi
2018-04-26
Discrepancies between expectations and outcomes, or prediction errors, are central to trial-and-error learning based on reward and punishment, and their neurobiological basis is well characterized. It is not known, however, whether the same principles apply to declarative memory systems, such as those supporting semantic learning. Here, we demonstrate with fMRI that the brain parametrically encodes the degree to which new factual information violates expectations based on prior knowledge and beliefs-most prominently in the ventral striatum, and cortical regions supporting declarative memory encoding. These semantic prediction errors determine the extent to which information is incorporated into long-term memory, such that learning is superior when incoming information counters strong incorrect recollections, thereby eliciting large prediction errors. Paradoxically, by the same account, strong accurate recollections are more amenable to being supplanted by misinformation, engendering false memories. These findings highlight a commonality in brain mechanisms and computational rules that govern declarative and nondeclarative learning, traditionally deemed dissociable.
-
Sampson, Maureen L; Gounden, Verena; van Deventer, Hendrik E; Remaley, Alan T
2016-02-01
The main drawback of the periodic analysis of quality control (QC) material is that test performance is not monitored in time periods between QC analyses, potentially leading to the reporting of faulty test results. The objective of this study was to develop a patient based QC procedure for the more timely detection of test errors. Results from a Chem-14 panel measured on the Beckman LX20 analyzer were used to develop the model. Each test result was predicted from the other 13 members of the panel by multiple regression, which resulted in correlation coefficients between the predicted and measured result of >0.7 for 8 of the 14 tests. A logistic regression model, which utilized the measured test result, the predicted test result, the day of the week and time of day, was then developed for predicting test errors. The output of the logistic regression was tallied by a daily CUSUM approach and used to predict test errors, with a fixed specificity of 90%. The mean average run length (ARL) before error detection by CUSUM-Logistic Regression (CSLR) was 20 with a mean sensitivity of 97%, which was considerably shorter than the mean ARL of 53 (sensitivity 87.5%) for a simple prediction model that only used the measured result for error detection. A CUSUM-Logistic Regression analysis of patient laboratory data can be an effective approach for the rapid and sensitive detection of clinical laboratory errors. Published by Elsevier Inc.
-
Surprise beyond prediction error
Chumbley, Justin R; Burke, Christopher J; Stephan, Klaas E; Friston, Karl J; Tobler, Philippe N; Fehr, Ernst
2014-01-01
Surprise drives learning. Various neural “prediction error” signals are believed to underpin surprise-based reinforcement learning. Here, we report a surprise signal that reflects reinforcement learning but is neither un/signed reward prediction error (RPE) nor un/signed state prediction error (SPE). To exclude these alternatives, we measured surprise responses in the absence of RPE and accounted for a host of potential SPE confounds. This new surprise signal was evident in ventral striatum, primary sensory cortex, frontal poles, and amygdala. We interpret these findings via a normative model of surprise. PMID:24700400
-
Homeostatic Regulation of Memory Systems and Adaptive Decisions
Mizumori, Sheri JY; Jo, Yong Sang
2013-01-01
While it is clear that many brain areas process mnemonic information, understanding how their interactions result in continuously adaptive behaviors has been a challenge. A homeostatic-regulated prediction model of memory is presented that considers the existence of a single memory system that is based on a multilevel coordinated and integrated network (from cells to neural systems) that determines the extent to which events and outcomes occur as predicted. The “multiple memory systems of the brain” have in common output that signals errors in the prediction of events and/or their outcomes, although these signals differ in terms of what the error signal represents (e.g., hippocampus: context prediction errors vs. midbrain/striatum: reward prediction errors). The prefrontal cortex likely plays a pivotal role in the coordination of prediction analysis within and across prediction brain areas. By virtue of its widespread control and influence, and intrinsic working memory mechanisms. Thus, the prefrontal cortex supports the flexible processing needed to generate adaptive behaviors and predict future outcomes. It is proposed that prefrontal cortex continually and automatically produces adaptive responses according to homeostatic regulatory principles: prefrontal cortex may serve as a controller that is intrinsically driven to maintain in prediction areas an experience-dependent firing rate set point that ensures adaptive temporally and spatially resolved neural responses to future prediction errors. This same drive by prefrontal cortex may also restore set point firing rates after deviations (i.e. prediction errors) are detected. In this way, prefrontal cortex contributes to reducing uncertainty in prediction systems. An emergent outcome of this homeostatic view may be the flexible and adaptive control that prefrontal cortex is known to implement (i.e. working memory) in the most challenging of situations. Compromise to any of the prediction circuits should result in rigid and suboptimal decision making and memory as seen in addiction and neurological disease. © 2013 The Authors. Hippocampus Published by Wiley Periodicals, Inc. PMID:23929788
-
Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination
NASA Astrophysics Data System (ADS)
Li, Weihua; Sankarasubramanian, A.
2012-12-01
Model errors are inevitable in any prediction exercise. One approach that is currently gaining attention in reducing model errors is by combining multiple models to develop improved predictions. The rationale behind this approach primarily lies on the premise that optimal weights could be derived for each model so that the developed multimodel predictions will result in improved predictions. A new dynamic approach (MM-1) to combine multiple hydrological models by evaluating their performance/skill contingent on the predictor state is proposed. We combine two hydrological models, "abcd" model and variable infiltration capacity (VIC) model, to develop multimodel streamflow predictions. To quantify precisely under what conditions the multimodel combination results in improved predictions, we compare multimodel scheme MM-1 with optimal model combination scheme (MM-O) by employing them in predicting the streamflow generated from a known hydrologic model (abcd model orVICmodel) with heteroscedastic error variance as well as from a hydrologic model that exhibits different structure than that of the candidate models (i.e., "abcd" model or VIC model). Results from the study show that streamflow estimated from single models performed better than multimodels under almost no measurement error. However, under increased measurement errors and model structural misspecification, both multimodel schemes (MM-1 and MM-O) consistently performed better than the single model prediction. Overall, MM-1 performs better than MM-O in predicting the monthly flow values as well as in predicting extreme monthly flows. Comparison of the weights obtained from each candidate model reveals that as measurement errors increase, MM-1 assigns weights equally for all the models, whereas MM-O assigns higher weights for always the best-performing candidate model under the calibration period. Applying the multimodel algorithms for predicting streamflows over four different sites revealed that MM-1 performs better than all single models and optimal model combination scheme, MM-O, in predicting the monthly flows as well as the flows during wetter months.
-
Homeostatic regulation of memory systems and adaptive decisions.
Mizumori, Sheri J Y; Jo, Yong Sang
2013-11-01
While it is clear that many brain areas process mnemonic information, understanding how their interactions result in continuously adaptive behaviors has been a challenge. A homeostatic-regulated prediction model of memory is presented that considers the existence of a single memory system that is based on a multilevel coordinated and integrated network (from cells to neural systems) that determines the extent to which events and outcomes occur as predicted. The "multiple memory systems of the brain" have in common output that signals errors in the prediction of events and/or their outcomes, although these signals differ in terms of what the error signal represents (e.g., hippocampus: context prediction errors vs. midbrain/striatum: reward prediction errors). The prefrontal cortex likely plays a pivotal role in the coordination of prediction analysis within and across prediction brain areas. By virtue of its widespread control and influence, and intrinsic working memory mechanisms. Thus, the prefrontal cortex supports the flexible processing needed to generate adaptive behaviors and predict future outcomes. It is proposed that prefrontal cortex continually and automatically produces adaptive responses according to homeostatic regulatory principles: prefrontal cortex may serve as a controller that is intrinsically driven to maintain in prediction areas an experience-dependent firing rate set point that ensures adaptive temporally and spatially resolved neural responses to future prediction errors. This same drive by prefrontal cortex may also restore set point firing rates after deviations (i.e. prediction errors) are detected. In this way, prefrontal cortex contributes to reducing uncertainty in prediction systems. An emergent outcome of this homeostatic view may be the flexible and adaptive control that prefrontal cortex is known to implement (i.e. working memory) in the most challenging of situations. Compromise to any of the prediction circuits should result in rigid and suboptimal decision making and memory as seen in addiction and neurological disease. Copyright © 2013 Wiley Periodicals, Inc.
-
Kellman, Philip J; Mnookin, Jennifer L; Erlikhman, Gennady; Garrigan, Patrick; Ghose, Tandra; Mettler, Everett; Charlton, David; Dror, Itiel E
2014-01-01
Latent fingerprint examination is a complex task that, despite advances in image processing, still fundamentally depends on the visual judgments of highly trained human examiners. Fingerprints collected from crime scenes typically contain less information than fingerprints collected under controlled conditions. Specifically, they are often noisy and distorted and may contain only a portion of the total fingerprint area. Expertise in fingerprint comparison, like other forms of perceptual expertise, such as face recognition or aircraft identification, depends on perceptual learning processes that lead to the discovery of features and relations that matter in comparing prints. Relatively little is known about the perceptual processes involved in making comparisons, and even less is known about what characteristics of fingerprint pairs make particular comparisons easy or difficult. We measured expert examiner performance and judgments of difficulty and confidence on a new fingerprint database. We developed a number of quantitative measures of image characteristics and used multiple regression techniques to discover objective predictors of error as well as perceived difficulty and confidence. A number of useful predictors emerged, and these included variables related to image quality metrics, such as intensity and contrast information, as well as measures of information quantity, such as the total fingerprint area. Also included were configural features that fingerprint experts have noted, such as the presence and clarity of global features and fingerprint ridges. Within the constraints of the overall low error rates of experts, a regression model incorporating the derived predictors demonstrated reasonable success in predicting objective difficulty for print pairs, as shown both in goodness of fit measures to the original data set and in a cross validation test. The results indicate the plausibility of using objective image metrics to predict expert performance and subjective assessment of difficulty in fingerprint comparisons.
-
The impact of perfectionism and anxiety traits on action monitoring in major depressive disorder
De Bruijn, Ellen R. A.; Destoop, Marianne; Hulstijn, Wouter; Sabbe, Bernard G. C.
2010-01-01
Perfectionism and anxiety features are involved in the clinical presentation and neurobiology of major depressive disorder (MDD). In MDD, cognitive control mechanisms such as action monitoring can adequately be investigated applying electrophysiological registrations of the error-related negativity (ERN) and error positivity (Pe). It is also known that traits of perfectionism and anxiety influence ERN amplitudes in healthy subjects. The current study explores the impact of perfectionism and anxiety traits on action monitoring in MDD. A total of 39 MDD patients performed a flankers task during an event-related potential (ERP) session and completed the multidimensional perfectionism scale (MPS) with its concern over mistakes (CM) and doubt about actions (DA) subscales and the trait form of the State Trait Anxiety Inventory. Multiple regression analyses with stepwise backward elimination revealed MPS-DA to be a significant predictor (R2:0.22) for the ERN outcomes, and overall MPS (R2:0.13) and MPS-CM scores (R2:0.18) to have significant predictive value for the Pe amplitudes. Anxiety traits did not have a predictive capacity for the ERPs. MPS-DA clearly affected the ERN, and overall MPS and MPS-CM influenced the Pe, whereas no predictive capacity was found for anxiety traits. The manifest impact of perfectionism on patients’ error-related ERPs may contribute to our understanding of the action-monitoring process and the functional significance of the Pe in MDD. The divergent findings for perfectionism and anxiety features also indicate that the wide range of various affective personality styles might exert a different effect on action monitoring in MDD, awaiting further investigation. PMID:20473695
-
NASA Astrophysics Data System (ADS)
Lee, J.; Matson, D. M.
2014-10-01
This paper describes the prediction of mass evaporation of at% alloys during thermophysical property measurements using the electrostatic levitator at NASA Marshall Space Flight Center in Huntsville, AL. The final mass, final composition, and activity of individual component are considered in the calculation of mass evaporation. The predicted reduction in mass and variation in composition are validated with six ESL samples which underwent different thermal cycles. The predicted mass evaporation and composition shift show good agreement with experiments with the maximum relative errors of 4.8 % and 1.7 %, respectively.
-
Competition between learned reward and error outcome predictions in anterior cingulate cortex.
Alexander, William H; Brown, Joshua W
2010-02-15
The anterior cingulate cortex (ACC) is implicated in performance monitoring and cognitive control. Non-human primate studies of ACC show prominent reward signals, but these are elusive in human studies, which instead show mainly conflict and error effects. Here we demonstrate distinct appetitive and aversive activity in human ACC. The error likelihood hypothesis suggests that ACC activity increases in proportion to the likelihood of an error, and ACC is also sensitive to the consequence magnitude of the predicted error. Previous work further showed that error likelihood effects reach a ceiling as the potential consequences of an error increase, possibly due to reductions in the average reward. We explored this issue by independently manipulating reward magnitude of task responses and error likelihood while controlling for potential error consequences in an Incentive Change Signal Task. The fMRI results ruled out a modulatory effect of expected reward on error likelihood effects in favor of a competition effect between expected reward and error likelihood. Dynamic causal modeling showed that error likelihood and expected reward signals are intrinsic to the ACC rather than received from elsewhere. These findings agree with interpretations of ACC activity as signaling both perceptions of risk and predicted reward. Copyright 2009 Elsevier Inc. All rights reserved.
-
Raman, Ilhan; Weekes, Brendan Stuart
2005-01-01
Deep dysgraphic patients make semantic errors when writing to dictation and they cannot write nonwords. Extant reports of deep dysgraphia come from languages with relatively opaque orthographies. Turkish is a transparent orthography because the bidirectional mappings between phonology and orthography are completely predictable. We report BRB, a biscriptal Turkish-English speaker who has acquired dysgraphia characterised by semantic errors as well as effects of grammatical class and imageability on writing in Turkish. Nonword spelling is abolished. A similar pattern of errors is observed in English. BRB is the first report of acquired dysgraphia in a truly transparent writing system. We argue that deep dysgraphia results from damage to the mappings that are common to both languages between word meanings and orthographic representations.
-
Hypoglycemia early alarm systems based on recursive autoregressive partial least squares models.
Bayrak, Elif Seyma; Turksoy, Kamuran; Cinar, Ali; Quinn, Lauretta; Littlejohn, Elizabeth; Rollins, Derrick
2013-01-01
Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia. A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated. Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min. The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance. © 2012 Diabetes Technology Society.
-
Hypoglycemia Early Alarm Systems Based on Recursive Autoregressive Partial Least Squares Models
Bayrak, Elif Seyma; Turksoy, Kamuran; Cinar, Ali; Quinn, Lauretta; Littlejohn, Elizabeth; Rollins, Derrick
2013-01-01
Background Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia. Methods A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated. Results Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min. Conclusions The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance. PMID:23439179
-
Kahmann, A; Anzanello, M J; Fogliatto, F S; Marcelo, M C A; Ferrão, M F; Ortiz, R S; Mariotti, K C
2018-04-15
Street cocaine is typically altered with several compounds that increase its harmful health-related side effects, most notably depression, convulsions, and severe damages to the cardiovascular system, lungs, and brain. Thus, determining the concentration of cocaine and adulterants in seized drug samples is important from both health and forensic perspectives. Although FTIR has been widely used to identify the fingerprint and concentration of chemical compounds, spectroscopy datasets are usually comprised of thousands of highly correlated wavenumbers which, when used as predictors in regression models, tend to undermine the predictive performance of multivariate techniques. In this paper, we propose an FTIR wavenumber selection method aimed at identifying FTIR spectra intervals that best predict the concentration of cocaine and adulterants (e.g. caffeine, phenacetin, levamisole, and lidocaine) in cocaine samples. For that matter, the Mutual Information measure is integrated into a Quadratic Programming problem with the objective of minimizing the probability of retaining redundant wavenumbers, while maximizing the relationship between retained wavenumbers and compounds' concentrations. Optimization outputs guide the order of inclusion of wavenumbers in a predictive model, using a forward-based wavenumber selection method. After the inclusion of each wavenumber, parameters of three alternative regression models are estimated, and each model's prediction error is assessed through the Mean Average Error (MAE) measure; the recommended subset of retained wavenumbers is the one that minimizes the prediction error with maximum parsimony. Using our propositions in a dataset of 115 cocaine samples we obtained a best prediction model with average MAE of 0.0502 while retaining only 2.29% of the original wavenumbers, increasing the predictive precision by 0.0359 when compared to a model using the complete set of wavenumbers as predictors. Copyright © 2018 Elsevier B.V. All rights reserved.
-
49 CFR Appendix D to Part 222 - Determining Risk Levels
Code of Federal Regulations, 2011 CFR
2011-10-01
... prediction formulas can be used to derive the following for each crossing: 1. the predicted collisions (PC) 2... for errors such as data entry errors. The final output is the predicted number of collisions (PC). (e... collisions (PC). (f) For the prediction and severity index formulas, please see the following DOT...
-
Sando, Roy; Chase, Katherine J.
2017-03-23
A common statistical procedure for estimating streamflow statistics at ungaged locations is to develop a relational model between streamflow and drainage basin characteristics at gaged locations using least squares regression analysis; however, least squares regression methods are parametric and make constraining assumptions about the data distribution. The random forest regression method provides an alternative nonparametric method for estimating streamflow characteristics at ungaged sites and requires that the data meet fewer statistical conditions than least squares regression methods.Random forest regression analysis was used to develop predictive models for 89 streamflow characteristics using Precipitation-Runoff Modeling System simulated streamflow data and drainage basin characteristics at 179 sites in central and eastern Montana. The predictive models were developed from streamflow data simulated for current (baseline, water years 1982–99) conditions and three future periods (water years 2021–38, 2046–63, and 2071–88) under three different climate-change scenarios. These predictive models were then used to predict streamflow characteristics for baseline conditions and three future periods at 1,707 fish sampling sites in central and eastern Montana. The average root mean square error for all predictive models was about 50 percent. When streamflow predictions at 23 fish sampling sites were compared to nearby locations with simulated data, the mean relative percent difference was about 43 percent. When predictions were compared to streamflow data recorded at 21 U.S. Geological Survey streamflow-gaging stations outside of the calibration basins, the average mean absolute percent error was about 73 percent.
-
Five-equation and robust three-equation methods for solution verification of large eddy simulation
NASA Astrophysics Data System (ADS)
Dutta, Rabijit; Xing, Tao
2018-02-01
This study evaluates the recently developed general framework for solution verification methods for large eddy simulation (LES) using implicitly filtered LES of periodic channel flows at friction Reynolds number of 395 on eight systematically refined grids. The seven-equation method shows that the coupling error based on Hypothesis I is much smaller as compared with the numerical and modeling errors and therefore can be neglected. The authors recommend five-equation method based on Hypothesis II, which shows a monotonic convergence behavior of the predicted numerical benchmark ( S C ), and provides realistic error estimates without the need of fixing the orders of accuracy for either numerical or modeling errors. Based on the results from seven-equation and five-equation methods, less expensive three and four-equation methods for practical LES applications were derived. It was found that the new three-equation method is robust as it can be applied to any convergence types and reasonably predict the error trends. It was also observed that the numerical and modeling errors usually have opposite signs, which suggests error cancellation play an essential role in LES. When Reynolds averaged Navier-Stokes (RANS) based error estimation method is applied, it shows significant error in the prediction of S C on coarse meshes. However, it predicts reasonable S C when the grids resolve at least 80% of the total turbulent kinetic energy.
-
Lock-in amplifier error prediction and correction in frequency sweep measurements.
Sonnaillon, Maximiliano Osvaldo; Bonetto, Fabian Jose
2007-01-01
This article proposes an analytical algorithm for predicting errors in lock-in amplifiers (LIAs) working with time-varying reference frequency. Furthermore, a simple method for correcting such errors is presented. The reference frequency can be swept in order to measure the frequency response of a system within a given spectrum. The continuous variation of the reference frequency produces a measurement error that depends on three factors: the sweep speed, the LIA low-pass filters, and the frequency response of the measured system. The proposed error prediction algorithm is based on the final value theorem of the Laplace transform. The correction method uses a double-sweep measurement. A mathematical analysis is presented and validated with computational simulations and experimental measurements.
-
Correlation of clinical predictions and surgical results in maxillary superior repositioning.
Tabrizi, Reza; Zamiri, Barbad; Kazemi, Hamidreza
2014-05-01
This is a prospective study to evaluate the accuracy of clinical predictions related to surgical results in subjects who underwent maxillary superior repositioning without anterior-posterior movement. Surgeons' predictions according to clinical (tooth show at rest and at the maximum smile) and cephalometric evaluation were documented for the amount of maxillary superior repositioning. Overcorrection or undercorrection was documented for every subject 1 year after the operations. Receiver operating characteristic curve test was used to find a cutoff point in prediction errors and to determine positive predictive value (PPV) and negative predictive value. Forty subjects (14 males and 26 females) were studied. Results showed a significant difference between changes in the tooth show at rest and at the maximum smile line before and after surgery. Analysis of the data demonstrated no correlation between the predictive data and the surgical results. The incidence of undercorrection (25%) was more common than overcorrection (7.5%). The cutoff point for errors in predictions was 5 mm for tooth show at rest and 15 mm at the maximum smile. When the amount of the presurgical tooth show at rest was more than 5 mm, 50.5% of clinical predictions did not match the clinical results (PPV), and 75% of clinical predictions showed the same results when the tooth show was less than 5 mm (negative predictive value). When the amount of presurgical tooth shown in the maximum smile line was more than 15 mm, 75% of clinical predictions did not match with clinical results (PPV), and 25% of the predictions had the same results because the tooth show at the maximum smile was lower than 15 mm. Clinical predictions according to the tooth show at rest and at the maximum smile have a poor correlation with clinical results in maxillary superior repositioning for vertical maxillary excess. The risk of errors in predictions increased when the amount of superior repositioning of the maxilla increased. Generally, surgeons have a tendency to undercorrect rather than overcorrect, although clinical prediction is an original guideline for surgeons, and it may be associated with variable clinical results.
-
The P-chain: relating sentence production and its disorders to comprehension and acquisition
Dell, Gary S.; Chang, Franklin
2014-01-01
This article introduces the P-chain, an emerging framework for theory in psycholinguistics that unifies research on comprehension, production and acquisition. The framework proposes that language processing involves incremental prediction, which is carried out by the production system. Prediction necessarily leads to prediction error, which drives learning, including both adaptive adjustment to the mature language processing system as well as language acquisition. To illustrate the P-chain, we review the Dual-path model of sentence production, a connectionist model that explains structural priming in production and a number of facts about language acquisition. The potential of this and related models for explaining acquired and developmental disorders of sentence production is discussed. PMID:24324238
-
The P-chain: relating sentence production and its disorders to comprehension and acquisition.
Dell, Gary S; Chang, Franklin
2014-01-01
This article introduces the P-chain, an emerging framework for theory in psycholinguistics that unifies research on comprehension, production and acquisition. The framework proposes that language processing involves incremental prediction, which is carried out by the production system. Prediction necessarily leads to prediction error, which drives learning, including both adaptive adjustment to the mature language processing system as well as language acquisition. To illustrate the P-chain, we review the Dual-path model of sentence production, a connectionist model that explains structural priming in production and a number of facts about language acquisition. The potential of this and related models for explaining acquired and developmental disorders of sentence production is discussed.
-
Prakash, Varuna; Koczmara, Christine; Savage, Pamela; Trip, Katherine; Stewart, Janice; McCurdie, Tara; Cafazzo, Joseph A; Trbovich, Patricia
2014-11-01
Nurses are frequently interrupted during medication verification and administration; however, few interventions exist to mitigate resulting errors, and the impact of these interventions on medication safety is poorly understood. The study objectives were to (A) assess the effects of interruptions on medication verification and administration errors, and (B) design and test the effectiveness of targeted interventions at reducing these errors. The study focused on medication verification and administration in an ambulatory chemotherapy setting. A simulation laboratory experiment was conducted to determine interruption-related error rates during specific medication verification and administration tasks. Interventions to reduce these errors were developed through a participatory design process, and their error reduction effectiveness was assessed through a postintervention experiment. Significantly more nurses committed medication errors when interrupted than when uninterrupted. With use of interventions when interrupted, significantly fewer nurses made errors in verifying medication volumes contained in syringes (16/18; 89% preintervention error rate vs 11/19; 58% postintervention error rate; p=0.038; Fisher's exact test) and programmed in ambulatory pumps (17/18; 94% preintervention vs 11/19; 58% postintervention; p=0.012). The rate of error commission significantly decreased with use of interventions when interrupted during intravenous push (16/18; 89% preintervention vs 6/19; 32% postintervention; p=0.017) and pump programming (7/18; 39% preintervention vs 1/19; 5% postintervention; p=0.017). No statistically significant differences were observed for other medication verification tasks. Interruptions can lead to medication verification and administration errors. Interventions were highly effective at reducing unanticipated errors of commission in medication administration tasks, but showed mixed effectiveness at reducing predictable errors of detection in medication verification tasks. These findings can be generalised and adapted to mitigate interruption-related errors in other settings where medication verification and administration are required. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
-
Prakash, Varuna; Koczmara, Christine; Savage, Pamela; Trip, Katherine; Stewart, Janice; McCurdie, Tara; Cafazzo, Joseph A; Trbovich, Patricia
2014-01-01
Background Nurses are frequently interrupted during medication verification and administration; however, few interventions exist to mitigate resulting errors, and the impact of these interventions on medication safety is poorly understood. Objective The study objectives were to (A) assess the effects of interruptions on medication verification and administration errors, and (B) design and test the effectiveness of targeted interventions at reducing these errors. Methods The study focused on medication verification and administration in an ambulatory chemotherapy setting. A simulation laboratory experiment was conducted to determine interruption-related error rates during specific medication verification and administration tasks. Interventions to reduce these errors were developed through a participatory design process, and their error reduction effectiveness was assessed through a postintervention experiment. Results Significantly more nurses committed medication errors when interrupted than when uninterrupted. With use of interventions when interrupted, significantly fewer nurses made errors in verifying medication volumes contained in syringes (16/18; 89% preintervention error rate vs 11/19; 58% postintervention error rate; p=0.038; Fisher's exact test) and programmed in ambulatory pumps (17/18; 94% preintervention vs 11/19; 58% postintervention; p=0.012). The rate of error commission significantly decreased with use of interventions when interrupted during intravenous push (16/18; 89% preintervention vs 6/19; 32% postintervention; p=0.017) and pump programming (7/18; 39% preintervention vs 1/19; 5% postintervention; p=0.017). No statistically significant differences were observed for other medication verification tasks. Conclusions Interruptions can lead to medication verification and administration errors. Interventions were highly effective at reducing unanticipated errors of commission in medication administration tasks, but showed mixed effectiveness at reducing predictable errors of detection in medication verification tasks. These findings can be generalised and adapted to mitigate interruption-related errors in other settings where medication verification and administration are required. PMID:24906806
-
Uncertainties of predictions from parton distributions II: theoretical errors
NASA Astrophysics Data System (ADS)
Martin, A. D.; Roberts, R. G.; Stirling, W. J.; Thorne, R. S.
2004-06-01
We study the uncertainties in parton distributions, determined in global fits to deep inelastic and related hard scattering data, due to so-called theoretical errors. Amongst these, we include potential errors due to the change of perturbative order (NLO to NNLO), ln(1/x) and ln(1-x) effects, absorptive corrections and higher-twist contributions. We investigate these uncertainties both by including explicit corrections to our standard global analysis and by examining the sensitivity to changes of the x, Q 2, W 2 cuts on the data that are fitted. In this way we expose those kinematic regions where the conventional DGLAP description is inadequate. As a consequence we obtain a set of NLO, and of NNLO, conservative partons where the data are fully consistent with DGLAP evolution, but over a restricted kinematic domain. We also examine the potential effects of such issues as the choice of input parametrisation, heavy target corrections, assumptions about the strange quark sea and isospin violation. Hence we are able to compare the theoretical errors with those uncertainties due to errors on the experimental measurements, which we studied previously. We use W and Higgs boson production at the Tevatron and the LHC as explicit examples of the uncertainties arising from parton distributions. For many observables the theoretical error is dominant, but for the cross section for W production at the Tevatron both the theoretical and experimental uncertainties are small, and hence the NNLO prediction may serve as a valuable luminosity monitor.
-
Family matters: dyadic agreement in end-of-life medical decision making.
Schmid, Bettina; Allen, Rebecca S; Haley, Philip P; Decoster, Jamie
2010-04-01
We examined race/ethnicity and cultural context within hypothetical end-of-life medical decision scenarios and its influence on patient-proxy agreement. Family dyads consisting of an older adult and 1 family member, typically an adult child, responded to questions regarding the older adult's preferences for cardiopulmonary resuscitation, artificial feeding and fluids, and palliative care in hypothetical illness scenarios. The responses of 34 Caucasian dyads and 30 African American dyads were compared to determine the extent to which family members could accurately predict the treatment preferences of their older relative. We found higher treatment preference agreement among African American dyads compared with Caucasian dyads when considering overall raw difference scores (i.e., overtreatment errors can compensate for undertreatment errors). Prior advance care planning moderated the effect such that lower levels of advance care planning predicted undertreatment errors among African American proxies and overtreatment errors among Caucasian proxies. In contrast, no racial/ethnic differences in treatment preference agreement were found within absolute difference scores (i.e., total error, regardless of the direction of error). This project is one of the first to examine the mediators and moderators of dyadic racial/cultural differences in treatment preference agreement for end-of-life care in hypothetical illness scenarios. Future studies should use mixed method approaches to explore underlying factors for racial differences in patient-proxy agreement as a basis for developing culturally sensitive interventions to reduce racial disparities in end-of-life care options.
-
Cerebral metabolic dysfunction and impaired vigilance in recently abstinent methamphetamine abusers.
London, Edythe D; Berman, Steven M; Voytek, Bradley; Simon, Sara L; Mandelkern, Mark A; Monterosso, John; Thompson, Paul M; Brody, Arthur L; Geaga, Jennifer A; Hong, Michael S; Hayashi, Kiralee M; Rawson, Richard A; Ling, Walter
2005-11-15
Methamphetamine (MA) abusers have cognitive deficits, abnormal metabolic activity and structural deficits in limbic and paralimbic cortices, and reduced hippocampal volume. The links between cognitive impairment and these cerebral abnormalities are not established. We assessed cerebral glucose metabolism with [F-18]fluorodeoxyglucose positron emission tomography in 17 abstinent (4 to 7 days) methamphetamine users and 16 control subjects performing an auditory vigilance task and obtained structural magnetic resonance brain scans. Regional brain radioactivity served as a marker for relative glucose metabolism. Error rates on the task were related to regional radioactivity and hippocampal morphology. Methamphetamine users had higher error rates than control subjects on the vigilance task. The groups showed different relationships between error rates and relative activity in the anterior and middle cingulate gyrus and the insula. Whereas the MA user group showed negative correlations involving these regions, the control group showed positive correlations involving the cingulate cortex. Across groups, hippocampal metabolic and structural measures were negatively correlated with error rates. Dysfunction in the cingulate and insular cortices of recently abstinent MA abusers contribute to impaired vigilance and other cognitive functions requiring sustained attention. Hippocampal integrity predicts task performance in methamphetamine users as well as control subjects.
-
A crowdsourcing workflow for extracting chemical-induced disease relations from free text
Li, Tong Shu; Bravo, Àlex; Furlong, Laura I.; Good, Benjamin M.; Su, Andrew I.
2016-01-01
Relations between chemicals and diseases are one of the most queried biomedical interactions. Although expert manual curation is the standard method for extracting these relations from the literature, it is expensive and impractical to apply to large numbers of documents, and therefore alternative methods are required. We describe here a crowdsourcing workflow for extracting chemical-induced disease relations from free text as part of the BioCreative V Chemical Disease Relation challenge. Five non-expert workers on the CrowdFlower platform were shown each potential chemical-induced disease relation highlighted in the original source text and asked to make binary judgments about whether the text supported the relation. Worker responses were aggregated through voting, and relations receiving four or more votes were predicted as true. On the official evaluation dataset of 500 PubMed abstracts, the crowd attained a 0.505 F-score (0.475 precision, 0.540 recall), with a maximum theoretical recall of 0.751 due to errors with named entity recognition. The total crowdsourcing cost was $1290.67 ($2.58 per abstract) and took a total of 7 h. A qualitative error analysis revealed that 46.66% of sampled errors were due to task limitations and gold standard errors, indicating that performance can still be improved. All code and results are publicly available at https://github.com/SuLab/crowd_cid_relex Database URL: https://github.com/SuLab/crowd_cid_relex PMID:27087308
-
NASA Technical Reports Server (NTRS)
Bell, Thomas L.; Kundu, Prasun K.; Kummerow, Christian D.; Einaudi, Franco (Technical Monitor)
2000-01-01
Quantitative use of satellite-derived maps of monthly rainfall requires some measure of the accuracy of the satellite estimates. The rainfall estimate for a given map grid box is subject to both remote-sensing error and, in the case of low-orbiting satellites, sampling error due to the limited number of observations of the grid box provided by the satellite. A simple model of rain behavior predicts that Root-mean-square (RMS) random error in grid-box averages should depend in a simple way on the local average rain rate, and the predicted behavior has been seen in simulations using surface rain-gauge and radar data. This relationship was examined using satellite SSM/I data obtained over the western equatorial Pacific during TOGA COARE. RMS error inferred directly from SSM/I rainfall estimates was found to be larger than predicted from surface data, and to depend less on local rain rate than was predicted. Preliminary examination of TRMM microwave estimates shows better agreement with surface data. A simple method of estimating rms error in satellite rainfall estimates is suggested, based on quantities that can be directly computed from the satellite data.
-
Prediction of transmission distortion for wireless video communication: analysis.
Chen, Zhifeng; Wu, Dapeng
2012-03-01
Transmitting video over wireless is a challenging problem since video may be seriously distorted due to packet errors caused by wireless channels. The capability of predicting transmission distortion (i.e., video distortion caused by packet errors) can assist in designing video encoding and transmission schemes that achieve maximum video quality or minimum end-to-end video distortion. This paper is aimed at deriving formulas for predicting transmission distortion. The contribution of this paper is twofold. First, we identify the governing law that describes how the transmission distortion process evolves over time and analytically derive the transmission distortion formula as a closed-form function of video frame statistics, channel error statistics, and system parameters. Second, we identify, for the first time, two important properties of transmission distortion. The first property is that the clipping noise, which is produced by nonlinear clipping, causes decay of propagated error. The second property is that the correlation between motion-vector concealment error and propagated error is negative and has dominant impact on transmission distortion, compared with other correlations. Due to these two properties and elegant error/distortion decomposition, our formula provides not only more accurate prediction but also lower complexity than the existing methods.
-
NASA Astrophysics Data System (ADS)
Behmanesh, Iman; Yousefianmoghadam, Seyedsina; Nozari, Amin; Moaveni, Babak; Stavridis, Andreas
2018-07-01
This paper investigates the application of Hierarchical Bayesian model updating for uncertainty quantification and response prediction of civil structures. In this updating framework, structural parameters of an initial finite element (FE) model (e.g., stiffness or mass) are calibrated by minimizing error functions between the identified modal parameters and the corresponding parameters of the model. These error functions are assumed to have Gaussian probability distributions with unknown parameters to be determined. The estimated parameters of error functions represent the uncertainty of the calibrated model in predicting building's response (modal parameters here). The focus of this paper is to answer whether the quantified model uncertainties using dynamic measurement at building's reference/calibration state can be used to improve the model prediction accuracies at a different structural state, e.g., damaged structure. Also, the effects of prediction error bias on the uncertainty of the predicted values is studied. The test structure considered here is a ten-story concrete building located in Utica, NY. The modal parameters of the building at its reference state are identified from ambient vibration data and used to calibrate parameters of the initial FE model as well as the error functions. Before demolishing the building, six of its exterior walls were removed and ambient vibration measurements were also collected from the structure after the wall removal. These data are not used to calibrate the model; they are only used to assess the predicted results. The model updating framework proposed in this paper is applied to estimate the modal parameters of the building at its reference state as well as two damaged states: moderate damage (removal of four walls) and severe damage (removal of six walls). Good agreement is observed between the model-predicted modal parameters and those identified from vibration tests. Moreover, it is shown that including prediction error bias in the updating process instead of commonly-used zero-mean error function can significantly reduce the prediction uncertainties.
-
Sosic-Vasic, Zrinka; Ulrich, Martin; Ruchsow, Martin; Vasic, Nenad; Grön, Georg
2012-01-01
The present study investigated the association between traits of the Five Factor Model of Personality (Neuroticism, Extraversion, Openness for Experiences, Agreeableness, and Conscientiousness) and neural correlates of error monitoring obtained from a combined Eriksen-Flanker-Go/NoGo task during event-related functional magnetic resonance imaging in 27 healthy subjects. Individual expressions of personality traits were measured using the NEO-PI-R questionnaire. Conscientiousness correlated positively with error signaling in the left inferior frontal gyrus and adjacent anterior insula (IFG/aI). A second strong positive correlation was observed in the anterior cingulate gyrus (ACC). Neuroticism was negatively correlated with error signaling in the inferior frontal cortex possibly reflecting the negative inter-correlation between both scales observed on the behavioral level. Under present statistical thresholds no significant results were obtained for remaining scales. Aligning the personality trait of Conscientiousness with task accomplishment striving behavior the correlation in the left IFG/aI possibly reflects an inter-individually different involvement whenever task-set related memory representations are violated by the occurrence of errors. The strong correlations in the ACC may indicate that more conscientious subjects were stronger affected by these violations of a given task-set expressed by individually different, negatively valenced signals conveyed by the ACC upon occurrence of an error. Present results illustrate that for predicting individual responses to errors underlying personality traits should be taken into account and also lend external validity to the personality trait approach suggesting that personality constructs do reflect more than mere descriptive taxonomies.
-
NASA Astrophysics Data System (ADS)
Blackman, Jonathan; Field, Scott E.; Galley, Chad R.; Szilágyi, Béla; Scheel, Mark A.; Tiglio, Manuel; Hemberger, Daniel A.
2015-09-01
Simulating a binary black hole coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time. Using reduced order modeling techniques, we construct an accurate surrogate model, which is evaluated in a millisecond to a second, for numerical relativity (NR) waveforms from nonspinning binary black hole coalescences with mass ratios in [1, 10] and durations corresponding to about 15 orbits before merger. We assess the model's uncertainty and show that our modeling strategy predicts NR waveforms not used for the surrogate's training with errors nearly as small as the numerical error of the NR code. Our model includes all spherical-harmonic -2Yℓm waveform modes resolved by the NR code up to ℓ=8 . We compare our surrogate model to effective one body waveforms from 50 M⊙ to 300 M⊙ for advanced LIGO detectors and find that the surrogate is always more faithful (by at least an order of magnitude in most cases).
-
Blackman, Jonathan; Field, Scott E; Galley, Chad R; Szilágyi, Béla; Scheel, Mark A; Tiglio, Manuel; Hemberger, Daniel A
2015-09-18
Simulating a binary black hole coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time. Using reduced order modeling techniques, we construct an accurate surrogate model, which is evaluated in a millisecond to a second, for numerical relativity (NR) waveforms from nonspinning binary black hole coalescences with mass ratios in [1, 10] and durations corresponding to about 15 orbits before merger. We assess the model's uncertainty and show that our modeling strategy predicts NR waveforms not used for the surrogate's training with errors nearly as small as the numerical error of the NR code. Our model includes all spherical-harmonic _{-2}Y_{ℓm} waveform modes resolved by the NR code up to ℓ=8. We compare our surrogate model to effective one body waveforms from 50M_{⊙} to 300M_{⊙} for advanced LIGO detectors and find that the surrogate is always more faithful (by at least an order of magnitude in most cases).
-
Rectification of General Relativity, Experimental Verifications, and Errors of the Wheeler School
NASA Astrophysics Data System (ADS)
Lo, C. Y.
2013-09-01
General relativity is not yet consistent. Pauli has misinterpreted Einstein's 1916 equivalence principle that can derive a valid field equation. The Wheeler School has distorted Einstein's 1916 principle to be his 1911 assumption of equivalence, and created new errors. Moreover, errors on dynamic solutions have allowed the implicit assumption of a unique coupling sign that violates the principle of causality. This leads to the space-time singularity theorems of Hawking and Penrose who "refute" applications for microscopic phenomena, and obstruct efforts to obtain a valid equation for the dynamic case. These errors also explain the mistakes in the press release of the 1993 Nobel Committee, who was unaware of the non-existence of dynamic solutions. To illustrate the damages to education, the MIT Open Course Phys. 8.033 is chosen. Rectification of errors confirms that E = mc2 is only conditionally valid, and leads to the discovery of the charge-mass interaction that is experimentally confirmed and subsequently the unification of gravitation and electromagnetism. The charge-mass interaction together with the unification predicts the weight reduction (instead of increment) of charged capacitors and heated metals, and helps to explain NASA's Pioneer anomaly and potentially other anomalies as well.
-
Kumar, Poornima; Eickhoff, Simon B.; Dombrovski, Alexandre Y.
2015-01-01
Reinforcement learning describes motivated behavior in terms of two abstract signals. The representation of discrepancies between expected and actual rewards/punishments – prediction error – is thought to update the expected value of actions and predictive stimuli. Electrophysiological and lesion studies suggest that mesostriatal prediction error signals control behavior through synaptic modification of cortico-striato-thalamic networks. Signals in the ventromedial prefrontal and orbitofrontal cortex are implicated in representing expected value. To obtain unbiased maps of these representations in the human brain, we performed a meta-analysis of functional magnetic resonance imaging studies that employed algorithmic reinforcement learning models, across a variety of experimental paradigms. We found that the ventral striatum (medial and lateral) and midbrain/thalamus represented reward prediction errors, consistent with animal studies. Prediction error signals were also seen in the frontal operculum/insula, particularly for social rewards. In Pavlovian studies, striatal prediction error signals extended into the amygdala, while instrumental tasks engaged the caudate. Prediction error maps were sensitive to the model-fitting procedure (fixed or individually-estimated) and to the extent of spatial smoothing. A correlate of expected value was found in a posterior region of the ventromedial prefrontal cortex, caudal and medial to the orbitofrontal regions identified in animal studies. These findings highlight a reproducible motif of reinforcement learning in the cortico-striatal loops and identify methodological dimensions that may influence the reproducibility of activation patterns across studies. PMID:25665667
-
Accuracy of Robotic Radiosurgical Liver Treatment Throughout the Respiratory Cycle
DOE Office of Scientific and Technical Information (OSTI.GOV)
Winter, Jeff D.; Wong, Raimond; Swaminath, Anand
Purpose: To quantify random uncertainties in robotic radiosurgical treatment of liver lesions with real-time respiratory motion management. Methods and Materials: We conducted a retrospective analysis of 27 liver cancer patients treated with robotic radiosurgery over 118 fractions. The robotic radiosurgical system uses orthogonal x-ray images to determine internal target position and correlates this position with an external surrogate to provide robotic corrections of linear accelerator positioning. Verification and update of this internal–external correlation model was achieved using periodic x-ray images collected throughout treatment. To quantify random uncertainties in targeting, we analyzed logged tracking information and isolated x-ray images collected immediately beforemore » beam delivery. For translational correlation errors, we quantified the difference between correlation model–estimated target position and actual position determined by periodic x-ray imaging. To quantify prediction errors, we computed the mean absolute difference between the predicted coordinates and actual modeled position calculated 115 milliseconds later. We estimated overall random uncertainty by quadratically summing correlation, prediction, and end-to-end targeting errors. We also investigated relationships between tracking errors and motion amplitude using linear regression. Results: The 95th percentile absolute correlation errors in each direction were 2.1 mm left–right, 1.8 mm anterior–posterior, 3.3 mm cranio–caudal, and 3.9 mm 3-dimensional radial, whereas 95th percentile absolute radial prediction errors were 0.5 mm. Overall 95th percentile random uncertainty was 4 mm in the radial direction. Prediction errors were strongly correlated with modeled target amplitude (r=0.53-0.66, P<.001), whereas only weak correlations existed for correlation errors. Conclusions: Study results demonstrate that model correlation errors are the primary random source of uncertainty in Cyberknife liver treatment and, unlike prediction errors, are not strongly correlated with target motion amplitude. Aggregate 3-dimensional radial position errors presented here suggest the target will be within 4 mm of the target volume for 95% of the beam delivery.« less
-
NASA Astrophysics Data System (ADS)
De Felice, Matteo; Petitta, Marcello; Ruti, Paolo
2014-05-01
Photovoltaic diffusion is steadily growing on Europe, passing from a capacity of almost 14 GWp in 2011 to 21.5 GWp in 2012 [1]. Having accurate forecast is needed for planning and operational purposes, with the possibility to model and predict solar variability at different time-scales. This study examines the predictability of daily surface solar radiation comparing ECMWF operational forecasts with CM-SAF satellite measurements on the Meteosat (MSG) full disk domain. Operational forecasts used are the IFS system up to 10 days and the System4 seasonal forecast up to three months. Forecast are analysed considering average and variance of errors, showing error maps and average on specific domains with respect to prediction lead times. In all the cases, forecasts are compared with predictions obtained using persistence and state-of-art time-series models. We can observe a wide range of errors, with the performance of forecasts dramatically affected by orography and season. Lower errors are on southern Italy and Spain, with errors on some areas consistently under 10% up to ten days during summer (JJA). Finally, we conclude the study with some insight on how to "translate" the error on solar radiation to error on solar power production using available production data from solar power plants. [1] EurObserver, "Baromètre Photovoltaïque, Le journal des énergies renouvables, April 2012."
-
Relative location prediction in CT scan images using convolutional neural networks.
Guo, Jiajia; Du, Hongwei; Zhu, Jianyue; Yan, Ting; Qiu, Bensheng
2018-07-01
Relative location prediction in computed tomography (CT) scan images is a challenging problem. Many traditional machine learning methods have been applied in attempts to alleviate this problem. However, the accuracy and speed of these methods cannot meet the requirement of medical scenario. In this paper, we propose a regression model based on one-dimensional convolutional neural networks (CNN) to determine the relative location of a CT scan image both quickly and precisely. In contrast to other common CNN models that use a two-dimensional image as an input, the input of this CNN model is a feature vector extracted by a shape context algorithm with spatial correlation. Normalization via z-score is first applied as a pre-processing step. Then, in order to prevent overfitting and improve model's performance, 20% of the elements of the feature vectors are randomly set to zero. This CNN model consists primarily of three one-dimensional convolutional layers, three dropout layers and two fully-connected layers with appropriate loss functions. A public dataset is employed to validate the performance of the proposed model using a 5-fold cross validation. Experimental results demonstrate an excellent performance of the proposed model when compared with contemporary techniques, achieving a median absolute error of 1.04 cm and mean absolute error of 1.69 cm. The time taken for each relative location prediction is approximately 2 ms. Results indicate that the proposed CNN method can contribute to a quick and accurate relative location prediction in CT scan images, which can improve efficiency of the medical picture archiving and communication system in the future. Copyright © 2018 Elsevier B.V. All rights reserved.
-
Debiasing affective forecasting errors with targeted, but not representative, experience narratives.
Shaffer, Victoria A; Focella, Elizabeth S; Scherer, Laura D; Zikmund-Fisher, Brian J
2016-10-01
To determine whether representative experience narratives (describing a range of possible experiences) or targeted experience narratives (targeting the direction of forecasting bias) can reduce affective forecasting errors, or errors in predictions of experiences. In Study 1, participants (N=366) were surveyed about their experiences with 10 common medical events. Those who had never experienced the event provided ratings of predicted discomfort and those who had experienced the event provided ratings of actual discomfort. Participants making predictions were randomly assigned to either the representative experience narrative condition or the control condition in which they made predictions without reading narratives. In Study 2, participants (N=196) were again surveyed about their experiences with these 10 medical events, but participants making predictions were randomly assigned to either the targeted experience narrative condition or the control condition. Affective forecasting errors were observed in both studies. These forecasting errors were reduced with the use of targeted experience narratives (Study 2) but not representative experience narratives (Study 1). Targeted, but not representative, narratives improved the accuracy of predicted discomfort. Public collections of patient experiences should favor stories that target affective forecasting biases over stories representing the range of possible experiences. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
-
Confidence intervals in Flow Forecasting by using artificial neural networks
NASA Astrophysics Data System (ADS)
Panagoulia, Dionysia; Tsekouras, George
2014-05-01
One of the major inadequacies in implementation of Artificial Neural Networks (ANNs) for flow forecasting is the development of confidence intervals, because the relevant estimation cannot be implemented directly, contrasted to the classical forecasting methods. The variation in the ANN output is a measure of uncertainty in the model predictions based on the training data set. Different methods for uncertainty analysis, such as bootstrap, Bayesian, Monte Carlo, have already proposed for hydrologic and geophysical models, while methods for confidence intervals, such as error output, re-sampling, multi-linear regression adapted to ANN have been used for power load forecasting [1-2]. The aim of this paper is to present the re-sampling method for ANN prediction models and to develop this for flow forecasting of the next day. The re-sampling method is based on the ascending sorting of the errors between real and predicted values for all input vectors. The cumulative sample distribution function of the prediction errors is calculated and the confidence intervals are estimated by keeping the intermediate value, rejecting the extreme values according to the desired confidence levels, and holding the intervals symmetrical in probability. For application of the confidence intervals issue, input vectors are used from the Mesochora catchment in western-central Greece. The ANN's training algorithm is the stochastic training back-propagation process with decreasing functions of learning rate and momentum term, for which an optimization process is conducted regarding the crucial parameters values, such as the number of neurons, the kind of activation functions, the initial values and time parameters of learning rate and momentum term etc. Input variables are historical data of previous days, such as flows, nonlinearly weather related temperatures and nonlinearly weather related rainfalls based on correlation analysis between the under prediction flow and each implicit input variable of different ANN structures [3]. The performance of each ANN structure is evaluated by the voting analysis based on eleven criteria, which are the root mean square error (RMSE), the correlation index (R), the mean absolute percentage error (MAPE), the mean percentage error (MPE), the mean percentage error (ME), the percentage volume in errors (VE), the percentage error in peak (MF), the normalized mean bias error (NMBE), the normalized root mean bias error (NRMSE), the Nash-Sutcliffe model efficiency coefficient (E) and the modified Nash-Sutcliffe model efficiency coefficient (E1). The next day flow for the test set is calculated using the best ANN structure's model. Consequently, the confidence intervals of various confidence levels for training, evaluation and test sets are compared in order to explore the generalisation dynamics of confidence intervals from training and evaluation sets. [1] H.S. Hippert, C.E. Pedreira, R.C. Souza, "Neural networks for short-term load forecasting: A review and evaluation," IEEE Trans. on Power Systems, vol. 16, no. 1, 2001, pp. 44-55. [2] G. J. Tsekouras, N.E. Mastorakis, F.D. Kanellos, V.T. Kontargyri, C.D. Tsirekis, I.S. Karanasiou, Ch.N. Elias, A.D. Salis, P.A. Kontaxis, A.A. Gialketsi: "Short term load forecasting in Greek interconnected power system using ANN: Confidence Interval using a novel re-sampling technique with corrective Factor", WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing, (CSECS '10), Vouliagmeni, Athens, Greece, December 29-31, 2010. [3] D. Panagoulia, I. Trichakis, G. J. Tsekouras: "Flow Forecasting via Artificial Neural Networks - A Study for Input Variables conditioned on atmospheric circulation", European Geosciences Union, General Assembly 2012 (NH1.1 / AS1.16 - Extreme meteorological and hydrological events induced by severe weather and climate change), Vienna, Austria, 22-27 April 2012.
-
On the internal target model in a tracking task
NASA Technical Reports Server (NTRS)
Caglayan, A. K.; Baron, S.
1981-01-01
An optimal control model for predicting operator's dynamic responses and errors in target tracking ability is summarized. The model, which predicts asymmetry in the tracking data, is dependent on target maneuvers and trajectories. Gunners perception, decision making, control, and estimate of target positions and velocity related to crossover intervals are discussed. The model provides estimates for means, standard deviations, and variances for variables investigated and for operator estimates of future target positions and velocities.
-
Application of linear regression analysis in accuracy assessment of rolling force calculations
NASA Astrophysics Data System (ADS)
Poliak, E. I.; Shim, M. K.; Kim, G. S.; Choo, W. Y.
1998-10-01
Efficient operation of the computational models employed in process control systems require periodical assessment of the accuracy of their predictions. Linear regression is proposed as a tool which allows separate systematic and random prediction errors from those related to measurements. A quantitative characteristic of the model predictive ability is introduced in addition to standard statistical tests for model adequacy. Rolling force calculations are considered as an example for the application. However, the outlined approach can be used to assess the performance of any computational model.
-
ANOPP programmer's reference manual for the executive System. [aircraft noise prediction program
NASA Technical Reports Server (NTRS)
Gillian, R. E.; Brown, C. G.; Bartlett, R. W.; Baucom, P. H.
1977-01-01
Documentation for the Aircraft Noise Prediction Program as of release level 01/00/00 is presented in a manual designed for programmers having a need for understanding the internal design and logical concepts of the executive system software. Emphasis is placed on providing sufficient information to modify the system for enhancements or error correction. The ANOPP executive system includes software related to operating system interface, executive control, and data base management for the Aircraft Noise Prediction Program. It is written in Fortran IV for use on CDC Cyber series of computers.
-
Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time.
Limongi, Roberto; Silva, Angélica M
2016-11-01
The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production - where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.
-
Tropical forecasting - Predictability perspective
NASA Technical Reports Server (NTRS)
Shukla, J.
1989-01-01
Results are presented of classical predictability studies and forecast experiments with observed initial conditions to show the nature of initial error growth and final error equilibration for the tropics and midlatitudes, separately. It is found that the theoretical upper limit of tropical circulation predictability is far less than for midlatitudes. The error growth for a complete general circulation model is compared to a dry version of the same model in which there is no prognostic equation for moisture, and diabatic heat sources are prescribed. It is found that the growth rate of synoptic-scale errors for the dry model is significantly smaller than for the moist model, suggesting that the interactions between dynamics and moist processes are among the important causes of atmospheric flow predictability degradation. Results are then presented of numerical experiments showing that correct specification of the slowly varying boundary condition of SST produces significant improvement in the prediction of time-averaged circulation and rainfall over the tropics.
-
Generalized Variance Function Applications in Forestry
James Alegria; Charles T. Scott; Charles T. Scott
1991-01-01
Adequately predicting the sampling errors of tabular data can reduce printing costs by eliminating the need to publish separate sampling error tables. Two generalized variance functions (GVFs) found in the literature and three GVFs derived for this study were evaluated for their ability to predict the sampling error of tabular forestry estimates. The recommended GVFs...
-
Mechanism reduction for multicomponent surrogates: A case study using toluene reference fuels
DOE Office of Scientific and Technical Information (OSTI.GOV)
Niemeyer, Kyle E.; Sung, Chih-Jen
Strategies and recommendations for performing skeletal reductions of multicomponent surrogate fuels are presented, through the generation and validation of skeletal mechanisms for a three-component toluene reference fuel. Using the directed relation graph with error propagation and sensitivity analysis method followed by a further unimportant reaction elimination stage, skeletal mechanisms valid over comprehensive and high-temperature ranges of conditions were developed at varying levels of detail. These skeletal mechanisms were generated based on autoignition simulations, and validation using ignition delay predictions showed good agreement with the detailed mechanism in the target range of conditions. When validated using phenomena other than autoignition, suchmore » as perfectly stirred reactor and laminar flame propagation, tight error control or more restrictions on the reduction during the sensitivity analysis stage were needed to ensure good agreement. In addition, tight error limits were needed for close prediction of ignition delay when varying the mixture composition away from that used for the reduction. In homogeneous compression-ignition engine simulations, the skeletal mechanisms closely matched the point of ignition and accurately predicted species profiles for lean to stoichiometric conditions. Furthermore, the efficacy of generating a multicomponent skeletal mechanism was compared to combining skeletal mechanisms produced separately for neat fuel components; using the same error limits, the latter resulted in a larger skeletal mechanism size that also lacked important cross reactions between fuel components. Based on the present results, general guidelines for reducing detailed mechanisms for multicomponent fuels are discussed.« less
-
Mechanism reduction for multicomponent surrogates: A case study using toluene reference fuels
Niemeyer, Kyle E.; Sung, Chih-Jen
2014-11-01
Strategies and recommendations for performing skeletal reductions of multicomponent surrogate fuels are presented, through the generation and validation of skeletal mechanisms for a three-component toluene reference fuel. Using the directed relation graph with error propagation and sensitivity analysis method followed by a further unimportant reaction elimination stage, skeletal mechanisms valid over comprehensive and high-temperature ranges of conditions were developed at varying levels of detail. These skeletal mechanisms were generated based on autoignition simulations, and validation using ignition delay predictions showed good agreement with the detailed mechanism in the target range of conditions. When validated using phenomena other than autoignition, suchmore » as perfectly stirred reactor and laminar flame propagation, tight error control or more restrictions on the reduction during the sensitivity analysis stage were needed to ensure good agreement. In addition, tight error limits were needed for close prediction of ignition delay when varying the mixture composition away from that used for the reduction. In homogeneous compression-ignition engine simulations, the skeletal mechanisms closely matched the point of ignition and accurately predicted species profiles for lean to stoichiometric conditions. Furthermore, the efficacy of generating a multicomponent skeletal mechanism was compared to combining skeletal mechanisms produced separately for neat fuel components; using the same error limits, the latter resulted in a larger skeletal mechanism size that also lacked important cross reactions between fuel components. Based on the present results, general guidelines for reducing detailed mechanisms for multicomponent fuels are discussed.« less
-
Water quality management using statistical analysis and time-series prediction model
NASA Astrophysics Data System (ADS)
Parmar, Kulwinder Singh; Bhardwaj, Rashmi
2014-12-01
This paper deals with water quality management using statistical analysis and time-series prediction model. The monthly variation of water quality standards has been used to compare statistical mean, median, mode, standard deviation, kurtosis, skewness, coefficient of variation at Yamuna River. Model validated using R-squared, root mean square error, mean absolute percentage error, maximum absolute percentage error, mean absolute error, maximum absolute error, normalized Bayesian information criterion, Ljung-Box analysis, predicted value and confidence limits. Using auto regressive integrated moving average model, future water quality parameters values have been estimated. It is observed that predictive model is useful at 95 % confidence limits and curve is platykurtic for potential of hydrogen (pH), free ammonia, total Kjeldahl nitrogen, dissolved oxygen, water temperature (WT); leptokurtic for chemical oxygen demand, biochemical oxygen demand. Also, it is observed that predicted series is close to the original series which provides a perfect fit. All parameters except pH and WT cross the prescribed limits of the World Health Organization /United States Environmental Protection Agency, and thus water is not fit for drinking, agriculture and industrial use.
-
NASA Astrophysics Data System (ADS)
Xu, H.; Luo, L.; Wu, Z.
2016-12-01
Drought, regarded as one of the major disasters all over the world, is not always easy to detect and forecast. Hydrological models coupled with Numerical Weather Prediction (NWP) has become a relatively effective method for drought monitoring and prediction. The accuracy of hydrological initial condition (IC) and the skill of NWP precipitation forecast can both heavily affect the quality and skill of hydrological forecast. In the study, the Variable Infiltration Capacity (VIC) model and Global Environmental Multi-scale (GEM) model were used to investigate the roles of IC and NWP forecast accuracy on hydrological predictions. A rev-ESP type experiment was conducted for a number of drought events in the Huaihe river basin. The experiment suggests that errors in ICs indeed affect the drought simulations by VIC and thus the drought monitoring. Although errors introduced in the ICs diminish gradually, the influence sometimes can last beyond 12 months. Using the soil moisture anomaly percentage index (SMAPI) as the metric to measure drought severity for the study region, we are able to quantify that time scale of influence from IC ranges. The analysis shows that the time scale is directly related to the magnitude of the introduced IC range and the average precipitation intensity. In order to explore how systematic bias correction in GEM forecasted precipitation can affect precipitation and hydrological forecast, we then both used station and gridded observations to eliminate biases of forecasted data. Meanwhile, different precipitation inputs with corrected data during drought process were conducted by VIC to investigate the changes of drought simulations, thus demonstrated short-term rolling drought prediction using a better performed corrected precipitation forecast. There is a word limit on the length of the abstract. So make sure your abstract fits the requirement. If this version is too long, try to shorten it as much as you can.
-
Maltreated children's memory: accuracy, suggestibility, and psychopathology.
Eisen, Mitchell L; Goodman, Gail S; Qin, Jianjian; Davis, Suzanne; Crayton, John
2007-11-01
Memory, suggestibility, stress arousal, and trauma-related psychopathology were examined in 328 3- to 16-year-olds involved in forensic investigations of abuse and neglect. Children's memory and suggestibility were assessed for a medical examination and venipuncture. Being older and scoring higher in cognitive functioning were related to fewer inaccuracies. In addition, cortisol level and trauma symptoms in children who reported more dissociative tendencies were associated with increased memory error, whereas cortisol level and trauma symptoms were not associated with increased error for children who reported fewer dissociative tendencies. Sexual and/or physical abuse predicted greater accuracy. The study contributes important new information to scientific understanding of maltreatment, psychopathology, and eyewitness memory in children. (c) 2007 APA.
-
SEC proton prediction model: verification and analysis.
Balch, C C
1999-06-01
This paper describes a model that has been used at the NOAA Space Environment Center since the early 1970s as a guide for the prediction of solar energetic particle events. The algorithms for proton event probability, peak flux, and rise time are described. The predictions are compared with observations. The current model shows some ability to distinguish between proton event associated flares and flares that are not associated with proton events. The comparisons of predicted and observed peak flux show considerable scatter, with an rms error of almost an order of magnitude. Rise time comparisons also show scatter, with an rms error of approximately 28 h. The model algorithms are analyzed using historical data and improvements are suggested. Implementation of the algorithm modifications reduces the rms error in the log10 of the flux prediction by 21%, and the rise time rms error by 31%. Improvements are also realized in the probability prediction by deriving the conditional climatology for proton event occurrence given flare characteristics.
-
Predictability of the Arctic sea ice edge
NASA Astrophysics Data System (ADS)
Goessling, H. F.; Tietsche, S.; Day, J. J.; Hawkins, E.; Jung, T.
2016-02-01
Skillful sea ice forecasts from days to years ahead are becoming increasingly important for the operation and planning of human activities in the Arctic. Here we analyze the potential predictability of the Arctic sea ice edge in six climate models. We introduce the integrated ice-edge error (IIEE), a user-relevant verification metric defined as the area where the forecast and the "truth" disagree on the ice concentration being above or below 15%. The IIEE lends itself to decomposition into an absolute extent error, corresponding to the common sea ice extent error, and a misplacement error. We find that the often-neglected misplacement error makes up more than half of the climatological IIEE. In idealized forecast ensembles initialized on 1 July, the IIEE grows faster than the absolute extent error. This means that the Arctic sea ice edge is less predictable than sea ice extent, particularly in September, with implications for the potential skill of end-user relevant forecasts.
-
The prediction of speech intelligibility in classrooms using computer models
NASA Astrophysics Data System (ADS)
Dance, Stephen; Dentoni, Roger
2005-04-01
Two classrooms were measured and modeled using the industry standard CATT model and the Web model CISM. Sound levels, reverberation times and speech intelligibility were predicted in these rooms using data for 7 octave bands. It was found that overall sound levels could be predicted to within 2 dB by both models. However, overall reverberation time was found to be accurately predicted by CATT 14% prediction error, but not by CISM, 41% prediction error. This compared to a 30% prediction error using classical theory. As for STI: CATT predicted within 11%, CISM to within 3% and Sabine to within 28% of the measured value. It should be noted that CISM took approximately 15 seconds to calculate, while CATT took 15 minutes. CISM is freely available on-line at www.whyverne.co.uk/acoustics/Pages/cism/cism.html
-
Ballesteros Peña, Sendoa
2013-04-01
To estimate the frequency of therapeutic errors and to evaluate the diagnostic accuracy in the recognition of shockable rhythms by automated external defibrillators. A retrospective descriptive study. Nine basic life support units from Biscay (Spain). Included 201 patients with cardiac arrest, since 2006 to 2011. The study was made of the suitability of treatment (shock or not) after each analysis and medical errors identified. The sensitivity, specificity and predictive values with 95% confidence intervals were then calculated. A total of 811 electrocardiographic rhythm analyses were obtained, of which 120 (14.1%), from 30 patients, corresponded to shockable rhythms. Sensitivity and specificity for appropriate automated external defibrillators management of a shockable rhythm were 85% (95% CI, 77.5% to 90.3%) and 100% (95% CI, 99.4% to 100%), respectively. Positive and negative predictive values were 100% (95% CI, 96.4% to 100%) and 97.5% (95% CI, 96% to 98.4%), respectively. There were 18 (2.2%; 95% CI, 1.3% to 3.5%) errors associated with defibrillator management, all relating to cases of shockable rhythms that were not shocked. One error was operator dependent, 6 were defibrillator dependent (caused by interaction of pacemakers), and 11 were unclassified. Automated external defibrillators have a very high specificity and moderately high sensitivity. There are few operator dependent errors. Implanted pacemakers interfere with defibrillator analyses. Copyright © 2012 Elsevier España, S.L. All rights reserved.
-
DOE Office of Scientific and Technical Information (OSTI.GOV)
Fang, Zongtang; Both, Johan; Li, Shenggang
The heats of formation and the normalized clustering energies (NCEs) for the group 4 and group 6 transition metal oxide (TMO) trimers and tetramers have been calculated by the Feller-Peterson-Dixon (FPD) method. The heats of formation predicted by the FPD method do not differ much from those previously derived from the NCEs at the CCSD(T)/aT level except for the CrO3 nanoclusters. New and improved heats of formation for Cr3O9 and Cr4O12 were obtained using PW91 orbitals instead of Hartree-Fock (HF) orbitals. Diffuse functions are necessary to predict accurate heats of formation. The fluoride affinities (FAs) are calculated with the CCSD(T)more » method. The relative energies (REs) of different isomers, NCEs, electron affinities (EAs), and FAs of (MO2)n ( M = Ti, Zr, Hf, n = 1 – 4 ) and (MO3)n ( M = Cr, Mo, W, n = 1 – 3) clusters have been benchmarked with 55 exchange-correlation DFT functionals including both pure and hybrid types. The absolute errors of the DFT results are mostly less than ±10 kcal/mol for the NCEs and the EAs, and less than ±15 kcal/mol for the FAs. Hybrid functionals usually perform better than the pure functionals for the REs and NCEs. The performance of the two types of functionals in predicting EAs and FAs is comparable. The B1B95 and PBE1PBE functionals provide reliable energetic properties for most isomers. Long range corrected pure functionals usually give poor FAs. The standard deviation of the absolute error is always close to the mean errors and the probability distributions of the DFT errors are often not Gaussian (normal). The breadth of the distribution of errors and the maximum probability are dependent on the energy property and the isomer.« less
-
Ciufolini, Ignazio; Paolozzi, Antonio; Pavlis, Erricos C; Koenig, Rolf; Ries, John; Gurzadyan, Vahe; Matzner, Richard; Penrose, Roger; Sindoni, Giampiero; Paris, Claudio; Khachatryan, Harutyun; Mirzoyan, Sergey
2016-01-01
We present a test of general relativity, the measurement of the Earth's dragging of inertial frames. Our result is obtained using about 3.5 years of laser-ranged observations of the LARES, LAGEOS, and LAGEOS 2 laser-ranged satellites together with the Earth gravity field model GGM05S produced by the space geodesy mission GRACE. We measure [Formula: see text], where [Formula: see text] is the Earth's dragging of inertial frames normalized to its general relativity value, 0.002 is the 1-sigma formal error and 0.05 is our preliminary estimate of systematic error mainly due to the uncertainties in the Earth gravity model GGM05S. Our result is in agreement with the prediction of general relativity.
-
Automated body weight prediction of dairy cows using 3-dimensional vision.
Song, X; Bokkers, E A M; van der Tol, P P J; Groot Koerkamp, P W G; van Mourik, S
2018-05-01
The objectives of this study were to quantify the error of body weight prediction using automatically measured morphological traits in a 3-dimensional (3-D) vision system and to assess the influence of various sources of uncertainty on body weight prediction. In this case study, an image acquisition setup was created in a cow selection box equipped with a top-view 3-D camera. Morphological traits of hip height, hip width, and rump length were automatically extracted from the raw 3-D images taken of the rump area of dairy cows (n = 30). These traits combined with days in milk, age, and parity were used in multiple linear regression models to predict body weight. To find the best prediction model, an exhaustive feature selection algorithm was used to build intermediate models (n = 63). Each model was validated by leave-one-out cross-validation, giving the root mean square error and mean absolute percentage error. The model consisting of hip width (measurement variability of 0.006 m), days in milk, and parity was the best model, with the lowest errors of 41.2 kg of root mean square error and 5.2% mean absolute percentage error. Our integrated system, including the image acquisition setup, image analysis, and the best prediction model, predicted the body weights with a performance similar to that achieved using semi-automated or manual methods. Moreover, the variability of our simplified morphological trait measurement showed a negligible contribution to the uncertainty of body weight prediction. We suggest that dairy cow body weight prediction can be improved by incorporating more predictive morphological traits and by improving the prediction model structure. The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).
-
Lindström, Björn R; Mattsson-Mårn, Isak Berglund; Golkar, Armita; Olsson, Andreas
2013-01-01
Cognitive control is needed when mistakes have consequences, especially when such consequences are potentially harmful. However, little is known about how the aversive consequences of deficient control affect behavior. To address this issue, participants performed a two-choice response time task where error commissions were expected to be punished by electric shocks during certain blocks. By manipulating (1) the perceived punishment risk (no, low, high) associated with error commissions, and (2) response conflict (low, high), we showed that motivation to avoid punishment enhanced performance during high response conflict. As a novel index of the processes enabling successful cognitive control under threat, we explored electromyographic activity in the corrugator supercilii (cEMG) muscle of the upper face. The corrugator supercilii is partially controlled by the anterior midcingulate cortex (aMCC) which is sensitive to negative affect, pain and cognitive control. As hypothesized, the cEMG exhibited several key similarities with the core temporal and functional characteristics of the Error-Related Negativity (ERN) ERP component, the hallmark index of cognitive control elicited by performance errors, and which has been linked to the aMCC. The cEMG was amplified within 100 ms of error commissions (the same time-window as the ERN), particularly during the high punishment risk condition where errors would be most aversive. Furthermore, similar to the ERN, the magnitude of error cEMG predicted post-error response time slowing. Our results suggest that cEMG activity can serve as an index of avoidance motivated control, which is instrumental to adaptive cognitive control when consequences are potentially harmful.
-
Lindström, Björn R.; Mattsson-Mårn, Isak Berglund; Golkar, Armita; Olsson, Andreas
2013-01-01
Cognitive control is needed when mistakes have consequences, especially when such consequences are potentially harmful. However, little is known about how the aversive consequences of deficient control affect behavior. To address this issue, participants performed a two-choice response time task where error commissions were expected to be punished by electric shocks during certain blocks. By manipulating (1) the perceived punishment risk (no, low, high) associated with error commissions, and (2) response conflict (low, high), we showed that motivation to avoid punishment enhanced performance during high response conflict. As a novel index of the processes enabling successful cognitive control under threat, we explored electromyographic activity in the corrugator supercilii (cEMG) muscle of the upper face. The corrugator supercilii is partially controlled by the anterior midcingulate cortex (aMCC) which is sensitive to negative affect, pain and cognitive control. As hypothesized, the cEMG exhibited several key similarities with the core temporal and functional characteristics of the Error-Related Negativity (ERN) ERP component, the hallmark index of cognitive control elicited by performance errors, and which has been linked to the aMCC. The cEMG was amplified within 100 ms of error commissions (the same time-window as the ERN), particularly during the high punishment risk condition where errors would be most aversive. Furthermore, similar to the ERN, the magnitude of error cEMG predicted post-error response time slowing. Our results suggest that cEMG activity can serve as an index of avoidance motivated control, which is instrumental to adaptive cognitive control when consequences are potentially harmful. PMID:23840356
-
DOE Office of Scientific and Technical Information (OSTI.GOV)
Weston, Louise Marie
2007-09-01
A recent report on criticality accidents in nuclear facilities indicates that human error played a major role in a significant number of incidents with serious consequences and that some of these human errors may be related to the emotional state of the individual. A pre-shift test to detect a deleterious emotional state could reduce the occurrence of such errors in critical operations. The effectiveness of pre-shift testing is a challenge because of the need to gather predictive data in a relatively short test period and the potential occurrence of learning effects due to a requirement for frequent testing. This reportmore » reviews the different types of reliability and validity methods and testing and statistical analysis procedures to validate measures of emotional state. The ultimate value of a validation study depends upon the percentage of human errors in critical operations that are due to the emotional state of the individual. A review of the literature to identify the most promising predictors of emotional state for this application is highly recommended.« less
-
Disruption of hierarchical predictive coding during sleep
Strauss, Melanie; Sitt, Jacobo D.; King, Jean-Remi; Elbaz, Maxime; Azizi, Leila; Buiatti, Marco; Naccache, Lionel; van Wassenhove, Virginie; Dehaene, Stanislas
2015-01-01
When presented with an auditory sequence, the brain acts as a predictive-coding device that extracts regularities in the transition probabilities between sounds and detects unexpected deviations from these regularities. Does such prediction require conscious vigilance, or does it continue to unfold automatically in the sleeping brain? The mismatch negativity and P300 components of the auditory event-related potential, reflecting two steps of auditory novelty detection, have been inconsistently observed in the various sleep stages. To clarify whether these steps remain during sleep, we recorded simultaneous electroencephalographic and magnetoencephalographic signals during wakefulness and during sleep in normal subjects listening to a hierarchical auditory paradigm including short-term (local) and long-term (global) regularities. The global response, reflected in the P300, vanished during sleep, in line with the hypothesis that it is a correlate of high-level conscious error detection. The local mismatch response remained across all sleep stages (N1, N2, and REM sleep), but with an incomplete structure; compared with wakefulness, a specific peak reflecting prediction error vanished during sleep. Those results indicate that sleep leaves initial auditory processing and passive sensory response adaptation intact, but specifically disrupts both short-term and long-term auditory predictive coding. PMID:25737555
-
Study on the medical meteorological forecast of the number of hypertension inpatient based on SVR
NASA Astrophysics Data System (ADS)
Zhai, Guangyu; Chai, Guorong; Zhang, Haifeng
2017-06-01
The purpose of this study is to build a hypertension prediction model by discussing the meteorological factors for hypertension incidence. The research method is selecting the standard data of relative humidity, air temperature, visibility, wind speed and air pressure of Lanzhou from 2010 to 2012(calculating the maximum, minimum and average value with 5 days as a unit ) as the input variables of Support Vector Regression(SVR) and the standard data of hypertension incidence of the same period as the output dependent variables to obtain the optimal prediction parameters by cross validation algorithm, then by SVR algorithm learning and training, a SVR forecast model for hypertension incidence is built. The result shows that the hypertension prediction model is composed of 15 input independent variables, the training accuracy is 0.005, the final error is 0.0026389. The forecast accuracy based on SVR model is 97.1429%, which is higher than statistical forecast equation and neural network prediction method. It is concluded that SVR model provides a new method for hypertension prediction with its simple calculation, small error as well as higher historical sample fitting and Independent sample forecast capability.
-
1991-07-01
predicted by equation using actual chart response obtained from each calibration gas response. (Concentration of cal. gas,l Calibration error, % span • ppm...Analyzer predicted by cali- Col. gas Chart divisions equation* bration Cylinder conc., error,** Drift,***INo. ppm or % Pretest Posttest Pretest Posttest...2m ~J * Correlation coef. * qgq’jq **Analyzer ca.error, % spn (Cal. gas conc. conc. predicted ) x 1003 cal spanSpan value Acceptable limit x ɚ% of
-
Dopamine reward prediction-error signalling: a two-component response
Schultz, Wolfram
2017-01-01
Environmental stimuli and objects, including rewards, are often processed sequentially in the brain. Recent work suggests that the phasic dopamine reward prediction-error response follows a similar sequential pattern. An initial brief, unselective and highly sensitive increase in activity unspecifically detects a wide range of environmental stimuli, then quickly evolves into the main response component, which reflects subjective reward value and utility. This temporal evolution allows the dopamine reward prediction-error signal to optimally combine speed and accuracy. PMID:26865020
-
NASA Technical Reports Server (NTRS)
Holms, A. G.
1974-01-01
Monte Carlo studies using population models intended to represent response surface applications are reported. Simulated experiments were generated by adding pseudo random normally distributed errors to population values to generate observations. Model equations were fitted to the observations and the decision procedure was used to delete terms. Comparison of values predicted by the reduced models with the true population values enabled the identification of deletion strategies that are approximately optimal for minimizing prediction errors.
-
Cullen, Kathleen E; Brooks, Jessica X
2015-02-01
During self-motion, the vestibular system makes essential contributions to postural stability and self-motion perception. To ensure accurate perception and motor control, it is critical to distinguish between vestibular sensory inputs that are the result of externally applied motion (exafference) and that are the result of our own actions (reafference). Indeed, although the vestibular sensors encode vestibular afference and reafference with equal fidelity, neurons at the first central stage of sensory processing selectively encode vestibular exafference. The mechanism underlying this reafferent suppression compares the brain's motor-based expectation of sensory feedback with the actual sensory consequences of voluntary self-motion, effectively computing the sensory prediction error (i.e., exafference). It is generally thought that sensory prediction errors are computed in the cerebellum, yet it has been challenging to explicitly demonstrate this. We have recently addressed this question and found that deep cerebellar nuclei neurons explicitly encode sensory prediction errors during self-motion. Importantly, in everyday life, sensory prediction errors occur in response to changes in the effector or world (muscle strength, load, etc.), as well as in response to externally applied sensory stimulation. Accordingly, we hypothesize that altering the relationship between motor commands and the actual movement parameters will result in the updating in the cerebellum-based computation of exafference. If our hypothesis is correct, under these conditions, neuronal responses should initially be increased--consistent with a sudden increase in the sensory prediction error. Then, over time, as the internal model is updated, response modulation should decrease in parallel with a reduction in sensory prediction error, until vestibular reafference is again suppressed. The finding that the internal model predicting the sensory consequences of motor commands adapts for new relationships would have important implications for understanding how responses to passive stimulation endure despite the cerebellum's ability to learn new relationships between motor commands and sensory feedback.
-
Wilson, John Thomas
2000-01-01
A mathematical technique of estimating low-flow frequencies from base-flow measurements was evaluated by using data for streams in Indiana. Low-flow frequencies at low- flow partial-record stations were estimated by relating base-flow measurements to concurrent daily flows at nearby streamflow-gaging stations (index stations) for which low-flowfrequency curves had been developed. A network of long-term streamflow-gaging stations in Indiana provided a sample of sites with observed low-flow frequencies. Observed values of 7-day, 10-year low flow and 7-day, 2-year low flow were compared to predicted values to evaluate the accuracy of the method. Five test cases were used to evaluate the method under a variety of conditions in which the location of the index station and its drainage area varied relative to the partial-record station. A total of 141 pairs of streamflow-gaging stations were used in the five test cases. Four of the test cases used one index station, the fifth test case used two index stations. The number of base-flow measurements was varied for each test case to see if the accuracy of the method was affected by the number of measurements used. The most accurate and least variable results were produced when two index stations on the same stream or tributaries of the partial-record station were used. All but one value of the predicted 7-day, 10-year low flow were within 15 percent of the values observed for the long-term continuous record, and all of the predicted values of the 7-day, 2-year lowflow were within 15 percent of the observed values. This apparent accuracy, to some extent, may be a result of the small sample set of 15. Of the four test cases that used one index station, the most accurate and least variable results were produced in the test case where the index station and partial-record station were on the same stream or on streams tributary to each other and where the index station had a larger drainage area than the partial-record station. In that test case, the method tended to over predict, based on the median relative error. In 23 of 28 test pairs, the predicted 7-day, 10-year low flow was within 15 percent of the observed value; in 26 of 28 test pairs, the predicted 7-day, 2-year low flow was within 15 percent of the observed value. When the index station and partial-record station were on the same stream or streams tributary to each other and the index station had a smaller drainage area than the partial-record station, the method tended to under predict the low-flow frequencies. Nineteen of 28 predicted values of the 7-day, 10-year low flow were within 15 percent of the observed values. Twenty-five of 28 predicted values of the 7-day, 2-year low flow were within 15 percent of the observed values. When the index station and the partial-record station were on different streams, the method tended to under predict regardless of whether the index station had a larger or smaller drainage area than that of the partial-record station. Also, the variability of the relative error of estimate was greatest for the test cases that used index stations and partial-record stations from different streams. This variability, in part, may be caused by using more streamflow-gaging stations with small low-flow frequencies in these test cases. A small difference in the predicted and observed values can equate to a large relative error when dealing with stations that have small low-flow frequencies. In the test cases that used one index station, the method tended to predict smaller low-flow frequencies as the number of base-flow measurements was reduced from 20 to 5. Overall, the average relative error of estimate and the variability of the predicted values increased as the number of base-flow measurements was reduced.
-
Altered neural reward and loss processing and prediction error signalling in depression
Ubl, Bettina; Kuehner, Christine; Kirsch, Peter; Ruttorf, Michaela
2015-01-01
Dysfunctional processing of reward and punishment may play an important role in depression. However, functional magnetic resonance imaging (fMRI) studies have shown heterogeneous results for reward processing in fronto-striatal regions. We examined neural responsivity associated with the processing of reward and loss during anticipation and receipt of incentives and related prediction error (PE) signalling in depressed individuals. Thirty medication-free depressed persons and 28 healthy controls performed an fMRI reward paradigm. Regions of interest analyses focused on neural responses during anticipation and receipt of gains and losses and related PE-signals. Additionally, we assessed the relationship between neural responsivity during gain/loss processing and hedonic capacity. When compared with healthy controls, depressed individuals showed reduced fronto-striatal activity during anticipation of gains and losses. The groups did not significantly differ in response to reward and loss outcomes. In depressed individuals, activity increases in the orbitofrontal cortex and nucleus accumbens during reward anticipation were associated with hedonic capacity. Depressed individuals showed an absence of reward-related PEs but encoded loss-related PEs in the ventral striatum. Depression seems to be linked to blunted responsivity in fronto-striatal regions associated with limited motivational responses for rewards and losses. Alterations in PE encoding might mirror blunted reward- and enhanced loss-related associative learning in depression. PMID:25567763
-
Converting international ¼ inch tree volume to Doyle
Aaron Holley; John R. Brooks; Stuart A. Moss
2014-01-01
An equation for converting Mesavage and Girard's International ¼ inch tree volumes to the Doyle log rule is presented as a function of tree diameter. Volume error for trees having less than four logs exhibited volume prediction errors within a range of ±10 board feet. In addition, volume prediction error as a percent of actual Doyle tree volume...
-
Mendez, Michelle A.; Popkin, Barry M.; Buckland, Genevieve; Schroder, Helmut; Amiano, Pilar; Barricarte, Aurelio; Huerta, José-María; Quirós, José R.; Sánchez, María-José; González, Carlos A
2011-01-01
Misreporting characterized by the reporting of implausible energy intakes may undermine the valid estimation of diet-disease relations, but the methods to best identify and account for misreporting are unknown. The present study compared how alternate approaches affected associations between selected dietary factors and body mass index (BMI) by using data from the European Prospective Investigation Into Cancer and Nutrition-Spain. A total of 24,332 women and 15,061 men 29–65 years of age recruited from 1992 to 1996 for whom measured height and weight and validated diet history data were available were included. Misreporters were identified on the basis of disparities between reported energy intakes and estimated requirements calculated using the original Goldberg method and 2 alternatives: one that substituted basal metabolic rate equations that are more valid at higher BMIs and another that used doubly labeled water-predicted total energy expenditure equations. Compared with results obtained using the original method, underreporting was considerably lower and overreporting higher with alternative methods, which were highly concordant. Accounting for misreporters with all methods yielded diet-BMI relations that were more consistent with expectations; alternative methods often strengthened associations. For example, among women, multivariable-adjusted differences in BMI for the highest versus lowest vegetable intake tertile (β = 0.37 (standard error, 0.07)) were neutral after adjusting with the original method (β = 0.01 (standard error, 07)) and negative using the predicted total energy expenditure method with stringent cutoffs (β = −0.15 (standard error, 0.07)). Alternative methods may yield more valid associations between diet and obesity-related outcomes. PMID:21242302
-
Mendez, Michelle A; Popkin, Barry M; Buckland, Genevieve; Schroder, Helmut; Amiano, Pilar; Barricarte, Aurelio; Huerta, José-María; Quirós, José R; Sánchez, María-José; González, Carlos A
2011-02-15
Misreporting characterized by the reporting of implausible energy intakes may undermine the valid estimation of diet-disease relations, but the methods to best identify and account for misreporting are unknown. The present study compared how alternate approaches affected associations between selected dietary factors and body mass index (BMI) by using data from the European Prospective Investigation Into Cancer and Nutrition-Spain. A total of 24,332 women and 15,061 men 29-65 years of age recruited from 1992 to 1996 for whom measured height and weight and validated diet history data were available were included. Misreporters were identified on the basis of disparities between reported energy intakes and estimated requirements calculated using the original Goldberg method and 2 alternatives: one that substituted basal metabolic rate equations that are more valid at higher BMIs and another that used doubly labeled water-predicted total energy expenditure equations. Compared with results obtained using the original method, underreporting was considerably lower and overreporting higher with alternative methods, which were highly concordant. Accounting for misreporters with all methods yielded diet-BMI relations that were more consistent with expectations; alternative methods often strengthened associations. For example, among women, multivariable-adjusted differences in BMI for the highest versus lowest vegetable intake tertile (β = 0.37 (standard error, 0.07)) were neutral after adjusting with the original method (β = 0.01 (standard error, 07)) and negative using the predicted total energy expenditure method with stringent cutoffs (β = -0.15 (standard error, 0.07)). Alternative methods may yield more valid associations between diet and obesity-related outcomes.
-
Long Term Mean Local Time of the Ascending Node Prediction
NASA Technical Reports Server (NTRS)
McKinley, David P.
2007-01-01
Significant error has been observed in the long term prediction of the Mean Local Time of the Ascending Node on the Aqua spacecraft. This error of approximately 90 seconds over a two year prediction is a complication in planning and timing of maneuvers for all members of the Earth Observing System Afternoon Constellation, which use Aqua's MLTAN as the reference for their inclination maneuvers. It was determined that the source of the prediction error was the lack of a solid Earth tide model in the operational force models. The Love Model of the solid Earth tide potential was used to derive analytic corrections to the inclination and right ascension of the ascending node of Aqua's Sun-synchronous orbit. Additionally, it was determined that the resonance between the Sun and orbit plane of the Sun-synchronous orbit is the primary driver of this error. The analytic corrections have been added to the operational force models for the Aqua spacecraft reducing the two-year 90-second error to less than 7 seconds.
-
Optimizing the learning rate for adaptive estimation of neural encoding models
2018-01-01
Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are critically affected by the learning rate, which dictates how fast model parameters are updated based on new observations. Despite the importance of the learning rate, currently an analytical approach for its selection is largely lacking and existing signal processing methods vastly tune it empirically or heuristically. Here, we develop a novel analytical calibration algorithm for optimal selection of the learning rate in adaptive Bayesian filters. We formulate the problem through a fundamental trade-off that learning rate introduces between the steady-state error and the convergence time of the estimated model parameters. We derive explicit functions that predict the effect of learning rate on error and convergence time. Using these functions, our calibration algorithm can keep the steady-state parameter error covariance smaller than a desired upper-bound while minimizing the convergence time, or keep the convergence time faster than a desired value while minimizing the error. We derive the algorithm both for discrete-valued spikes modeled as point processes nonlinearly dependent on the brain state, and for continuous-valued neural recordings modeled as Gaussian processes linearly dependent on the brain state. Using extensive closed-loop simulations, we show that the analytical solution of the calibration algorithm accurately predicts the effect of learning rate on parameter error and convergence time. Moreover, the calibration algorithm allows for fast and accurate learning of the encoding model and for fast convergence of decoding to accurate performance. Finally, larger learning rates result in inaccurate encoding models and decoders, and smaller learning rates delay their convergence. The calibration algorithm provides a novel analytical approach to predictably achieve a desired level of error and convergence time in adaptive learning, with application to closed-loop neurotechnologies and other signal processing domains. PMID:29813069
-
Optimizing the learning rate for adaptive estimation of neural encoding models.
Hsieh, Han-Lin; Shanechi, Maryam M
2018-05-01
Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are critically affected by the learning rate, which dictates how fast model parameters are updated based on new observations. Despite the importance of the learning rate, currently an analytical approach for its selection is largely lacking and existing signal processing methods vastly tune it empirically or heuristically. Here, we develop a novel analytical calibration algorithm for optimal selection of the learning rate in adaptive Bayesian filters. We formulate the problem through a fundamental trade-off that learning rate introduces between the steady-state error and the convergence time of the estimated model parameters. We derive explicit functions that predict the effect of learning rate on error and convergence time. Using these functions, our calibration algorithm can keep the steady-state parameter error covariance smaller than a desired upper-bound while minimizing the convergence time, or keep the convergence time faster than a desired value while minimizing the error. We derive the algorithm both for discrete-valued spikes modeled as point processes nonlinearly dependent on the brain state, and for continuous-valued neural recordings modeled as Gaussian processes linearly dependent on the brain state. Using extensive closed-loop simulations, we show that the analytical solution of the calibration algorithm accurately predicts the effect of learning rate on parameter error and convergence time. Moreover, the calibration algorithm allows for fast and accurate learning of the encoding model and for fast convergence of decoding to accurate performance. Finally, larger learning rates result in inaccurate encoding models and decoders, and smaller learning rates delay their convergence. The calibration algorithm provides a novel analytical approach to predictably achieve a desired level of error and convergence time in adaptive learning, with application to closed-loop neurotechnologies and other signal processing domains.
-
A new model integrating short- and long-term aging of copper added to soils
Zeng, Saiqi; Li, Jumei; Wei, Dongpu
2017-01-01
Aging refers to the processes by which the bioavailability/toxicity, isotopic exchangeability, and extractability of metals added to soils decline overtime. We studied the characteristics of the aging process in copper (Cu) added to soils and the factors that affect this process. Then we developed a semi-mechanistic model to predict the lability of Cu during the aging process with descriptions of the diffusion process using complementary error function. In the previous studies, two semi-mechanistic models to separately predict short-term and long-term aging of Cu added to soils were developed with individual descriptions of the diffusion process. In the short-term model, the diffusion process was linearly related to the square root of incubation time (t1/2), and in the long-term model, the diffusion process was linearly related to the natural logarithm of incubation time (lnt). Both models could predict short-term or long-term aging processes separately, but could not predict the short- and long-term aging processes by one model. By analyzing and combining the two models, we found that the short- and long-term behaviors of the diffusion process could be described adequately using the complementary error function. The effect of temperature on the diffusion process was obtained in this model as well. The model can predict the aging process continuously based on four factors—soil pH, incubation time, soil organic matter content and temperature. PMID:28820888
-
Kormány, Róbert; Fekete, Jenő; Guillarme, Davy; Fekete, Szabolcs
2014-02-01
The goal of this study was to evaluate the accuracy of simulated robustness testing using commercial modelling software (DryLab) and state-of-the-art stationary phases. For this purpose, a mixture of amlodipine and its seven related impurities was analyzed on short narrow bore columns (50×2.1mm, packed with sub-2μm particles) providing short analysis times. The performance of commercial modelling software for robustness testing was systematically compared to experimental measurements and DoE based predictions. We have demonstrated that the reliability of predictions was good, since the predicted retention times and resolutions were in good agreement with the experimental ones at the edges of the design space. In average, the retention time relative errors were <1.0%, while the predicted critical resolution errors were comprised between 6.9 and 17.2%. Because the simulated robustness testing requires significantly less experimental work than the DoE based predictions, we think that robustness could now be investigated in the early stage of method development. Moreover, the column interchangeability, which is also an important part of robustness testing, was investigated considering five different C8 and C18 columns packed with sub-2μm particles. Again, thanks to modelling software, we proved that the separation was feasible on all columns within the same analysis time (less than 4min), by proper adjustments of variables. Copyright © 2013 Elsevier B.V. All rights reserved.
-
Artificial Intelligence Techniques for Predicting and Mapping Daily Pan Evaporation
NASA Astrophysics Data System (ADS)
Arunkumar, R.; Jothiprakash, V.; Sharma, Kirty
2017-09-01
In this study, Artificial Intelligence techniques such as Artificial Neural Network (ANN), Model Tree (MT) and Genetic Programming (GP) are used to develop daily pan evaporation time-series (TS) prediction and cause-effect (CE) mapping models. Ten years of observed daily meteorological data such as maximum temperature, minimum temperature, relative humidity, sunshine hours, dew point temperature and pan evaporation are used for developing the models. For each technique, several models are developed by changing the number of inputs and other model parameters. The performance of each model is evaluated using standard statistical measures such as Mean Square Error, Mean Absolute Error, Normalized Mean Square Error and correlation coefficient (R). The results showed that daily TS-GP (4) model predicted better with a correlation coefficient of 0.959 than other TS models. Among various CE models, CE-ANN (6-10-1) resulted better than MT and GP models with a correlation coefficient of 0.881. Because of the complex non-linear inter-relationship among various meteorological variables, CE mapping models could not achieve the performance of TS models. From this study, it was found that GP performs better for recognizing single pattern (time series modelling), whereas ANN is better for modelling multiple patterns (cause-effect modelling) in the data.
-
Sensitivity study on durability variables of marine concrete structures
NASA Astrophysics Data System (ADS)
Zhou, Xin'gang; Li, Kefei
2013-06-01
In order to study the influence of parameters on durability of marine concrete structures, the parameter's sensitivity analysis was studied in this paper. With the Fick's 2nd law of diffusion and the deterministic sensitivity analysis method (DSA), the sensitivity factors of apparent surface chloride content, apparent chloride diffusion coefficient and its time dependent attenuation factor were analyzed. The results of the analysis show that the impact of design variables on concrete durability was different. The values of sensitivity factor of chloride diffusion coefficient and its time dependent attenuation factor were higher than others. Relative less error in chloride diffusion coefficient and its time dependent attenuation coefficient induces a bigger error in concrete durability design and life prediction. According to probability sensitivity analysis (PSA), the influence of mean value and variance of concrete durability design variables on the durability failure probability was studied. The results of the study provide quantitative measures of the importance of concrete durability design and life prediction variables. It was concluded that the chloride diffusion coefficient and its time dependent attenuation factor have more influence on the reliability of marine concrete structural durability. In durability design and life prediction of marine concrete structures, it was very important to reduce the measure and statistic error of durability design variables.
-
Lin, Lixin; Wang, Yunjia; Teng, Jiyao; Xi, Xiuxiu
2015-07-23
The measurement of soil total nitrogen (TN) by hyperspectral remote sensing provides an important tool for soil restoration programs in areas with subsided land caused by the extraction of natural resources. This study used the local correlation maximization-complementary superiority method (LCMCS) to establish TN prediction models by considering the relationship between spectral reflectance (measured by an ASD FieldSpec 3 spectroradiometer) and TN based on spectral reflectance curves of soil samples collected from subsided land which is determined by synthetic aperture radar interferometry (InSAR) technology. Based on the 1655 selected effective bands of the optimal spectrum (OSP) of the first derivate differential of reciprocal logarithm ([log{1/R}]'), (correlation coefficients, p < 0.01), the optimal model of LCMCS method was obtained to determine the final model, which produced lower prediction errors (root mean square error of validation [RMSEV] = 0.89, mean relative error of validation [MREV] = 5.93%) when compared with models built by the local correlation maximization (LCM), complementary superiority (CS) and partial least squares regression (PLS) methods. The predictive effect of LCMCS model was optional in Cangzhou, Renqiu and Fengfeng District. Results indicate that the LCMCS method has great potential to monitor TN in subsided lands caused by the extraction of natural resources including groundwater, oil and coal.
-
Comparison of Predictive Modeling Methods of Aircraft Landing Speed
NASA Technical Reports Server (NTRS)
Diallo, Ousmane H.
2012-01-01
Expected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports. Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations. There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors. This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway. As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE). Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed. The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport. However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods. Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach. For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation. Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.
-
NASA Technical Reports Server (NTRS)
Buglia, James J.
1989-01-01
An analysis was made of the error in the minimum altitude of a geometric ray from an orbiting spacecraft to the Sun. The sunrise and sunset errors are highly correlated and are opposite in sign. With the ephemeris generated for the SAGE 1 instrument data reduction, these errors can be as large as 200 to 350 meters (1 sigma) after 7 days of orbit propagation. The bulk of this error results from errors in the position of the orbiting spacecraft rather than errors in computing the position of the Sun. These errors, in turn, result from the discontinuities in the ephemeris tapes resulting from the orbital determination process. Data taken from the end of the definitive ephemeris tape are used to generate the predict data for the time interval covered by the next arc of the orbit determination process. The predicted data are then updated by using the tracking data. The growth of these errors is very nearly linear, with a slight nonlinearity caused by the beta angle. An approximate analytic method is given, which predicts the magnitude of the errors and their growth in time with reasonable fidelity.
-
Effects of Parameter Uncertainty on Long-Term Simulations of Lake Alkalinity
NASA Astrophysics Data System (ADS)
Lee, Sijin; Georgakakos, Konstantine P.; Schnoor, Jerald L.
1990-03-01
A first-order second-moment uncertainty analysis has been applied to two lakes in the Adirondack Park, New York, to assess the long-term response of lakes to acid deposition. Uncertainty due to parameter error and initial condition error was considered. Because the enhanced trickle-down (ETD) model is calibrated with only 3 years of field data and is used to simulate a 50-year period, the uncertainty in the lake alkalinity prediction is relatively large. When a best estimate of parameter uncertainty is used, the annual average alkalinity is predicted to be -11 ±28 μeq/L for Lake Woods and 142 ± 139 μeq/L for Lake Panther after 50 years. Hydrologic parameters and chemical weathering rate constants contributed most to the uncertainty of the simulations. Results indicate that the uncertainty in long-range predictions of lake alkalinity increased significantly over a 5- to 10-year period and then reached a steady state.