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Sample records for absolute prediction error

  1. Relative errors can cue absolute visuomotor mappings.

    PubMed

    van Dam, Loes C J; Ernst, Marc O

    2015-12-01

    When repeatedly switching between two visuomotor mappings, e.g. in a reaching or pointing task, adaptation tends to speed up over time. That is, when the error in the feedback corresponds to a mapping switch, fast adaptation occurs. Yet, what is learned, the relative error or the absolute mappings? When switching between mappings, errors with a size corresponding to the relative difference between the mappings will occur more often than other large errors. Thus, we could learn to correct more for errors with this familiar size (Error Learning). On the other hand, it has been shown that the human visuomotor system can store several absolute visuomotor mappings (Mapping Learning) and can use associated contextual cues to retrieve them. Thus, when contextual information is present, no error feedback is needed to switch between mappings. Using a rapid pointing task, we investigated how these two types of learning may each contribute when repeatedly switching between mappings in the absence of task-irrelevant contextual cues. After training, we examined how participants changed their behaviour when a single error probe indicated either the often-experienced error (Error Learning) or one of the previously experienced absolute mappings (Mapping Learning). Results were consistent with Mapping Learning despite the relative nature of the error information in the feedback. This shows that errors in the feedback can have a double role in visuomotor behaviour: they drive the general adaptation process by making corrections possible on subsequent movements, as well as serve as contextual cues that can signal a learned absolute mapping. PMID:26280315

  2. Classification images predict absolute efficiency.

    PubMed

    Murray, Richard F; Bennett, Patrick J; Sekuler, Allison B

    2005-02-24

    How well do classification images characterize human observers' strategies in perceptual tasks? We show mathematically that from the classification image of a noisy linear observer, it is possible to recover the observer's absolute efficiency. If we could similarly predict human observers' performance from their classification images, this would suggest that the linear model that underlies use of the classification image method is adequate over the small range of stimuli typically encountered in a classification image experiment, and that a classification image captures most important aspects of human observers' performance over this range. In a contrast discrimination task and in a shape discrimination task, we found that observers' absolute efficiencies were generally well predicted by their classification images, although consistently slightly (approximately 13%) higher than predicted. We consider whether a number of plausible nonlinearities can account for the slight under prediction, and of these we find that only a form of phase uncertainty can account for the discrepancy.

  3. Dialogues on prediction errors.

    PubMed

    Niv, Yael; Schoenbaum, Geoffrey

    2008-07-01

    The recognition that computational ideas from reinforcement learning are relevant to the study of neural circuits has taken the cognitive neuroscience community by storm. A central tenet of these models is that discrepancies between actual and expected outcomes can be used for learning. Neural correlates of such prediction-error signals have been observed now in midbrain dopaminergic neurons, striatum, amygdala and even prefrontal cortex, and models incorporating prediction errors have been invoked to explain complex phenomena such as the transition from goal-directed to habitual behavior. Yet, like any revolution, the fast-paced progress has left an uneven understanding in its wake. Here, we provide answers to ten simple questions about prediction errors, with the aim of exposing both the strengths and the limitations of this active area of neuroscience research.

  4. Dopamine reward prediction error coding.

    PubMed

    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.

  5. Dopamine reward prediction error coding

    PubMed Central

    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

  6. On the Error Sources in Absolute Individual Antenna Calibrations

    NASA Astrophysics Data System (ADS)

    Aerts, Wim; Baire, Quentin; Bilich, Andria; Bruyninx, Carine; Legrand, Juliette

    2013-04-01

    field) multi path errors, both during calibration and later on at the station, absolute sub-millimeter positioning with GPS is not (yet) possible. References [1] G. Wübbena, M. Schmitz, G. Boettcher, C. Schumann, "Absolute GNSS Antenna Calibration with a Robot: Repeatability of Phase Variations, Calibration of GLONASS and Determination of Carrier-to-Noise Pattern", International GNSS Service: Analysis Center workshop, 8-12 May 2006, Darmstadt, Germany. [2] P. Zeimetz, H. Kuhlmann, "On the Accuracy of Absolute GNSS Antenna Calibration and the Conception of a New Anechoic Chamber", FIG Working Week 2008, 14-19 June 2008, Stockholm, Sweden. [3] P. Zeimetz, H. Kuhlmann, L. Wanninger, V. Frevert, S. Schön and K. Strauch, "Ringversuch 2009", 7th GNSS-Antennen-Workshop, 19-20 March 2009, Dresden, Germany.

  7. Reward positivity: Reward prediction error or salience prediction error?

    PubMed

    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. PMID:27184070

  8. Reward positivity: Reward prediction error or salience prediction error?

    PubMed

    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.

  9. Absolute vs. relative error characterization of electromagnetic tracking accuracy

    NASA Astrophysics Data System (ADS)

    Matinfar, Mohammad; Narayanasamy, Ganesh; Gutierrez, Luis; Chan, Raymond; Jain, Ameet

    2010-02-01

    Electromagnetic (EM) tracking systems are often used for real time navigation of medical tools in an Image Guided Therapy (IGT) system. They are specifically advantageous when the medical device requires tracking within the body of a patient where line of sight constraints prevent the use of conventional optical tracking. EM tracking systems are however very sensitive to electromagnetic field distortions. These distortions, arising from changes in the electromagnetic environment due to the presence of conductive ferromagnetic surgical tools or other medical equipment, limit the accuracy of EM tracking, in some cases potentially rendering tracking data unusable. We present a mapping method for the operating region over which EM tracking sensors are used, allowing for characterization of measurement errors, in turn providing physicians with visual feedback about measurement confidence or reliability of localization estimates. In this instance, we employ a calibration phantom to assess distortion within the operating field of the EM tracker and to display in real time the distribution of measurement errors, as well as the location and extent of the field associated with minimal spatial distortion. The accuracy is assessed relative to successive measurements. Error is computed for a reference point and consecutive measurement errors are displayed relative to the reference in order to characterize the accuracy in near-real-time. In an initial set-up phase, the phantom geometry is calibrated by registering the data from a multitude of EM sensors in a non-ferromagnetic ("clean") EM environment. The registration results in the locations of sensors with respect to each other and defines the geometry of the sensors in the phantom. In a measurement phase, the position and orientation data from all sensors are compared with the known geometry of the sensor spacing, and localization errors (displacement and orientation) are computed. Based on error thresholds provided by the

  10. Absolute Plate Velocities from Seismic Anisotropy: Importance of Correlated Errors

    NASA Astrophysics Data System (ADS)

    Gordon, R. G.; Zheng, L.; Kreemer, C.

    2014-12-01

    The orientation of seismic anisotropy inferred beneath the interiors of plates may provide a means to estimate the motions of the plate relative to the deeper mantle. Here we analyze a global set of shear-wave splitting data to estimate plate motions and to better understand the dispersion of the data, correlations in the errors, and their relation to plate speed. The errors in plate motion azimuths inferred from shear-wave splitting beneath any one tectonic plate are shown to be correlated with the errors of other azimuths from the same plate. To account for these correlations, we adopt a two-tier analysis: First, find the pole of rotation and confidence limits for each plate individually. Second, solve for the best fit to these poles while constraining relative plate angular velocities to consistency with the MORVEL relative plate angular velocities. Our preferred set of angular velocities, SKS-MORVEL, is determined from the poles from eight plates weighted proportionally to the root-mean-square velocity of each plate. SKS-MORVEL indicates that eight plates (Amur, Antarctica, Caribbean, Eurasia, Lwandle, Somalia, Sundaland, and Yangtze) have angular velocities that differ insignificantly from zero. The net rotation of the lithosphere is 0.25±0.11º Ma-1 (95% confidence limits) right-handed about 57.1ºS, 68.6ºE. The within-plate dispersion of seismic anisotropy for oceanic lithosphere (σ=19.2°) differs insignificantly from that for continental lithosphere (σ=21.6°). The between-plate dispersion, however, is significantly smaller for oceanic lithosphere (σ=7.4°) than for continental lithosphere (σ=14.7°). Two of the slowest-moving plates, Antarctica (vRMS=4 mm a-1, σ=29°) and Eurasia (vRMS=3 mm a-1, σ=33°), have two of the largest within-plate dispersions, which may indicate that a plate must move faster than ≈5 mm a-1 to result in seismic anisotropy useful for estimating plate motion.

  11. Comparing Absolute Error with Squared Error for Evaluating Empirical Models of Continuous Variables: Compositions, Implications, and Consequences

    NASA Astrophysics Data System (ADS)

    Gao, J.

    2014-12-01

    Reducing modeling error is often a major concern of empirical geophysical models. However, modeling errors can be defined in different ways: When the response variable is continuous, the most commonly used metrics are squared (SQ) and absolute (ABS) errors. For most applications, ABS error is the more natural, but SQ error is mathematically more tractable, so is often used as a substitute with little scientific justification. Existing literature has not thoroughly investigated the implications of using SQ error in place of ABS error, especially not geospatially. This study compares the two metrics through the lens of bias-variance decomposition (BVD). BVD breaks down the expected modeling error of each model evaluation point into bias (systematic error), variance (model sensitivity), and noise (observation instability). It offers a way to probe the composition of various error metrics. I analytically derived the BVD of ABS error and compared it with the well-known SQ error BVD, and found that not only the two metrics measure the characteristics of the probability distributions of modeling errors differently, but also the effects of these characteristics on the overall expected error are different. Most notably, under SQ error all bias, variance, and noise increase expected error, while under ABS error certain parts of the error components reduce expected error. Since manipulating these subtractive terms is a legitimate way to reduce expected modeling error, SQ error can never capture the complete story embedded in ABS error. I then empirically compared the two metrics with a supervised remote sensing model for mapping surface imperviousness. Pair-wise spatially-explicit comparison for each error component showed that SQ error overstates all error components in comparison to ABS error, especially variance-related terms. Hence, substituting ABS error with SQ error makes model performance appear worse than it actually is, and the analyst would more likely accept a

  12. Absolute plate velocities from seismic anisotropy: Importance of correlated errors

    NASA Astrophysics Data System (ADS)

    Zheng, Lin; Gordon, Richard G.; Kreemer, Corné

    2014-09-01

    The errors in plate motion azimuths inferred from shear wave splitting beneath any one tectonic plate are shown to be correlated with the errors of other azimuths from the same plate. To account for these correlations, we adopt a two-tier analysis: First, find the pole of rotation and confidence limits for each plate individually. Second, solve for the best fit to these poles while constraining relative plate angular velocities to consistency with the MORVEL relative plate angular velocities. Our preferred set of angular velocities, SKS-MORVEL, is determined from the poles from eight plates weighted proportionally to the root-mean-square velocity of each plate. SKS-MORVEL indicates that eight plates (Amur, Antarctica, Caribbean, Eurasia, Lwandle, Somalia, Sundaland, and Yangtze) have angular velocities that differ insignificantly from zero. The net rotation of the lithosphere is 0.25 ± 0.11° Ma-1 (95% confidence limits) right handed about 57.1°S, 68.6°E. The within-plate dispersion of seismic anisotropy for oceanic lithosphere (σ = 19.2°) differs insignificantly from that for continental lithosphere (σ = 21.6°). The between-plate dispersion, however, is significantly smaller for oceanic lithosphere (σ = 7.4°) than for continental lithosphere (σ = 14.7°). Two of the slowest-moving plates, Antarctica (vRMS = 4 mm a-1, σ = 29°) and Eurasia (vRMS = 3 mm a-1, σ = 33°), have two of the largest within-plate dispersions, which may indicate that a plate must move faster than ≈ 5 mm a-1 to result in seismic anisotropy useful for estimating plate motion. The tendency of observed azimuths on the Arabia plate to be counterclockwise of plate motion may provide information about the direction and amplitude of superposed asthenospheric flow or about anisotropy in the lithospheric mantle.

  13. Students' Mathematical Work on Absolute Value: Focusing on Conceptions, Errors and Obstacles

    ERIC Educational Resources Information Center

    Elia, Iliada; Özel, Serkan; Gagatsis, Athanasios; Panaoura, Areti; Özel, Zeynep Ebrar Yetkiner

    2016-01-01

    This study investigates students' conceptions of absolute value (AV), their performance in various items on AV, their errors in these items and the relationships between students' conceptions and their performance and errors. The Mathematical Working Space (MWS) is used as a framework for studying students' mathematical work on AV and the…

  14. Interactions of timing and prediction error learning.

    PubMed

    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.

  15. Interactions of timing and prediction error learning.

    PubMed

    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. PMID:23962670

  16. Assessing Suturing Skills in a Self-Guided Learning Setting: Absolute Symmetry Error

    ERIC Educational Resources Information Center

    Brydges, Ryan; Carnahan, Heather; Dubrowski, Adam

    2009-01-01

    Directed self-guidance, whereby trainees independently practice a skill-set in a structured setting, may be an effective technique for novice training. Currently, however, most evaluation methods require an expert to be present during practice. The study aim was to determine if absolute symmetry error, a clinically important measure that can be…

  17. IMPROVEMENT OF SMVGEAR II ON VECTOR AND SCALAR MACHINES THROUGH ABSOLUTE ERROR TOLERANCE CONTROL (R823186)

    EPA Science Inventory

    The computer speed of SMVGEAR II was improved markedly on scalar and vector machines with relatively little loss in accuracy. The improvement was due to a method of frequently recalculating the absolute error tolerance instead of keeping it constant for a given set of chemistry. ...

  18. Relative and Absolute Error Control in a Finite-Difference Method Solution of Poisson's Equation

    ERIC Educational Resources Information Center

    Prentice, J. S. C.

    2012-01-01

    An algorithm for error control (absolute and relative) in the five-point finite-difference method applied to Poisson's equation is described. The algorithm is based on discretization of the domain of the problem by means of three rectilinear grids, each of different resolution. We discuss some hardware limitations associated with the algorithm,…

  19. Demonstrating the Error Budget for the Climate Absolute Radiance and Refractivity Observatory Through Solar Irradiance Measurements

    NASA Technical Reports Server (NTRS)

    Thome, Kurtis; McCorkel, Joel; McAndrew, Brendan

    2016-01-01

    The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission addresses the need to observe highaccuracy, long-term climate change trends and to use decadal change observations as a method to determine the accuracy of climate change. A CLARREO objective is to improve the accuracy of SI-traceable, absolute calibration at infrared and reflected solar wavelengths to reach on-orbit accuracies required to allow climate change observations to survive data gaps and observe climate change at the limit of natural variability. Such an effort will also demonstrate National Institute of Standards and Technology (NIST) approaches for use in future spaceborne instruments. The current work describes the results of laboratory and field measurements with the Solar, Lunar for Absolute Reflectance Imaging Spectroradiometer (SOLARIS) which is the calibration demonstration system (CDS) for the reflected solar portion of CLARREO. SOLARIS allows testing and evaluation of calibration approaches, alternate design and/or implementation approaches and components for the CLARREO mission. SOLARIS also provides a test-bed for detector technologies, non-linearity determination and uncertainties, and application of future technology developments and suggested spacecraft instrument design modifications. Results of laboratory calibration measurements are provided to demonstrate key assumptions about instrument behavior that are needed to achieve CLARREO's climate measurement requirements. Absolute radiometric response is determined using laser-based calibration sources and applied to direct solar views for comparison with accepted solar irradiance models to demonstrate accuracy values giving confidence in the error budget for the CLARREO reflectance retrieval.

  20. Demonstrating the error budget for the Climate Absolute Radiance and Refractivity Observatory through solar irradiance measurements

    NASA Astrophysics Data System (ADS)

    Thome, Kurtis; McCorkel, Joel; McAndrew, Brendan

    2015-09-01

    The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission addresses the need to observe highaccuracy, long-term climate change trends and to use decadal change observations as a method to determine the accuracy of climate change. A CLARREO objective is to improve the accuracy of SI-traceable, absolute calibration at infrared and reflected solar wavelengths to reach on-orbit accuracies required to allow climate change observations to survive data gaps and observe climate change at the limit of natural variability. Such an effort will also demonstrate National Institute of Standards and Technology (NIST) approaches for use in future spaceborne instruments. The current work describes the results of laboratory and field measurements with the Solar, Lunar for Absolute Reflectance Imaging Spectroradiometer (SOLARIS) which is the calibration demonstration system (CDS) for the reflected solar portion of CLARREO. SOLARIS allows testing and evaluation of calibration approaches, alternate design and/or implementation approaches and components for the CLARREO mission. SOLARIS also provides a testbed for detector technologies, non-linearity determination and uncertainties, and application of future technology developments and suggested spacecraft instrument design modifications. Results of laboratory calibration measurements are provided to demonstrate key assumptions about instrument behavior that are needed to achieve CLARREO's climate measurement requirements. Absolute radiometric response is determined using laser-based calibration sources and applied to direct solar views for comparison with accepted solar irradiance models to demonstrate accuracy values giving confidence in the error budget for the CLARREO reflectance retrieval.

  1. Absolute analytical prediction of photonic crystal guided mode resonance wavelengths

    SciTech Connect

    Hermannsson, Pétur Gordon; Vannahme, Christoph; Smith, Cameron L. C.; Kristensen, Anders

    2014-08-18

    A class of photonic crystal resonant reflectors known as guided mode resonant filters are optical structures that are widely used in the field of refractive index sensing, particularly in biosensing. For the purposes of understanding and design, their behavior has traditionally been modeled numerically with methods such as rigorous coupled wave analysis. Here it is demonstrated how the absolute resonance wavelengths of such structures can be predicted by analytically modeling them as slab waveguides in which the propagation constant is determined by a phase matching condition. The model is experimentally verified to be capable of predicting the absolute resonance wavelengths to an accuracy of within 0.75 nm, as well as resonance wavelength shifts due to changes in cladding index within an accuracy of 0.45 nm across the visible wavelength regime in the case where material dispersion is taken into account. Furthermore, it is demonstrated that the model is valid beyond the limit of low grating modulation, for periodically discontinuous waveguide layers, high refractive index contrasts, and highly dispersive media.

  2. The feedback-related negativity signals salience prediction errors, not reward prediction errors.

    PubMed

    Talmi, Deborah; Atkinson, Ryan; El-Deredy, Wael

    2013-05-01

    Modulations of the feedback-related negativity (FRN) event-related potential (ERP) have been suggested as a potential biomarker in psychopathology. A dominant theory about this signal contends that it reflects the operation of the neural system underlying reinforcement learning in humans. The theory suggests that this frontocentral negative deflection in the ERP 230-270 ms after the delivery of a probabilistic reward expresses a prediction error signal derived from midbrain dopaminergic projections to the anterior cingulate cortex. We tested this theory by investigating whether FRN will also be observed for an inherently aversive outcome: physical pain. In another session, the outcome was monetary reward instead of pain. As predicted, unexpected reward omissions (a negative reward prediction error) yielded a more negative deflection relative to unexpected reward delivery. Surprisingly, unexpected pain omission (a positive reward prediction error) also yielded a negative deflection relative to unexpected pain delivery. Our data challenge the theory by showing that the FRN expresses aversive prediction errors with the same sign as reward prediction errors. Both FRNs were spatiotemporally and functionally equivalent. We suggest that FRN expresses salience prediction errors rather than reward prediction errors. PMID:23658166

  3. The feedback-related negativity signals salience prediction errors, not reward prediction errors.

    PubMed

    Talmi, Deborah; Atkinson, Ryan; El-Deredy, Wael

    2013-05-01

    Modulations of the feedback-related negativity (FRN) event-related potential (ERP) have been suggested as a potential biomarker in psychopathology. A dominant theory about this signal contends that it reflects the operation of the neural system underlying reinforcement learning in humans. The theory suggests that this frontocentral negative deflection in the ERP 230-270 ms after the delivery of a probabilistic reward expresses a prediction error signal derived from midbrain dopaminergic projections to the anterior cingulate cortex. We tested this theory by investigating whether FRN will also be observed for an inherently aversive outcome: physical pain. In another session, the outcome was monetary reward instead of pain. As predicted, unexpected reward omissions (a negative reward prediction error) yielded a more negative deflection relative to unexpected reward delivery. Surprisingly, unexpected pain omission (a positive reward prediction error) also yielded a negative deflection relative to unexpected pain delivery. Our data challenge the theory by showing that the FRN expresses aversive prediction errors with the same sign as reward prediction errors. Both FRNs were spatiotemporally and functionally equivalent. We suggest that FRN expresses salience prediction errors rather than reward prediction errors.

  4. Linear unbiased prediction of clock errors.

    PubMed

    Shmaliy, Yuriy S

    2009-09-01

    In this paper, we propose a new formula for linear unbiased prediction of the local clock timescales. To predict future errors over all the measurement data, a new gain is derived for the p-step ramp unbiased finite impulse response (FIR) predictor. We then show that this gain gives the best linear unbiased fit suitable for forming the prediction vector. The predictor proposed is consistent with linear regression and best linear unbiased estimator. Applications are given for a crystal clock and the USNO Master Clock.

  5. Generalized approach for using unbiased symmetric metrics with negative values: normalized mean bias factor and normalized mean absolute error factor

    EPA Science Inventory

    Unbiased symmetric metrics provide a useful measure to quickly compare two datasets, with similar interpretations for both under and overestimations. Two examples include the normalized mean bias factor and normalized mean absolute error factor. However, the original formulations...

  6. Predicted errors in children's early sentence comprehension.

    PubMed

    Gertner, Yael; Fisher, Cynthia

    2012-07-01

    Children use syntax to interpret sentences and learn verbs; this is syntactic bootstrapping. The structure-mapping account of early syntactic bootstrapping proposes that a partial representation of sentence structure, the set of nouns occurring with the verb, guides initial interpretation and provides an abstract format for new learning. This account predicts early successes, but also telltale errors: Toddlers should be unable to tell transitive sentences from other sentences containing two nouns. In testing this prediction, we capitalized on evidence that 21-month-olds use what they have learned about noun order in English sentences to understand new transitive verbs. In two experiments, 21-month-olds applied this noun-order knowledge to two-noun intransitive sentences, mistakenly assigning different interpretations to "The boy and the girl are gorping!" and "The girl and the boy are gorping!". This suggests that toddlers exploit partial representations of sentence structure to guide sentence interpretation; these sparse representations are useful, but error-prone.

  7. Perceptual Inference: A Matter of Predictions and Errors.

    PubMed

    Kok, Peter

    2016-09-12

    A recent study finds that separate populations of neurons in inferotemporal cortex code for perceptual predictions and prediction errors, supporting predictive coding theories of perception. PMID:27623264

  8. 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

  9. Effective connectivity associated with auditory error detection in musicians with absolute pitch

    PubMed Central

    Parkinson, Amy L.; Behroozmand, Roozbeh; Ibrahim, Nadine; Korzyukov, Oleg; Larson, Charles R.; Robin, Donald A.

    2014-01-01

    It is advantageous to study a wide range of vocal abilities in order to fully understand how vocal control measures vary across the full spectrum. Individuals with absolute pitch (AP) are able to assign a verbal label to musical notes and have enhanced abilities in pitch identification without reliance on an external referent. In this study we used dynamic causal modeling (DCM) to model effective connectivity of ERP responses to pitch perturbation in voice auditory feedback in musicians with relative pitch (RP), AP, and non-musician controls. We identified a network compromising left and right hemisphere superior temporal gyrus (STG), primary motor cortex (M1), and premotor cortex (PM). We specified nine models and compared two main factors examining various combinations of STG involvement in feedback pitch error detection/correction process. Our results suggest that modulation of left to right STG connections are important in the identification of self-voice error and sensory motor integration in AP musicians. We also identify reduced connectivity of left hemisphere PM to STG connections in AP and RP groups during the error detection and corrections process relative to non-musicians. We suggest that this suppression may allow for enhanced connectivity relating to pitch identification in the right hemisphere in those with more precise pitch matching abilities. Musicians with enhanced pitch identification abilities likely have an improved auditory error detection and correction system involving connectivity of STG regions. Our findings here also suggest that individuals with AP are more adept at using feedback related to pitch from the right hemisphere. PMID:24634644

  10. Predicted Errors In Children's Early Sentence Comprehension

    PubMed Central

    Gertner, Yael; Fisher, Cynthia

    2012-01-01

    Children use syntax to interpret sentences and learn verbs; this is syntactic bootstrapping. The structure-mapping account of early syntactic bootstrapping proposes that a partial representation of sentence structure, the set of nouns occurring with the verb, guides initial interpretation and provides an abstract format for new learning. This account predicts early successes, but also telltale errors: Toddlers should be unable to tell transitive sentences from other sentences containing two nouns. In testing this prediction, we capitalized on evidence that 21-month-olds use what they have learned about noun order in English sentences to understand new transitive verbs. In two experiments, 21-month-olds applied this noun-order knowledge to two-noun intransitive sentences, mistakenly assigning different interpretations to “The boy and the girl are gorping!” and “The girl and the boy are gorping!”. This suggests that toddlers exploit partial representations of sentence structure to guide sentence interpretation; these sparse representations are useful, but error-prone. PMID:22525312

  11. Axiomatic methods, dopamine and reward prediction error.

    PubMed

    Caplin, Andrew; Dean, Mark

    2008-04-01

    The phasic firing rate of midbrain dopamine neurons has been shown to respond both to the receipt of rewarding stimuli, and the degree to which such stimuli are anticipated by the recipient. This has led to the hypothesis that these neurons encode reward prediction error (RPE)-the difference between how rewarding an event is, and how rewarding it was expected to be. However, the RPE model is one of a number of competing explanations for dopamine activity that have proved hard to disentangle, mainly because they are couched in terms of latent, or unobservable, variables. This article describes techniques for dealing with latent variables common in economics and decision theory, and reviews work that uses these techniques to provide simple, non-parametric tests of the RPE hypothesis, allowing clear differentiation between competing explanations.

  12. Preliminary Error Budget for the Reflected Solar Instrument for the Climate Absolute Radiance and Refractivity Observatory

    NASA Technical Reports Server (NTRS)

    Thome, Kurtis; Gubbels, Timothy; Barnes, Robert

    2011-01-01

    The Climate Absolute Radiance and Refractivity Observatory (CLARREO) plans to observe climate change trends over decadal time scales to determine the accuracy of climate projections. The project relies on spaceborne earth observations of SI-traceable variables sensitive to key decadal change parameters. The mission includes a reflected solar instrument retrieving at-sensor reflectance over the 320 to 2300 nm spectral range with 500-m spatial resolution and 100-km swath. Reflectance is obtained from the ratio of measurements of the earth s surface to those while viewing the sun relying on a calibration approach that retrieves reflectance with uncertainties less than 0.3%. The calibration is predicated on heritage hardware, reduction of sensor complexity, adherence to detector-based calibration standards, and an ability to simulate in the laboratory on-orbit sources in both size and brightness to provide the basis of a transfer to orbit of the laboratory calibration including a link to absolute solar irradiance measurements. The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission addresses the need to observe high-accuracy, long-term climate change trends and to use decadal change observations as the most critical method to determine the accuracy of climate change projections such as those in the IPCC Report. A rigorously known accuracy of both decadal change observations as well as climate projections is critical in order to enable sound policy decisions. The CLARREO Project will implement a spaceborne earth observation mission designed to provide rigorous SI traceable observations (i.e., radiance, reflectance, and refractivity) that are sensitive to a wide range of key decadal change variables, including: 1) Surface temperature and atmospheric temperature profile 2) Atmospheric water vapor profile 3) Far infrared water vapor greenhouse 4) Aerosol properties and anthropogenic aerosol direct radiative forcing 5) Total and spectral solar

  13. Error Budget for a Calibration Demonstration System for the Reflected Solar Instrument for the Climate Absolute Radiance and Refractivity Observatory

    NASA Technical Reports Server (NTRS)

    Thome, Kurtis; McCorkel, Joel; McAndrew, Brendan

    2013-01-01

    A goal of the Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission is to observe highaccuracy, long-term climate change trends over decadal time scales. The key to such a goal is to improving the accuracy of SI traceable absolute calibration across infrared and reflected solar wavelengths allowing climate change to be separated from the limit of natural variability. The advances required to reach on-orbit absolute accuracy to allow climate change observations to survive data gaps exist at NIST in the laboratory, but still need demonstration that the advances can move successfully from to NASA and/or instrument vendor capabilities for spaceborne instruments. The current work describes the radiometric calibration error budget for the Solar, Lunar for Absolute Reflectance Imaging Spectroradiometer (SOLARIS) which is the calibration demonstration system (CDS) for the reflected solar portion of CLARREO. The goal of the CDS is to allow the testing and evaluation of calibration approaches, alternate design and/or implementation approaches and components for the CLARREO mission. SOLARIS also provides a test-bed for detector technologies, non-linearity determination and uncertainties, and application of future technology developments and suggested spacecraft instrument design modifications. The resulting SI-traceable error budget for reflectance retrieval using solar irradiance as a reference and methods for laboratory-based, absolute calibration suitable for climatequality data collections is given. Key components in the error budget are geometry differences between the solar and earth views, knowledge of attenuator behavior when viewing the sun, and sensor behavior such as detector linearity and noise behavior. Methods for demonstrating this error budget are also presented.

  14. Prediction with measurement errors in finite populations

    PubMed Central

    Singer, Julio M; Stanek, Edward J; Lencina, Viviana B; González, Luz Mery; Li, Wenjun; Martino, Silvina San

    2011-01-01

    We address the problem of selecting the best linear unbiased predictor (BLUP) of the latent value (e.g., serum glucose fasting level) of sample subjects with heteroskedastic measurement errors. Using a simple example, we compare the usual mixed model BLUP to a similar predictor based on a mixed model framed in a finite population (FPMM) setup with two sources of variability, the first of which corresponds to simple random sampling and the second, to heteroskedastic measurement errors. Under this last approach, we show that when measurement errors are subject-specific, the BLUP shrinkage constants are based on a pooled measurement error variance as opposed to the individual ones generally considered for the usual mixed model BLUP. In contrast, when the heteroskedastic measurement errors are measurement condition-specific, the FPMM BLUP involves different shrinkage constants. We also show that in this setup, when measurement errors are subject-specific, the usual mixed model predictor is biased but has a smaller mean squared error than the FPMM BLUP which point to some difficulties in the interpretation of such predictors. PMID:22162621

  15. The impact of covariate measurement error on risk prediction.

    PubMed

    Khudyakov, Polyna; Gorfine, Malka; Zucker, David; Spiegelman, Donna

    2015-07-10

    In the development of risk prediction models, predictors are often measured with error. In this paper, we investigate the impact of covariate measurement error on risk prediction. We compare the prediction performance using a costly variable measured without error, along with error-free covariates, to that of a model based on an inexpensive surrogate along with the error-free covariates. We consider continuous error-prone covariates with homoscedastic and heteroscedastic errors, and also a discrete misclassified covariate. Prediction performance is evaluated by the area under the receiver operating characteristic curve (AUC), the Brier score (BS), and the ratio of the observed to the expected number of events (calibration). In an extensive numerical study, we show that (i) the prediction model with the error-prone covariate is very well calibrated, even when it is mis-specified; (ii) using the error-prone covariate instead of the true covariate can reduce the AUC and increase the BS dramatically; (iii) adding an auxiliary variable, which is correlated with the error-prone covariate but conditionally independent of the outcome given all covariates in the true model, can improve the AUC and BS substantially. We conclude that reducing measurement error in covariates will improve the ensuing risk prediction, unless the association between the error-free and error-prone covariates is very high. Finally, we demonstrate how a validation study can be used to assess the effect of mismeasured covariates on risk prediction. These concepts are illustrated in a breast cancer risk prediction model developed in the Nurses' Health Study. PMID:25865315

  16. Critical evidence for the prediction error theory in associative learning.

    PubMed

    Terao, Kanta; Matsumoto, Yukihisa; Mizunami, Makoto

    2015-01-01

    In associative learning in mammals, it is widely accepted that the discrepancy, or error, between actual and predicted reward determines whether learning occurs. Complete evidence for the prediction error theory, however, has not been obtained in any learning systems: Prediction error theory stems from the finding of a blocking phenomenon, but blocking can also be accounted for by other theories, such as the attentional theory. We demonstrated blocking in classical conditioning in crickets and obtained evidence to reject the attentional theory. To obtain further evidence supporting the prediction error theory and rejecting alternative theories, we constructed a neural model to match the prediction error theory, by modifying our previous model of learning in crickets, and we tested a prediction from the model: the model predicts that pharmacological intervention of octopaminergic transmission during appetitive conditioning impairs learning but not formation of reward prediction itself, and it thus predicts no learning in subsequent training. We observed such an "auto-blocking", which could be accounted for by the prediction error theory but not by other competitive theories to account for blocking. This study unambiguously demonstrates validity of the prediction error theory in associative learning. PMID:25754125

  17. Critical evidence for the prediction error theory in associative learning

    PubMed Central

    Terao, Kanta; Matsumoto, Yukihisa; Mizunami, Makoto

    2015-01-01

    In associative learning in mammals, it is widely accepted that the discrepancy, or error, between actual and predicted reward determines whether learning occurs. Complete evidence for the prediction error theory, however, has not been obtained in any learning systems: Prediction error theory stems from the finding of a blocking phenomenon, but blocking can also be accounted for by other theories, such as the attentional theory. We demonstrated blocking in classical conditioning in crickets and obtained evidence to reject the attentional theory. To obtain further evidence supporting the prediction error theory and rejecting alternative theories, we constructed a neural model to match the prediction error theory, by modifying our previous model of learning in crickets, and we tested a prediction from the model: the model predicts that pharmacological intervention of octopaminergic transmission during appetitive conditioning impairs learning but not formation of reward prediction itself, and it thus predicts no learning in subsequent training. We observed such an “auto-blocking”, which could be accounted for by the prediction error theory but not by other competitive theories to account for blocking. This study unambiguously demonstrates validity of the prediction error theory in associative learning. PMID:25754125

  18. Critical evidence for the prediction error theory in associative learning.

    PubMed

    Terao, Kanta; Matsumoto, Yukihisa; Mizunami, Makoto

    2015-03-10

    In associative learning in mammals, it is widely accepted that the discrepancy, or error, between actual and predicted reward determines whether learning occurs. Complete evidence for the prediction error theory, however, has not been obtained in any learning systems: Prediction error theory stems from the finding of a blocking phenomenon, but blocking can also be accounted for by other theories, such as the attentional theory. We demonstrated blocking in classical conditioning in crickets and obtained evidence to reject the attentional theory. To obtain further evidence supporting the prediction error theory and rejecting alternative theories, we constructed a neural model to match the prediction error theory, by modifying our previous model of learning in crickets, and we tested a prediction from the model: the model predicts that pharmacological intervention of octopaminergic transmission during appetitive conditioning impairs learning but not formation of reward prediction itself, and it thus predicts no learning in subsequent training. We observed such an "auto-blocking", which could be accounted for by the prediction error theory but not by other competitive theories to account for blocking. This study unambiguously demonstrates validity of the prediction error theory in associative learning.

  19. Dopamine neurons share common response function for reward prediction error

    PubMed Central

    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

  20. Dopamine neurons share common response function for reward prediction error.

    PubMed

    Eshel, Neir; Tian, Ju; Bukwich, Michael; Uchida, Naoshige

    2016-03-01

    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 marked 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 were able to 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

  1. Dopamine neurons share common response function for reward prediction error.

    PubMed

    Eshel, Neir; Tian, Ju; Bukwich, Michael; Uchida, Naoshige

    2016-03-01

    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 marked 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 were able to 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.

  2. Comprehensive fluence model for absolute portal dose image prediction

    SciTech Connect

    Chytyk, K.; McCurdy, B. M. C.

    2009-04-15

    Amorphous silicon (a-Si) electronic portal imaging devices (EPIDs) continue to be investigated as treatment verification tools, with a particular focus on intensity modulated radiation therapy (IMRT). This verification could be accomplished through a comparison of measured portal images to predicted portal dose images. A general fluence determination tailored to portal dose image prediction would be a great asset in order to model the complex modulation of IMRT. A proposed physics-based parameter fluence model was commissioned by matching predicted EPID images to corresponding measured EPID images of multileaf collimator (MLC) defined fields. The two-source fluence model was composed of a focal Gaussian and an extrafocal Gaussian-like source. Specific aspects of the MLC and secondary collimators were also modeled (e.g., jaw and MLC transmission factors, MLC rounded leaf tips, tongue and groove effect, interleaf leakage, and leaf offsets). Several unique aspects of the model were developed based on the results of detailed Monte Carlo simulations of the linear accelerator including (1) use of a non-Gaussian extrafocal fluence source function, (2) separate energy spectra used for focal and extrafocal fluence, and (3) different off-axis energy spectra softening used for focal and extrafocal fluences. The predicted energy fluence was then convolved with Monte Carlo generated, EPID-specific dose kernels to convert incident fluence to dose delivered to the EPID. Measured EPID data were obtained with an a-Si EPID for various MLC-defined fields (from 1x1 to 20x20 cm{sup 2}) over a range of source-to-detector distances. These measured profiles were used to determine the fluence model parameters in a process analogous to the commissioning of a treatment planning system. The resulting model was tested on 20 clinical IMRT plans, including ten prostate and ten oropharyngeal cases. The model predicted the open-field profiles within 2%, 2 mm, while a mean of 96.6% of pixels over

  3. Predictive error analysis for a water resource management model

    NASA Astrophysics Data System (ADS)

    Gallagher, Mark; Doherty, John

    2007-02-01

    SummaryIn calibrating a model, a set of parameters is assigned to the model which will be employed for the making of all future predictions. If these parameters are estimated through solution of an inverse problem, formulated to be properly posed through either pre-calibration or mathematical regularisation, then solution of this inverse problem will, of necessity, lead to a simplified parameter set that omits the details of reality, while still fitting historical data acceptably well. Furthermore, estimates of parameters so obtained will be contaminated by measurement noise. Both of these phenomena will lead to errors in predictions made by the model, with the potential for error increasing with the hydraulic property detail on which the prediction depends. Integrity of model usage demands that model predictions be accompanied by some estimate of the possible errors associated with them. The present paper applies theory developed in a previous work to the analysis of predictive error associated with a real world, water resource management model. The analysis offers many challenges, including the fact that the model is a complex one that was partly calibrated by hand. Nevertheless, it is typical of models which are commonly employed as the basis for the making of important decisions, and for which such an analysis must be made. The potential errors associated with point-based and averaged water level and creek inflow predictions are examined, together with the dependence of these errors on the amount of averaging involved. Error variances associated with predictions made by the existing model are compared with "optimized error variances" that could have been obtained had calibration been undertaken in such a way as to minimize predictive error variance. The contributions by different parameter types to the overall error variance of selected predictions are also examined.

  4. Predicting Error Bars for QSAR Models

    SciTech Connect

    Schroeter, Timon; Mika, Sebastian; Ter Laak, Antonius; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Mueller, Klaus-Robert

    2007-09-18

    Unfavorable physicochemical properties often cause drug failures. It is therefore important to take lipophilicity and water solubility into account early on in lead discovery. This study presents log D{sub 7} models built using Gaussian Process regression, Support Vector Machines, decision trees and ridge regression algorithms based on 14556 drug discovery compounds of Bayer Schering Pharma. A blind test was conducted using 7013 new measurements from the last months. We also present independent evaluations using public data. Apart from accuracy, we discuss the quality of error bars that can be computed by Gaussian Process models, and ensemble and distance based techniques for the other modelling approaches.

  5. Predicting Error Bars for QSAR Models

    NASA Astrophysics Data System (ADS)

    Schroeter, Timon; Schwaighofer, Anton; Mika, Sebastian; Ter Laak, Antonius; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Müller, Klaus-Robert

    2007-09-01

    Unfavorable physicochemical properties often cause drug failures. It is therefore important to take lipophilicity and water solubility into account early on in lead discovery. This study presents log D7 models built using Gaussian Process regression, Support Vector Machines, decision trees and ridge regression algorithms based on 14556 drug discovery compounds of Bayer Schering Pharma. A blind test was conducted using 7013 new measurements from the last months. We also present independent evaluations using public data. Apart from accuracy, we discuss the quality of error bars that can be computed by Gaussian Process models, and ensemble and distance based techniques for the other modelling approaches.

  6. Learned predictions of error likelihood in the anterior cingulate cortex.

    PubMed

    Brown, Joshua W; Braver, Todd S

    2005-02-18

    The anterior cingulate cortex (ACC) and the related medial wall play a critical role in recruiting cognitive control. Although ACC exhibits selective error and conflict responses, it has been unclear how these develop and become context-specific. With use of a modified stop-signal task, we show from integrated computational neural modeling and neuroimaging studies that ACC learns to predict error likelihood in a given context, even for trials in which there is no error or response conflict. These results support a more general error-likelihood theory of ACC function based on reinforcement learning, of which conflict and error detection are special cases.

  7. Scaling prediction errors to reward variability benefits error-driven learning in humans

    PubMed Central

    Schultz, Wolfram

    2015-01-01

    Effective error-driven learning requires individuals to adapt learning to environmental reward variability. The adaptive mechanism may involve decays in learning rate across subsequent trials, as shown previously, and rescaling of reward prediction errors. The present study investigated the influence of prediction error scaling and, in particular, the consequences for learning performance. Participants explicitly predicted reward magnitudes that were drawn from different probability distributions with specific standard deviations. By fitting the data with reinforcement learning models, we found scaling of prediction errors, in addition to the learning rate decay shown previously. Importantly, the prediction error scaling was closely related to learning performance, defined as accuracy in predicting the mean of reward distributions, across individual participants. In addition, participants who scaled prediction errors relative to standard deviation also presented with more similar performance for different standard deviations, indicating that increases in standard deviation did not substantially decrease “adapters'” accuracy in predicting the means of reward distributions. However, exaggerated scaling beyond the standard deviation resulted in impaired performance. Thus efficient adaptation makes learning more robust to changing variability. PMID:26180123

  8. Effect of correlated observation error on parameters, predictions, and uncertainty

    USGS Publications Warehouse

    Tiedeman, Claire R.; 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.

  9. A causal link between prediction errors, dopamine neurons and learning.

    PubMed

    Steinberg, Elizabeth E; Keiflin, Ronald; Boivin, Josiah R; Witten, Ilana B; Deisseroth, Karl; Janak, Patricia H

    2013-07-01

    Situations in which rewards are unexpectedly obtained or withheld represent opportunities for new learning. Often, this learning includes identifying cues that predict reward availability. Unexpected rewards strongly activate midbrain dopamine neurons. This phasic signal is proposed to support learning about antecedent cues by signaling discrepancies between actual and expected outcomes, termed a reward prediction error. However, it is unknown whether dopamine neuron prediction error signaling and cue-reward learning are causally linked. To test this hypothesis, we manipulated dopamine neuron activity in rats in two behavioral procedures, associative blocking and extinction, that illustrate the essential function of prediction errors in learning. We observed that optogenetic activation of dopamine neurons concurrent with reward delivery, mimicking a prediction error, was sufficient to cause long-lasting increases in cue-elicited reward-seeking behavior. Our findings establish a causal role for temporally precise dopamine neuron signaling in cue-reward learning, bridging a critical gap between experimental evidence and influential theoretical frameworks.

  10. When is an error not a prediction error? An electrophysiological investigation.

    PubMed

    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.

  11. Encoding of Sensory Prediction Errors in the Human Cerebellum

    PubMed Central

    Schlerf, John; Ivry, Richard B.; Diedrichsen, Jörn

    2015-01-01

    A central tenet of motor neuroscience is that the cerebellum learns from sensory prediction errors. Surprisingly, neuroimaging studies have not revealed definitive signatures of error processing in the cerebellum. Furthermore, neurophysiologic studies suggest an asymmetry, such that the cerebellum may encode errors arising from unexpected sensory events, but not errors reflecting the omission of expected stimuli. We conducted an imaging study to compare the cerebellar response to these two types of errors. Participants made fast out-and-back reaching movements, aiming either for an object that delivered a force pulse if intersected or for a gap between two objects, either of which delivered a force pulse if intersected. Errors (missing the target) could therefore be signaled either through the presence or absence of a force pulse. In an initial analysis, the cerebellar BOLD response was smaller on trials with errors compared with trials without errors. However, we also observed an error-related decrease in heart rate. After correcting for variation in heart rate, increased activation during error trials was observed in the hand area of lobules V and VI. This effect was similar for the two error types. The results provide evidence for the encoding of errors resulting from either the unexpected presence or unexpected absence of sensory stimulation in the human cerebellum. PMID:22492047

  12. Generalized approach for using unbiased symmetric metrics with negative values: normalized mean bias factor and normalized mean absolute error factor

    SciTech Connect

    Gustafson, William I.; Yu, Shaocai

    2012-10-23

    Unbiased symmetric metrics provide a useful measure to quickly compare two datasets, with similar interpretations for both under and overestimations. Two examples include the normalized mean bias factor and normalized mean absolute error factor. However, the original formulations of these metrics are only valid for datasets with positive means. This paper presents a methodology to use and interpret the metrics with datasets that have negative means. The updated formulations give identical results compared to the original formulations for the case of positive means, so researchers are encouraged to use the updated formulations going forward without introducing ambiguity.

  13. Multiscale error analysis, correction, and predictive uncertainty estimation in a flood forecasting system

    NASA Astrophysics Data System (ADS)

    Bogner, K.; Pappenberger, F.

    2011-07-01

    River discharge predictions often show errors that degrade the quality of forecasts. Three different methods of error correction are compared, namely, an autoregressive model with and without exogenous input (ARX and AR, respectively), and a method based on wavelet transforms. For the wavelet method, a Vector-Autoregressive model with exogenous input (VARX) is simultaneously fitted for the different levels of wavelet decomposition; after predicting the next time steps for each scale, a reconstruction formula is applied to transform the predictions in the wavelet domain back to the original time domain. The error correction methods are combined with the Hydrological Uncertainty Processor (HUP) in order to estimate the predictive conditional distribution. For three stations along the Danube catchment, and using output from the European Flood Alert System (EFAS), we demonstrate that the method based on wavelets outperforms simpler methods and uncorrected predictions with respect to mean absolute error, Nash-Sutcliffe efficiency coefficient (and its decomposed performance criteria), informativeness score, and in particular forecast reliability. The wavelet approach efficiently accounts for forecast errors with scale properties of unknown source and statistical structure.

  14. Identifying state-dependent model error in numerical weather prediction

    NASA Astrophysics Data System (ADS)

    Moskaitis, J.; Hansen, J.; Toth, Z.; Zhu, Y.

    2003-04-01

    Model forecasts of complex systems such as the atmosphere lose predictive skill because of two different sources of error: initial conditions error and model error. While much study has been done to determine the nature and consequences of initial conditions error in operational forecast models, relatively little has been done to identify the source of model error and to quantify the effects of model error on forecasts. Here, we attempt to "disentangle" model error from initial conditions error by applying a diagnostic tool in a simple model framework to identify poor forecasts for which model error is likely responsible. The diagnostic is based on the premise that for a perfect ensemble forecast, verification should fall outside the range of ensemble forecast states only a small percentage of the time, according to the size of the ensemble. Identifying these outlier verifications and comparing the statistics of their occurrence to those of a perfect ensemble can tell us about the role of model error in a quantitative, state-dependent manner. The same diagnostic is applied to operational NWP models to quantify the role of model error in poor forecasts (see companion paper by Toth et al.). From these results, we can infer the atmospheric processes the model cannot adequately simulate.

  15. Generalized error-dependent prediction uncertainty in multivariate calibration.

    PubMed

    Allegrini, Franco; Wentzell, Peter D; Olivieri, Alejandro C

    2016-01-15

    Most of the current expressions used to calculate figures of merit in multivariate calibration have been derived assuming independent and identically distributed (iid) measurement errors. However, it is well known that this condition is not always valid for real data sets, where the existence of many external factors can lead to correlated and/or heteroscedastic noise structures. In this report, the influence of the deviations from the classical iid paradigm is analyzed in the context of error propagation theory. New expressions have been derived to calculate sample dependent prediction standard errors under different scenarios. These expressions allow for a quantitative study of the influence of the different sources of instrumental error affecting the system under analysis. Significant differences are observed when the prediction error is estimated in each of the studied scenarios using the most popular first-order multivariate algorithms, under both simulated and experimental conditions.

  16. Characterizing Complex Time Series from the Scaling of Prediction Error.

    NASA Astrophysics Data System (ADS)

    Hinrichs, Brant Eric

    This thesis concerns characterizing complex time series from the scaling of prediction error. We use the global modeling technique of radial basis function approximation to build models from a state-space reconstruction of a time series that otherwise appears complicated or random (i.e. aperiodic, irregular). Prediction error as a function of prediction horizon is obtained from the model using the direct method. The relationship between the underlying dynamics of the time series and the logarithmic scaling of prediction error as a function of prediction horizon is investigated. We use this relationship to characterize the dynamics of both a model chaotic system and physical data from the optic tectum of an attentive pigeon exhibiting the important phenomena of nonstationary neuronal oscillations in response to visual stimuli.

  17. Reward prediction error computation in the pedunculopontine tegmental nucleus neurons.

    PubMed

    Kobayashi, Yasushi; Okada, Ken-Ichi

    2007-05-01

    In this article, we address the role of neuronal activity in the pathways of the brainstem-midbrain circuit in reward and the basis for believing that this circuit provides advantages over previous reinforcement learning theory. Several lines of evidence support the reward-based learning theory proposing that midbrain dopamine (DA) neurons send a teaching signal (the reward prediction error signal) to control synaptic plasticity of the projection area. However, the underlying mechanism of where and how the reward prediction error signal is computed still remains unclear. Since the pedunculopontine tegmental nucleus (PPTN) in the brainstem is one of the strongest excitatory input sources to DA neurons, we hypothesized that the PPTN may play an important role in activating DA neurons and reinforcement learning by relaying necessary signals for reward prediction error computation to DA neurons. To investigate the involvement of the PPTN neurons in computation of reward prediction error, we used a visually guided saccade task (VGST) during recording of neuronal activity in monkeys. Here, we predict that PPTN neurons may relay the excitatory component of tonic reward prediction and phasic primary reward signals, and derive a new computational theory of the reward prediction error in DA neurons.

  18. Differential neural mechanisms for early and late prediction error detection.

    PubMed

    Malekshahi, Rahim; Seth, Anil; Papanikolaou, Amalia; Mathews, Zenon; Birbaumer, Niels; Verschure, Paul F M J; Caria, Andrea

    2016-01-01

    Emerging evidence indicates that prediction, instantiated at different perceptual levels, facilitate visual processing and enable prompt and appropriate reactions. Until now, the mechanisms underlying the effect of predictive coding at different stages of visual processing have still remained unclear. Here, we aimed to investigate early and late processing of spatial prediction violation by performing combined recordings of saccadic eye movements and fast event-related fMRI during a continuous visual detection task. Psychophysical reverse correlation analysis revealed that the degree of mismatch between current perceptual input and prior expectations is mainly processed at late rather than early stage, which is instead responsible for fast but general prediction error detection. Furthermore, our results suggest that conscious late detection of deviant stimuli is elicited by the assessment of prediction error's extent more than by prediction error per se. Functional MRI and functional connectivity data analyses indicated that higher-level brain systems interactions modulate conscious detection of prediction error through top-down processes for the analysis of its representational content, and possibly regulate subsequent adaptation of predictive models. Overall, our experimental paradigm allowed to dissect explicit from implicit behavioral and neural responses to deviant stimuli in terms of their reliance on predictive models. PMID:27079423

  19. The Pupillary Orienting Response Predicts Adaptive Behavioral Adjustment after Errors

    PubMed Central

    Murphy, Peter R.; van Moort, Marianne L.; Nieuwenhuis, Sander

    2016-01-01

    Reaction time (RT) is commonly observed to slow down after an error. This post-error slowing (PES) has been thought to arise from the strategic adoption of a more cautious response mode following deployment of cognitive control. Recently, an alternative account has suggested that PES results from interference due to an error-evoked orienting response. We investigated whether error-related orienting may in fact be a pre-cursor to adaptive post-error behavioral adjustment when the orienting response resolves before subsequent trial onset. We measured pupil dilation, a prototypical measure of autonomic orienting, during performance of a choice RT task with long inter-stimulus intervals, and found that the trial-by-trial magnitude of the error-evoked pupil response positively predicted both PES magnitude and the likelihood that the following response would be correct. These combined findings suggest that the magnitude of the error-related orienting response predicts an adaptive change of response strategy following errors, and thereby promote a reconciliation of the orienting and adaptive control accounts of PES. PMID:27010472

  20. Prediction of absolute infrared intensities for the fundamental vibrations of H2O2

    NASA Technical Reports Server (NTRS)

    Rogers, J. D.; Hillman, J. J.

    1981-01-01

    Absolute infrared intensities are predicted for the vibrational bands of gas-phase H2O2 by the use of a hydrogen atomic polar tensor transferred from the hydroxyl hydrogen atom of CH3OH. These predicted intensities are compared with intensities predicted by the use of a hydrogen atomic polar tensor transferred from H2O. The predicted relative intensities agree well with published spectra of gas-phase H2O2, and the predicted absolute intensities are expected to be accurate to within at least a factor of two. Among the vibrational degrees of freedom, the antisymmetric O-H bending mode nu(6) is found to be the strongest with a calculated intensity of 60.5 km/mole. The torsional band, a consequence of hindered rotation, is found to be the most intense fundamental with a predicted intensity of 120 km/mole. These results are compared with the recent absolute intensity determinations for the nu(6) band.

  1. Differential neural mechanisms for early and late prediction error detection

    PubMed Central

    Malekshahi, Rahim; Seth, Anil; Papanikolaou, Amalia; Mathews, Zenon; Birbaumer, Niels; Verschure, Paul F. M. J.; Caria, Andrea

    2016-01-01

    Emerging evidence indicates that prediction, instantiated at different perceptual levels, facilitate visual processing and enable prompt and appropriate reactions. Until now, the mechanisms underlying the effect of predictive coding at different stages of visual processing have still remained unclear. Here, we aimed to investigate early and late processing of spatial prediction violation by performing combined recordings of saccadic eye movements and fast event-related fMRI during a continuous visual detection task. Psychophysical reverse correlation analysis revealed that the degree of mismatch between current perceptual input and prior expectations is mainly processed at late rather than early stage, which is instead responsible for fast but general prediction error detection. Furthermore, our results suggest that conscious late detection of deviant stimuli is elicited by the assessment of prediction error’s extent more than by prediction error per se. Functional MRI and functional connectivity data analyses indicated that higher-level brain systems interactions modulate conscious detection of prediction error through top-down processes for the analysis of its representational content, and possibly regulate subsequent adaptation of predictive models. Overall, our experimental paradigm allowed to dissect explicit from implicit behavioral and neural responses to deviant stimuli in terms of their reliance on predictive models. PMID:27079423

  2. Arithmetic and local circuitry underlying dopamine prediction errors

    PubMed Central

    Eshel, Neir; Bukwich, Michael; Rao, Vinod; Hemmelder, Vivian; Tian, Ju; Uchida, Naoshige

    2015-01-01

    Dopamine neurons are thought to facilitate learning by comparing actual and expected reward1,2. Despite two decades of investigation, little is known about how this comparison is made. To determine how dopamine neurons calculate prediction error, we combined optogenetic manipulations with extracellular recordings in the ventral tegmental area (VTA) while mice engaged in classical conditioning. By manipulating the temporal expectation of reward, we demonstrate that dopamine neurons perform subtraction, a computation that is ideal for reinforcement learning but rarely observed in the brain. Furthermore, selectively exciting and inhibiting neighbouring GABA neurons in the VTA reveals that these neurons are a source of subtraction: they inhibit dopamine neurons when reward is expected, causally contributing to prediction error calculations. Finally, bilaterally stimulating VTA GABA neurons dramatically reduces anticipatory licking to conditioned odours, consistent with an important role for these neurons in reinforcement learning. Together, our results uncover the arithmetic and local circuitry underlying dopamine prediction errors. PMID:26322583

  3. Arithmetic and local circuitry underlying dopamine prediction errors.

    PubMed

    Eshel, Neir; Bukwich, Michael; Rao, Vinod; Hemmelder, Vivian; Tian, Ju; Uchida, Naoshige

    2015-09-10

    Dopamine neurons are thought to facilitate learning by comparing actual and expected reward. Despite two decades of investigation, little is known about how this comparison is made. To determine how dopamine neurons calculate prediction error, we combined optogenetic manipulations with extracellular recordings in the ventral tegmental area while mice engaged in classical conditioning. Here we demonstrate, by manipulating the temporal expectation of reward, that dopamine neurons perform subtraction, a computation that is ideal for reinforcement learning but rarely observed in the brain. Furthermore, selectively exciting and inhibiting neighbouring GABA (γ-aminobutyric acid) neurons in the ventral tegmental area reveals that these neurons are a source of subtraction: they inhibit dopamine neurons when reward is expected, causally contributing to prediction-error calculations. Finally, bilaterally stimulating ventral tegmental area GABA neurons dramatically reduces anticipatory licking to conditioned odours, consistent with an important role for these neurons in reinforcement learning. Together, our results uncover the arithmetic and local circuitry underlying dopamine prediction errors.

  4. Neural correlates of error prediction in a complex motor task

    PubMed Central

    Maurer, Lisa Katharina; Maurer, Heiko; Müller, Hermann

    2015-01-01

    The goal of the study was to quantify error prediction processes via neural correlates in the Electroencephalogram (EEG). Access to such a neural signal will allow to gain insights into functional and temporal aspects of error perception in the course of learning. We focused on the error negativity (Ne) or error-related negativity (ERN) as a candidate index for the prediction processes. We have used a virtual goal-oriented throwing task where participants used a lever to throw a virtual ball displayed on a computer monitor with the goal of hitting a virtual target as often as possible. After one day of practice with 400 trials, participants performed another 400 trials on a second day with EEG measurement. After error trials (i.e., when the ball missed the target), we found a sharp negative deflection in the EEG peaking 250 ms after ball release (mean amplitude: t = −2.5, df = 20, p = 0.02) and another broader negative deflection following the first, reaching from about 300 ms after release until unambiguous visual knowledge of results (KR; hitting or passing by the target; mean amplitude: t = −7.5, df = 20, p < 0.001). According to shape and timing of the two deflections, we assume that the first deflection represents a predictive Ne/ERN (prediction based on efferent commands and proprioceptive feedback) while the second deflection might have arisen from action monitoring. PMID:26300754

  5. Error analysis in predictive modelling demonstrated on mould data.

    PubMed

    Baranyi, József; Csernus, Olívia; Beczner, Judit

    2014-01-17

    The purpose of this paper was to develop a predictive model for the effect of temperature and water activity on the growth rate of Aspergillus niger and to determine the sources of the error when the model is used for prediction. Parallel mould growth curves, derived from the same spore batch, were generated and fitted to determine their growth rate. The variances of replicate ln(growth-rate) estimates were used to quantify the experimental variability, inherent to the method of determining the growth rate. The environmental variability was quantified by the variance of the respective means of replicates. The idea is analogous to the "within group" and "between groups" variability concepts of ANOVA procedures. A (secondary) model, with temperature and water activity as explanatory variables, was fitted to the natural logarithm of the growth rates determined by the primary model. The model error and the experimental and environmental errors were ranked according to their contribution to the total error of prediction. Our method can readily be applied to analysing the error structure of predictive models of bacterial growth models, too.

  6. Error prediction for probes guided by means of fixtures

    NASA Astrophysics Data System (ADS)

    Fitzpatrick, J. Michael

    2012-02-01

    Probe guides are surgical fixtures that are rigidly attached to bone anchors in order to place a probe at a target with high accuracy (RMS error < 1 mm). Applications include needle biopsy, the placement of electrodes for deep-brain stimulation (DBS), spine surgery, and cochlear implant surgery. Targeting is based on pre-operative images, but targeting errors can arise from three sources: (1) anchor localization error, (2) guide fabrication error, and (3) external forces and torques. A well-established theory exists for the statistical prediction of target registration error (TRE) when targeting is accomplished by means of tracked probes, but no such TRE theory is available for fixtured probe guides. This paper provides that theory and shows that all three error sources can be accommodated in a remarkably simple extension of existing theory. Both the guide and the bone with attached anchors are modeled as objects with rigid sections and elastic sections, the latter of which are described by stiffness matrices. By relating minimization of elastic energy for guide attachment to minimization of fiducial registration error for point registration, it is shown that the expression for targeting error for the guide is identical to that for weighted rigid point registration if the weighting matrices are properly derived from stiffness matrices and the covariance matrices for fiducial localization are augmented with offsets in the anchor positions. An example of the application of the theory is provided for ear surgery.

  7. Principal components analysis of reward prediction errors in a reinforcement learning task.

    PubMed

    Sambrook, Thomas D; Goslin, Jeremy

    2016-01-01

    Models of reinforcement learning represent reward and punishment in terms of reward prediction errors (RPEs), quantitative signed terms describing the degree to which outcomes are better than expected (positive RPEs) or worse (negative RPEs). An electrophysiological component known as feedback related negativity (FRN) occurs at frontocentral sites 240-340ms after feedback on whether a reward or punishment is obtained, and has been claimed to neurally encode an RPE. An outstanding question however, is whether the FRN is sensitive to the size of both positive RPEs and negative RPEs. Previous attempts to answer this question have examined the simple effects of RPE size for positive RPEs and negative RPEs separately. However, this methodology can be compromised by overlap from components coding for unsigned prediction error size, or "salience", which are sensitive to the absolute size of a prediction error but not its valence. In our study, positive and negative RPEs were parametrically modulated using both reward likelihood and magnitude, with principal components analysis used to separate out overlying components. This revealed a single RPE encoding component responsive to the size of positive RPEs, peaking at ~330ms, and occupying the delta frequency band. Other components responsive to unsigned prediction error size were shown, but no component sensitive to negative RPE size was found.

  8. IR signature prediction errors for skin-heated aerial targets

    NASA Astrophysics Data System (ADS)

    McGlynn, John D.; Auerbach, Steven P.

    1997-06-01

    The infrared signature of an aircraft is generally calculated as the sum of multiple components. These components are, typically: the aerodynamic skin heating, reflected solar and upwelling and downwelling radiation, engine hot parts, and exhaust gas emissions. For most airframes, the latter two components overwhelmingly dominate the IR signature. However, for small targets--such as small fighters and cruise missiles, particularly targets with masked hot parts, emissivity control, and suppressed plumes- -aerodynamic heating is the dominant term. This term is determined by the speed of the target, the sea-level air temperature, and the adiabatic lapse rate of the atmosphere, as a function of altitude. Simulations which use AFGL atmospheric codes (LOWTRAN and MODTRAN)--such as SPIRITS--to predict skin heating, may have an intrinsic error in the predicted skin heating component, due to the fixed number of discrete sea-level air temperatures implicit in the atmospheric models. Whenever the assumed background temperature deviates from the implicit model atmosphere sea- level temperature, there will be a measurable error. This error becomes significant in magnitude when trying to model the signatures of small, dim targets dominated by skin heating. This study quantifies the predicted signature errors and suggests simulation implementations which can minimize these errors.

  9. Evaluating Random Forests for Survival Analysis using Prediction Error Curves.

    PubMed

    Mogensen, Ulla B; Ishwaran, Hemant; Gerds, Thomas A

    2012-09-01

    Prediction error curves are increasingly used to assess and compare predictions in survival analysis. This article surveys the R package pec which provides a set of functions for efficient computation of prediction error curves. The software implements inverse probability of censoring weights to deal with right censored data and several variants of cross-validation to deal with the apparent error problem. In principle, all kinds of prediction models can be assessed, and the package readily supports most traditional regression modeling strategies, like Cox regression or additive hazard regression, as well as state of the art machine learning methods such as random forests, a nonparametric method which provides promising alternatives to traditional strategies in low and high-dimensional settings. We show how the functionality of pec can be extended to yet unsupported prediction models. As an example, we implement support for random forest prediction models based on the R-packages randomSurvivalForest and party. Using data of the Copenhagen Stroke Study we use pec to compare random forests to a Cox regression model derived from stepwise variable selection. Reproducible results on the user level are given for publicly available data from the German breast cancer study group.

  10. Recovery of absolute phases for the fringe patterns of three selected wavelengths with improved anti-error capability

    NASA Astrophysics Data System (ADS)

    Long, Jiale; Xi, Jiangtao; Zhang, Jianmin; Zhu, Ming; Cheng, Wenqing; Li, Zhongwei; Shi, Yusheng

    2016-09-01

    In a recent published work, we proposed a technique to recover the absolute phase maps of fringe patterns with two selected fringe wavelengths. To achieve higher anti-error capability, the proposed method requires employing the fringe patterns with longer wavelengths; however, longer wavelength may lead to the degradation of the signal-to-noise ratio (SNR) in the surface measurement. In this paper, we propose a new approach to unwrap the phase maps from their wrapped versions based on the use of fringes with three different wavelengths which is characterized by improved anti-error capability and SNR. Therefore, while the previous method works on the two-phase maps obtained from six-step phase-shifting profilometry (PSP) (thus 12 fringe patterns are needed), the proposed technique performs very well on three-phase maps from three steps PSP, requiring only nine fringe patterns and hence more efficient. Moreover, the advantages of the two-wavelength method in simple implementation and flexibility in the use of fringe patterns are also reserved. Theoretical analysis and experiment results are presented to confirm the effectiveness of the proposed method.

  11. Predicting AIDS-related events using CD4 percentage or CD4 absolute counts

    PubMed Central

    Pirzada, Yasmin; Khuder, Sadik; Donabedian, Haig

    2006-01-01

    Background The extent of immunosuppression and the probability of developing an AIDS-related complication in HIV-infected people is usually measured by the absolute number of CD4 positive T-cells. The percentage of CD4 positive cells is a more easily measured and less variable number. We analyzed sequential CD4 and CD8 numbers, percentages and ratios in 218 of our HIV infected patients to determine the most reliable predictor of an AIDS-related event. Results The CD4 percentage was an unsurpassed predictor of the occurrence of AIDS-related events when all subsets of patients are considered. The CD4 absolute count was the next most reliable, followed by the ratio of CD4/CD8 percentages. The value of CD4 percentage over the CD4 absolute count was seen even after the introduction of highly effective HIV therapy. Conclusion The CD4 percentage is unsurpassed as a parameter for predicting the onset of HIV-related diseases. The extra time and expense of measuring the CD4 absolute count may be unnecessary. PMID:16916461

  12. Error Estimation of An Ensemble Statistical Seasonal Precipitation Prediction Model

    NASA Technical Reports Server (NTRS)

    Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Gui-Long

    2001-01-01

    This NASA Technical Memorandum describes an optimal ensemble canonical correlation forecasting model for seasonal precipitation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. Since new CCA scheme is derived for continuous fields of predictor and predictand, an area-factor is automatically included. Thus our model is an improvement of the spectral CCA scheme of Barnett and Preisendorfer. The improvements include (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States (US) precipitation field. The predictor is the sea surface temperature (SST). The US Climate Prediction Center's reconstructed SST is used as the predictor's historical data. The US National Center for Environmental Prediction's optimally interpolated precipitation (1951-2000) is used as the predictand's historical data. Our forecast experiments show that the new ensemble canonical correlation scheme renders a reasonable forecasting skill. For example, when using September-October-November SST to predict the next season December-January-February precipitation, the spatial pattern correlation between the observed and predicted are positive in 46 years among the 50 years of experiments. The positive correlations are close to or greater than 0.4 in 29 years, which indicates excellent performance of the forecasting model. The forecasting skill can be further enhanced when several predictors are used.

  13. Dopamine neurons encode errors in predicting movement trigger occurrence

    PubMed Central

    Pasquereau, Benjamin

    2014-01-01

    The capacity to anticipate the timing of events in a dynamic environment allows us to optimize the processes necessary for perceiving, attending to, and responding to them. Such anticipation requires neuronal mechanisms that track the passage of time and use this representation, combined with prior experience, to estimate the likelihood that an event will occur (i.e., the event's “hazard rate”). Although hazard-like ramps in activity have been observed in several cortical areas in preparation for movement, it remains unclear how such time-dependent probabilities are estimated to optimize response performance. We studied the spiking activity of dopamine neurons in the substantia nigra pars compacta of monkeys during an arm-reaching task for which the foreperiod preceding the “go” signal varied randomly along a uniform distribution. After extended training, the monkeys' reaction times correlated inversely with foreperiod duration, reflecting a progressive anticipation of the go signal according to its hazard rate. Many dopamine neurons modulated their firing rates as predicted by a succession of hazard-related prediction errors. First, as time passed during the foreperiod, slowly decreasing anticipatory activity tracked the elapsed time as if encoding negative prediction errors. Then, when the go signal appeared, a phasic response encoded the temporal unpredictability of the event, consistent with a positive prediction error. Neither the anticipatory nor the phasic signals were affected by the anticipated magnitudes of future reward or effort, or by parameters of the subsequent movement. These results are consistent with the notion that dopamine neurons encode hazard-related prediction errors independently of other information. PMID:25411459

  14. Multiscale Reactive Molecular Dynamics for Absolute pK a Predictions and Amino Acid Deprotonation.

    PubMed

    Nelson, J Gard; Peng, Yuxing; Silverstein, Daniel W; Swanson, Jessica M J

    2014-07-01

    Accurately calculating a weak acid's pK a from simulations remains a challenging task. We report a multiscale theoretical approach to calculate the free energy profile for acid ionization, resulting in accurate absolute pK a values in addition to insights into the underlying mechanism. Importantly, our approach minimizes empiricism by mapping electronic structure data (QM/MM forces) into a reactive molecular dynamics model capable of extensive sampling. Consequently, the bulk property of interest (the absolute pK a) is the natural consequence of the model, not a parameter used to fit it. This approach is applied to create reactive models of aspartic and glutamic acids. We show that these models predict the correct pK a values and provide ample statistics to probe the molecular mechanism of dissociation. This analysis shows changes in the solvation structure and Zundel-dominated transitions between the protonated acid, contact ion pair, and bulk solvated excess proton. PMID:25061442

  15. Prediction Accuracy of Error Rates for MPTB Space Experiment

    NASA Technical Reports Server (NTRS)

    Buchner, S. P.; Campbell, A. B.; Davis, D.; McMorrow, D.; Petersen, E. L.; Stassinopoulos, E. G.; Ritter, J. C.

    1998-01-01

    This paper addresses the accuracy of radiation-induced upset-rate predictions in space using the results of ground-based measurements together with standard environmental and device models. The study is focused on two part types - 16 Mb NEC DRAM's (UPD4216) and 1 Kb SRAM's (AMD93L422) - both of which are currently in space on board the Microelectronics and Photonics Test Bed (MPTB). To date, ground-based measurements of proton-induced single event upset (SEM cross sections as a function of energy have been obtained and combined with models of the proton environment to predict proton-induced error rates in space. The role played by uncertainties in the environmental models will be determined by comparing the modeled radiation environment with the actual environment measured aboard MPTB. Heavy-ion induced upsets have also been obtained from MPTB and will be compared with the "predicted" error rate following ground testing that will be done in the near future. These results should help identify sources of uncertainty in predictions of SEU rates in space.

  16. The Representation of Prediction Error in Auditory Cortex

    PubMed Central

    Rubin, Jonathan; Ulanovsky, Nachum; Tishby, Naftali

    2016-01-01

    To survive, organisms must extract information from the past that is relevant for their future. How this process is expressed at the neural level remains unclear. We address this problem by developing a novel approach from first principles. We show here how to generate low-complexity representations of the past that produce optimal predictions of future events. We then illustrate this framework by studying the coding of ‘oddball’ sequences in auditory cortex. We find that for many neurons in primary auditory cortex, trial-by-trial fluctuations of neuronal responses correlate with the theoretical prediction error calculated from the short-term past of the stimulation sequence, under constraints on the complexity of the representation of this past sequence. In some neurons, the effect of prediction error accounted for more than 50% of response variability. Reliable predictions often depended on a representation of the sequence of the last ten or more stimuli, although the representation kept only few details of that sequence. PMID:27490251

  17. No Absolutism Here: Harm Predicts Moral Judgment 30× Better Than Disgust-Commentary on Scott, Inbar, & Rozin (2016).

    PubMed

    Gray, Kurt; Schein, Chelsea

    2016-05-01

    Moral absolutism is the idea that people's moral judgments are insensitive to considerations of harm. Scott, Inbar, and Rozin (2016, this issue) claim that most moral opponents to genetically modified organisms are absolutely opposed-motivated by disgust and not harm. Yet there is no evidence for moral absolutism in their data. Perceived risk/harm is the most significant predictor of moral judgments for "absolutists," accounting for 30 times more variance than disgust. Reanalyses suggest that disgust is not even a significant predictor of the moral judgments of absolutists once accounting for perceived harm and anger. Instead of revealing actual moral absolutism, Scott et al. find only empty absolutism: hypothetical, forecasted, self-reported moral absolutism. Strikingly, the moral judgments of so-called absolutists are somewhat more sensitive to consequentialist concerns than those of nonabsolutists. Mediation reanalyses reveal that moral judgments are most proximally predicted by harm and not disgust, consistent with dyadic morality.

  18. No Absolutism Here: Harm Predicts Moral Judgment 30× Better Than Disgust-Commentary on Scott, Inbar, & Rozin (2016).

    PubMed

    Gray, Kurt; Schein, Chelsea

    2016-05-01

    Moral absolutism is the idea that people's moral judgments are insensitive to considerations of harm. Scott, Inbar, and Rozin (2016, this issue) claim that most moral opponents to genetically modified organisms are absolutely opposed-motivated by disgust and not harm. Yet there is no evidence for moral absolutism in their data. Perceived risk/harm is the most significant predictor of moral judgments for "absolutists," accounting for 30 times more variance than disgust. Reanalyses suggest that disgust is not even a significant predictor of the moral judgments of absolutists once accounting for perceived harm and anger. Instead of revealing actual moral absolutism, Scott et al. find only empty absolutism: hypothetical, forecasted, self-reported moral absolutism. Strikingly, the moral judgments of so-called absolutists are somewhat more sensitive to consequentialist concerns than those of nonabsolutists. Mediation reanalyses reveal that moral judgments are most proximally predicted by harm and not disgust, consistent with dyadic morality. PMID:27217244

  19. Testing the reward prediction error hypothesis with an axiomatic model.

    PubMed

    Rutledge, Robb B; Dean, Mark; Caplin, Andrew; Glimcher, Paul W

    2010-10-01

    Neuroimaging studies typically identify neural activity correlated with the predictions of highly parameterized models, like the many reward prediction error (RPE) models used to study reinforcement learning. Identified brain areas might encode RPEs or, alternatively, only have activity correlated with RPE model predictions. Here, we use an alternate axiomatic approach rooted in economic theory to formally test the entire class of RPE models on neural data. We show that measurements of human neural activity from the striatum, medial prefrontal cortex, amygdala, and posterior cingulate cortex satisfy necessary and sufficient conditions for the entire class of RPE models. However, activity measured from the anterior insula falsifies the axiomatic model, and therefore no RPE model can account for measured activity. Further analysis suggests the anterior insula might instead encode something related to the salience of an outcome. As cognitive neuroscience matures and models proliferate, formal approaches of this kind that assess entire model classes rather than specific model exemplars may take on increased significance.

  20. Impaired Neural Response to Negative Prediction Errors in Cocaine Addiction

    PubMed Central

    Parvaz, Muhammad A.; Konova, Anna B.; Proudfit, Greg H.; Dunning, Jonathan P.; Malaker, Pias; Moeller, Scott J.; Maloney, Tom; Alia-Klein, Nelly

    2015-01-01

    Learning can be guided by unexpected success or failure, signaled via dopaminergic positive reward prediction error (+RPE) and negative reward-prediction error (−RPE) signals, respectively. Despite conflicting empirical evidence, RPE signaling is thought to be impaired in drug addiction. To resolve this outstanding question, we studied as a measure of RPE the feedback negativity (FN) that is sensitive to both reward and the violation of expectation. We examined FN in 25 healthy controls; 25 individuals with cocaine-use disorder (CUD) who tested positive for cocaine on the study day (CUD+), indicating cocaine use within the past 72 h; and in 25 individuals with CUD who tested negative for cocaine (CUD−). EEG was acquired while the participants performed a gambling task predicting whether they would win or lose money on each trial given three known win probabilities (25, 50, or 75%). FN was scored for the period in each trial when the actual outcome (win or loss) was revealed. A significant interaction between prediction, outcome, and group revealed that controls showed increased FN to unpredicted compared with predicted wins (i.e., intact +RPE) and decreased FN to unpredicted compared with predicted losses (i.e., intact −RPE). However, neither CUD subgroup showed FN modulation to loss (i.e., impaired −RPE), and unlike CUD+ individuals, CUD− individuals also did not show FN modulation to win (i.e., impaired +RPE). Thus, using FN, the current study directly documents −RPE deficits in CUD individuals. The mechanisms underlying −RPE signaling impairments in addiction may contribute to the disadvantageous nature of excessive drug use, which can persist despite repeated unfavorable life experiences (e.g., frequent incarcerations). PMID:25653348

  1. Impaired neural response to negative prediction errors in cocaine addiction.

    PubMed

    Parvaz, Muhammad A; Konova, Anna B; Proudfit, Greg H; Dunning, Jonathan P; Malaker, Pias; Moeller, Scott J; Maloney, Tom; Alia-Klein, Nelly; Goldstein, Rita Z

    2015-02-01

    Learning can be guided by unexpected success or failure, signaled via dopaminergic positive reward prediction error (+RPE) and negative reward-prediction error (-RPE) signals, respectively. Despite conflicting empirical evidence, RPE signaling is thought to be impaired in drug addiction. To resolve this outstanding question, we studied as a measure of RPE the feedback negativity (FN) that is sensitive to both reward and the violation of expectation. We examined FN in 25 healthy controls; 25 individuals with cocaine-use disorder (CUD) who tested positive for cocaine on the study day (CUD+), indicating cocaine use within the past 72 h; and in 25 individuals with CUD who tested negative for cocaine (CUD-). EEG was acquired while the participants performed a gambling task predicting whether they would win or lose money on each trial given three known win probabilities (25, 50, or 75%). FN was scored for the period in each trial when the actual outcome (win or loss) was revealed. A significant interaction between prediction, outcome, and group revealed that controls showed increased FN to unpredicted compared with predicted wins (i.e., intact +RPE) and decreased FN to unpredicted compared with predicted losses (i.e., intact -RPE). However, neither CUD subgroup showed FN modulation to loss (i.e., impaired -RPE), and unlike CUD+ individuals, CUD- individuals also did not show FN modulation to win (i.e., impaired +RPE). Thus, using FN, the current study directly documents -RPE deficits in CUD individuals. The mechanisms underlying -RPE signaling impairments in addiction may contribute to the disadvantageous nature of excessive drug use, which can persist despite repeated unfavorable life experiences (e.g., frequent incarcerations). PMID:25653348

  2. Left-hemisphere activation is associated with enhanced vocal pitch error detection in musicians with absolute pitch

    PubMed Central

    Behroozmand, Roozbeh; Ibrahim, Nadine; Korzyukov, Oleg; Robin, Donald A.; Larson, Charles R.

    2014-01-01

    The ability to process auditory feedback for vocal pitch control is crucial during speaking and singing. Previous studies have suggested that musicians with absolute pitch (AP) develop specialized left-hemisphere mechanisms for pitch processing. The present study adopted an auditory feedback pitch perturbation paradigm combined with ERP recordings to test the hypothesis whether the neural mechanisms of the left-hemisphere enhance vocal pitch error detection and control in AP musicians compared with relative pitch (RP) musicians and non-musicians (NM). Results showed a stronger N1 response to pitch-shifted voice feedback in the right-hemisphere for both AP and RP musicians compared with the NM group. However, the left-hemisphere P2 component activation was greater in AP and RP musicians compared with NMs and also for the AP compared with RP musicians. The NM group was slower in generating compensatory vocal reactions to feedback pitch perturbation compared with musicians, and they failed to re-adjust their vocal pitch after the feedback perturbation was removed. These findings suggest that in the earlier stages of cortical neural processing, the right hemisphere is more active in musicians for detecting pitch changes in voice feedback. In the later stages, the left-hemisphere is more active during the processing of auditory feedback for vocal motor control and seems to involve specialized mechanisms that facilitate pitch processing in the AP compared with RP musicians. These findings indicate that the left hemisphere mechanisms of AP ability are associated with improved auditory feedback pitch processing during vocal pitch control in tasks such as speaking or singing. PMID:24355545

  3. Time-series modeling and prediction of global monthly absolute temperature for environmental decision making

    NASA Astrophysics Data System (ADS)

    Ye, Liming; Yang, Guixia; Van Ranst, Eric; Tang, Huajun

    2013-03-01

    A generalized, structural, time series modeling framework was developed to analyze the monthly records of absolute surface temperature, one of the most important environmental parameters, using a deterministicstochastic combined (DSC) approach. Although the development of the framework was based on the characterization of the variation patterns of a global dataset, the methodology could be applied to any monthly absolute temperature record. Deterministic processes were used to characterize the variation patterns of the global trend and the cyclic oscillations of the temperature signal, involving polynomial functions and the Fourier method, respectively, while stochastic processes were employed to account for any remaining patterns in the temperature signal, involving seasonal autoregressive integrated moving average (SARIMA) models. A prediction of the monthly global surface temperature during the second decade of the 21st century using the DSC model shows that the global temperature will likely continue to rise at twice the average rate of the past 150 years. The evaluation of prediction accuracy shows that DSC models perform systematically well against selected models of other authors, suggesting that DSC models, when coupled with other ecoenvironmental models, can be used as a supplemental tool for short-term (˜10-year) environmental planning and decision making.

  4. Efron-type measures of prediction error for survival analysis.

    PubMed

    Gerds, Thomas A; Schumacher, Martin

    2007-12-01

    Estimates of the prediction error play an important role in the development of statistical methods and models, and in their applications. We adapt the resampling tools of Efron and Tibshirani (1997, Journal of the American Statistical Association92, 548-560) to survival analysis with right-censored event times. We find that flexible rules, like artificial neural nets, classification and regression trees, or regression splines can be assessed, and compared to less flexible rules in the same data where they are developed. The methods are illustrated with data from a breast cancer trial.

  5. CREME96 and Related Error Rate Prediction Methods

    NASA Technical Reports Server (NTRS)

    Adams, James H., Jr.

    2012-01-01

    Predicting the rate of occurrence of single event effects (SEEs) in space requires knowledge of the radiation environment and the response of electronic devices to that environment. Several analytical models have been developed over the past 36 years to predict SEE rates. The first error rate calculations were performed by Binder, Smith and Holman. Bradford and Pickel and Blandford, in their CRIER (Cosmic-Ray-Induced-Error-Rate) analysis code introduced the basic Rectangular ParallelePiped (RPP) method for error rate calculations. For the radiation environment at the part, both made use of the Cosmic Ray LET (Linear Energy Transfer) spectra calculated by Heinrich for various absorber Depths. A more detailed model for the space radiation environment within spacecraft was developed by Adams and co-workers. This model, together with a reformulation of the RPP method published by Pickel and Blandford, was used to create the CR ME (Cosmic Ray Effects on Micro-Electronics) code. About the same time Shapiro wrote the CRUP (Cosmic Ray Upset Program) based on the RPP method published by Bradford. It was the first code to specifically take into account charge collection from outside the depletion region due to deformation of the electric field caused by the incident cosmic ray. Other early rate prediction methods and codes include the Single Event Figure of Merit, NOVICE, the Space Radiation code and the effective flux method of Binder which is the basis of the SEFA (Scott Effective Flux Approximation) model. By the early 1990s it was becoming clear that CREME and the other early models needed Revision. This revision, CREME96, was completed and released as a WWW-based tool, one of the first of its kind. The revisions in CREME96 included improved environmental models and improved models for calculating single event effects. The need for a revision of CREME also stimulated the development of the CHIME (CRRES/SPACERAD Heavy Ion Model of the Environment) and MACREE (Modeling and

  6. 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.

  7. Deep and beautiful. The reward prediction error hypothesis of dopamine.

    PubMed

    Colombo, Matteo

    2014-03-01

    According to the reward-prediction error hypothesis (RPEH) of dopamine, the phasic activity of dopaminergic neurons in the midbrain signals a discrepancy between the predicted and currently experienced reward of a particular event. It can be claimed that this hypothesis is deep, elegant and beautiful, representing one of the largest successes of computational neuroscience. This paper examines this claim, making two contributions to existing literature. First, it draws a comprehensive historical account of the main steps that led to the formulation and subsequent success of the RPEH. Second, in light of this historical account, it explains in which sense the RPEH is explanatory and under which conditions it can be justifiably deemed deeper than the incentive salience hypothesis of dopamine, which is arguably the most prominent contemporary alternative to the RPEH. PMID:24252364

  8. Deep and beautiful. The reward prediction error hypothesis of dopamine.

    PubMed

    Colombo, Matteo

    2014-03-01

    According to the reward-prediction error hypothesis (RPEH) of dopamine, the phasic activity of dopaminergic neurons in the midbrain signals a discrepancy between the predicted and currently experienced reward of a particular event. It can be claimed that this hypothesis is deep, elegant and beautiful, representing one of the largest successes of computational neuroscience. This paper examines this claim, making two contributions to existing literature. First, it draws a comprehensive historical account of the main steps that led to the formulation and subsequent success of the RPEH. Second, in light of this historical account, it explains in which sense the RPEH is explanatory and under which conditions it can be justifiably deemed deeper than the incentive salience hypothesis of dopamine, which is arguably the most prominent contemporary alternative to the RPEH.

  9. Dopamine Reward Prediction Error Responses Reflect Marginal Utility

    PubMed Central

    Stauffer, William R.; Lak, Armin; Schultz, Wolfram

    2014-01-01

    Summary Background Optimal choices require an accurate neuronal representation of economic value. In economics, utility functions are mathematical representations of subjective value that can be constructed from choices under risk. Utility usually exhibits a nonlinear relationship to physical reward value that corresponds to risk attitudes and reflects the increasing or decreasing marginal utility obtained with each additional unit of reward. Accordingly, neuronal reward responses coding utility should robustly reflect this nonlinearity. Results In two monkeys, we measured utility as a function of physical reward value from meaningful choices under risk (that adhered to first- and second-order stochastic dominance). The resulting nonlinear utility functions predicted the certainty equivalents for new gambles, indicating that the functions’ shapes were meaningful. The monkeys were risk seeking (convex utility function) for low reward and risk avoiding (concave utility function) with higher amounts. Critically, the dopamine prediction error responses at the time of reward itself reflected the nonlinear utility functions measured at the time of choices. In particular, the reward response magnitude depended on the first derivative of the utility function and thus reflected the marginal utility. Furthermore, dopamine responses recorded outside of the task reflected the marginal utility of unpredicted reward. Accordingly, these responses were sufficient to train reinforcement learning models to predict the behaviorally defined expected utility of gambles. Conclusions These data suggest a neuronal manifestation of marginal utility in dopamine neurons and indicate a common neuronal basis for fundamental explanatory constructs in animal learning theory (prediction error) and economic decision theory (marginal utility). PMID:25283778

  10. Chasing probabilities - Signaling negative and positive prediction errors across domains.

    PubMed

    Meder, David; Madsen, Kristoffer H; Hulme, Oliver; Siebner, Hartwig R

    2016-07-01

    Adaptive actions build on internal probabilistic models of possible outcomes that are tuned according to the errors of their predictions when experiencing an actual outcome. Prediction errors (PEs) inform choice behavior across a diversity of outcome domains and dimensions, yet neuroimaging studies have so far only investigated such signals in singular experimental contexts. It is thus unclear whether the neuroanatomical distribution of PE encoding reported previously pertains to computational features that are invariant with respect to outcome valence, sensory domain, or some combination of the two. We acquired functional MRI data while volunteers performed four probabilistic reversal learning tasks which differed in terms of outcome valence (reward-seeking versus punishment-avoidance) and domain (abstract symbols versus facial expressions) of outcomes. We found that ventral striatum and frontopolar cortex coded increasingly positive PEs, whereas dorsal anterior cingulate cortex (dACC) traced increasingly negative PEs, irrespectively of the outcome dimension. Individual reversal behavior was unaffected by context manipulations and was predicted by activity in dACC and right inferior frontal gyrus (IFG). The stronger the response to negative PEs in these areas, the lower was the tendency to reverse choice behavior in response to negative events, suggesting that these regions enforce a rule-based strategy across outcome dimensions. Outcome valence influenced PE-related activity in left amygdala, IFG, and dorsomedial prefrontal cortex, where activity selectively scaled with increasingly positive PEs in the reward-seeking but not punishment-avoidance context, irrespective of sensory domain. Left amygdala displayed an additional influence of sensory domain. In the context of avoiding punishment, amygdala activity increased with increasingly negative PEs, but only for facial stimuli, indicating an integration of outcome valence and sensory domain during probabilistic

  11. Prediction of Error and Error Type in Computation of Sixth Grade Mathematics Students.

    ERIC Educational Resources Information Center

    Baxter, Marion McComb

    The study of computational errors among sixth grade students included identification and classification of errors, investigation of the effects of two feedback treatments and of classwork and homework on error patterns, and investigation of the relationships of error patterns with intelligence, mathematics achievement, attitudes toward…

  12. Representation of aversive prediction errors in the human periaqueductal gray.

    PubMed

    Roy, Mathieu; Shohamy, Daphna; Daw, Nathaniel; Jepma, Marieke; Wimmer, G Elliott; Wager, Tor D

    2014-11-01

    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), a structure important 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.

  13. Reward prediction error coding in dorsal striatal neurons.

    PubMed

    Oyama, Kei; Hernádi, István; Iijima, Toshio; Tsutsui, Ken-Ichiro

    2010-08-25

    In the current theory of learning, the reward prediction error (RPE), the difference between expected and received reward, is thought to be a key factor in reward-based learning, working as a teaching signal. The activity of dopamine neurons is known to code RPE, and the release of dopamine is known to modify the strength of synaptic connectivity in the target neurons. A fundamental interest in current neuroscience concerns the origin of RPE signals in the brain. Here, we show that a group of rat striatal neurons show a clear parametric RPE coding similar to that of dopamine neurons when tested under probabilistic pavlovian conditioning. Together with the fact that striatum and dopamine neurons have strong direct and indirect fiber connections, the result suggests that the striatum plays an important role in coding RPE signal by cooperating with dopamine neurons.

  14. Temporal prediction errors modulate task-switching performance.

    PubMed

    Limongi, Roberto; Silva, Angélica M; Góngora-Costa, Begoña

    2015-01-01

    We have previously shown that temporal prediction errors (PEs, the differences between the expected and the actual stimulus' onset times) modulate the effective connectivity between the anterior cingulate cortex and the right anterior insular cortex (rAI), causing the activity of the rAI to decrease. The activity of the rAI is associated with efficient performance under uncertainty (e.g., changing a prepared behavior when a change demand is not expected), which leads to hypothesize that temporal PEs might disrupt behavior-change performance under uncertainty. This hypothesis has not been tested at a behavioral level. In this work, we evaluated this hypothesis within the context of task switching and concurrent temporal predictions. Our participants performed temporal predictions while observing one moving ball striking a stationary ball which bounced off with a variable temporal gap. Simultaneously, they performed a simple color comparison task. In some trials, a change signal made the participants change their behaviors. Performance accuracy decreased as a function of both the temporal PE and the delay. Explaining these results without appealing to ad hoc concepts such as "executive control" is a challenge for cognitive neuroscience. We provide a predictive coding explanation. We hypothesize that exteroceptive and proprioceptive minimization of PEs would converge in a fronto-basal ganglia network which would include the rAI. Both temporal gaps (or uncertainty) and temporal PEs would drive and modulate this network respectively. Whereas the temporal gaps would drive the activity of the rAI, the temporal PEs would modulate the endogenous excitatory connections of the fronto-striatal network. We conclude that in the context of perceptual uncertainty, the system is not able to minimize perceptual PE, causing the ongoing behavior to finalize and, in consequence, disrupting task switching. PMID:26379568

  15. Temporal prediction errors modulate task-switching performance

    PubMed Central

    Limongi, Roberto; Silva, Angélica M.; Góngora-Costa, Begoña

    2015-01-01

    We have previously shown that temporal prediction errors (PEs, the differences between the expected and the actual stimulus’ onset times) modulate the effective connectivity between the anterior cingulate cortex and the right anterior insular cortex (rAI), causing the activity of the rAI to decrease. The activity of the rAI is associated with efficient performance under uncertainty (e.g., changing a prepared behavior when a change demand is not expected), which leads to hypothesize that temporal PEs might disrupt behavior-change performance under uncertainty. This hypothesis has not been tested at a behavioral level. In this work, we evaluated this hypothesis within the context of task switching and concurrent temporal predictions. Our participants performed temporal predictions while observing one moving ball striking a stationary ball which bounced off with a variable temporal gap. Simultaneously, they performed a simple color comparison task. In some trials, a change signal made the participants change their behaviors. Performance accuracy decreased as a function of both the temporal PE and the delay. Explaining these results without appealing to ad hoc concepts such as “executive control” is a challenge for cognitive neuroscience. We provide a predictive coding explanation. We hypothesize that exteroceptive and proprioceptive minimization of PEs would converge in a fronto-basal ganglia network which would include the rAI. Both temporal gaps (or uncertainty) and temporal PEs would drive and modulate this network respectively. Whereas the temporal gaps would drive the activity of the rAI, the temporal PEs would modulate the endogenous excitatory connections of the fronto-striatal network. We conclude that in the context of perceptual uncertainty, the system is not able to minimize perceptual PE, causing the ongoing behavior to finalize and, in consequence, disrupting task switching. PMID:26379568

  16. Temporal prediction errors modulate task-switching performance.

    PubMed

    Limongi, Roberto; Silva, Angélica M; Góngora-Costa, Begoña

    2015-01-01

    We have previously shown that temporal prediction errors (PEs, the differences between the expected and the actual stimulus' onset times) modulate the effective connectivity between the anterior cingulate cortex and the right anterior insular cortex (rAI), causing the activity of the rAI to decrease. The activity of the rAI is associated with efficient performance under uncertainty (e.g., changing a prepared behavior when a change demand is not expected), which leads to hypothesize that temporal PEs might disrupt behavior-change performance under uncertainty. This hypothesis has not been tested at a behavioral level. In this work, we evaluated this hypothesis within the context of task switching and concurrent temporal predictions. Our participants performed temporal predictions while observing one moving ball striking a stationary ball which bounced off with a variable temporal gap. Simultaneously, they performed a simple color comparison task. In some trials, a change signal made the participants change their behaviors. Performance accuracy decreased as a function of both the temporal PE and the delay. Explaining these results without appealing to ad hoc concepts such as "executive control" is a challenge for cognitive neuroscience. We provide a predictive coding explanation. We hypothesize that exteroceptive and proprioceptive minimization of PEs would converge in a fronto-basal ganglia network which would include the rAI. Both temporal gaps (or uncertainty) and temporal PEs would drive and modulate this network respectively. Whereas the temporal gaps would drive the activity of the rAI, the temporal PEs would modulate the endogenous excitatory connections of the fronto-striatal network. We conclude that in the context of perceptual uncertainty, the system is not able to minimize perceptual PE, causing the ongoing behavior to finalize and, in consequence, disrupting task switching.

  17. Striatal prediction errors support dynamic control of declarative memory decisions

    PubMed Central

    Scimeca, Jason M.; Katzman, Perri L.; Badre, David

    2016-01-01

    Adaptive memory requires context-dependent control over how information is retrieved, evaluated and used to guide action, yet the signals that drive adjustments to memory decisions remain unknown. Here we show that prediction errors (PEs) coded by the striatum support control over memory decisions. Human participants completed a recognition memory test that incorporated biased feedback to influence participants' recognition criterion. Using model-based fMRI, we find that PEs—the deviation between the outcome and expected value of a memory decision—correlate with striatal activity and predict individuals' final criterion. Importantly, the striatal PEs are scaled relative to memory strength rather than the expected trial outcome. Follow-up experiments show that the learned recognition criterion transfers to free recall, and targeting biased feedback to experimentally manipulate the magnitude of PEs influences criterion consistent with PEs scaled relative to memory strength. This provides convergent evidence that declarative memory decisions can be regulated via striatally mediated reinforcement learning signals. PMID:27713407

  18. Heavy-tailed prediction error: a difficulty in predicting biomedical signals of 1/f noise type.

    PubMed

    Li, Ming; Zhao, Wei; Chen, Biao

    2012-01-01

    A fractal signal x(t) in biomedical engineering may be characterized by 1/f noise, that is, the power spectrum density (PSD) divergences at f = 0. According the Taqqu's law, 1/f noise has the properties of long-range dependence and heavy-tailed probability density function (PDF). The contribution of this paper is to exhibit that the prediction error of a biomedical signal of 1/f noise type is long-range dependent (LRD). Thus, it is heavy-tailed and of 1/f noise. Consequently, the variance of the prediction error is usually large or may not exist, making predicting biomedical signals of 1/f noise type difficult. PMID:23251226

  19. Perceptual learning of degraded speech by minimizing prediction error.

    PubMed

    Sohoglu, Ediz; Davis, Matthew H

    2016-03-22

    Human perception is shaped by past experience on multiple timescales. Sudden and dramatic changes in perception occur when prior knowledge or expectations match stimulus content. These immediate effects contrast with the longer-term, more gradual improvements that are characteristic of perceptual learning. Despite extensive investigation of these two experience-dependent phenomena, there is considerable debate about whether they result from common or dissociable neural mechanisms. Here we test single- and dual-mechanism accounts of experience-dependent changes in perception using concurrent magnetoencephalographic and EEG recordings of neural responses evoked by degraded speech. When speech clarity was enhanced by prior knowledge obtained from matching text, we observed reduced neural activity in a peri-auditory region of the superior temporal gyrus (STG). Critically, longer-term improvements in the accuracy of speech recognition following perceptual learning resulted in reduced activity in a nearly identical STG region. Moreover, short-term neural changes caused by prior knowledge and longer-term neural changes arising from perceptual learning were correlated across subjects with the magnitude of learning-induced changes in recognition accuracy. These experience-dependent effects on neural processing could be dissociated from the neural effect of hearing physically clearer speech, which similarly enhanced perception but increased rather than decreased STG responses. Hence, the observed neural effects of prior knowledge and perceptual learning cannot be attributed to epiphenomenal changes in listening effort that accompany enhanced perception. Instead, our results support a predictive coding account of speech perception; computational simulations show how a single mechanism, minimization of prediction error, can drive immediate perceptual effects of prior knowledge and longer-term perceptual learning of degraded speech. PMID:26957596

  20. Perceptual learning of degraded speech by minimizing prediction error

    PubMed Central

    Sohoglu, Ediz

    2016-01-01

    Human perception is shaped by past experience on multiple timescales. Sudden and dramatic changes in perception occur when prior knowledge or expectations match stimulus content. These immediate effects contrast with the longer-term, more gradual improvements that are characteristic of perceptual learning. Despite extensive investigation of these two experience-dependent phenomena, there is considerable debate about whether they result from common or dissociable neural mechanisms. Here we test single- and dual-mechanism accounts of experience-dependent changes in perception using concurrent magnetoencephalographic and EEG recordings of neural responses evoked by degraded speech. When speech clarity was enhanced by prior knowledge obtained from matching text, we observed reduced neural activity in a peri-auditory region of the superior temporal gyrus (STG). Critically, longer-term improvements in the accuracy of speech recognition following perceptual learning resulted in reduced activity in a nearly identical STG region. Moreover, short-term neural changes caused by prior knowledge and longer-term neural changes arising from perceptual learning were correlated across subjects with the magnitude of learning-induced changes in recognition accuracy. These experience-dependent effects on neural processing could be dissociated from the neural effect of hearing physically clearer speech, which similarly enhanced perception but increased rather than decreased STG responses. Hence, the observed neural effects of prior knowledge and perceptual learning cannot be attributed to epiphenomenal changes in listening effort that accompany enhanced perception. Instead, our results support a predictive coding account of speech perception; computational simulations show how a single mechanism, minimization of prediction error, can drive immediate perceptual effects of prior knowledge and longer-term perceptual learning of degraded speech. PMID:26957596

  1. Perceptual learning of degraded speech by minimizing prediction error.

    PubMed

    Sohoglu, Ediz; Davis, Matthew H

    2016-03-22

    Human perception is shaped by past experience on multiple timescales. Sudden and dramatic changes in perception occur when prior knowledge or expectations match stimulus content. These immediate effects contrast with the longer-term, more gradual improvements that are characteristic of perceptual learning. Despite extensive investigation of these two experience-dependent phenomena, there is considerable debate about whether they result from common or dissociable neural mechanisms. Here we test single- and dual-mechanism accounts of experience-dependent changes in perception using concurrent magnetoencephalographic and EEG recordings of neural responses evoked by degraded speech. When speech clarity was enhanced by prior knowledge obtained from matching text, we observed reduced neural activity in a peri-auditory region of the superior temporal gyrus (STG). Critically, longer-term improvements in the accuracy of speech recognition following perceptual learning resulted in reduced activity in a nearly identical STG region. Moreover, short-term neural changes caused by prior knowledge and longer-term neural changes arising from perceptual learning were correlated across subjects with the magnitude of learning-induced changes in recognition accuracy. These experience-dependent effects on neural processing could be dissociated from the neural effect of hearing physically clearer speech, which similarly enhanced perception but increased rather than decreased STG responses. Hence, the observed neural effects of prior knowledge and perceptual learning cannot be attributed to epiphenomenal changes in listening effort that accompany enhanced perception. Instead, our results support a predictive coding account of speech perception; computational simulations show how a single mechanism, minimization of prediction error, can drive immediate perceptual effects of prior knowledge and longer-term perceptual learning of degraded speech.

  2. Sensorimotor adaptation error signals are derived from realistic predictions of movement outcomes.

    PubMed

    Wong, Aaron L; Shelhamer, Mark

    2011-03-01

    Neural systems that control movement maintain accuracy by adaptively altering motor commands in response to errors. It is often assumed that the error signal that drives adaptation is equivalent to the sensory error observed at the conclusion of a movement; for saccades, this is typically the visual (retinal) error. However, we instead propose that the adaptation error signal is derived as the difference between the observed visual error and a realistic prediction of movement outcome. Using a modified saccade-adaptation task in human subjects, we precisely controlled the amount of error experienced at the conclusion of a movement by back-stepping the target so that the saccade is hypometric (positive retinal error), but less hypometric than if the target had not moved (smaller retinal error than expected). This separates prediction error from both visual errors and motor corrections. Despite positive visual errors and forward-directed motor corrections, we found an adaptive decrease in saccade amplitudes, a finding that is well-explained by the employment of a prediction-based error signal. Furthermore, adaptive changes in movement size were linearly correlated to the disparity between the predicted and observed movement outcomes, in agreement with the forward-model hypothesis of motor learning, which states that adaptation error signals incorporate predictions of motor outcomes computed using a copy of the motor command (efference copy).

  3. Seasonal prediction of Indian summer monsoon rainfall in NCEP CFSv2: forecast and predictability error

    NASA Astrophysics Data System (ADS)

    Pokhrel, Samir; Saha, Subodh Kumar; Dhakate, Ashish; Rahman, Hasibur; Chaudhari, Hemantkumar S.; Salunke, Kiran; Hazra, Anupam; Sujith, K.; Sikka, D. R.

    2016-04-01

    A detailed analysis of sensitivity to the initial condition for the simulation of the Indian summer monsoon using retrospective forecast by the latest version of the Climate Forecast System version-2 (CFSv2) is carried out. This study primarily focuses on the tropical region of Indian and Pacific Ocean basin, with special emphasis on the Indian land region. The simulated seasonal mean and the inter-annual standard deviations of rainfall, upper and lower level atmospheric circulations and Sea Surface Temperature (SST) tend to be more skillful as the lead forecast time decreases (5 month lead to 0 month lead time i.e. L5-L0). In general spatial correlation (bias) increases (decreases) as forecast lead time decreases. This is further substantiated by their averaged value over the selected study regions over the Indian and Pacific Ocean basins. The tendency of increase (decrease) of model bias with increasing (decreasing) forecast lead time also indicates the dynamical drift of the model. Large scale lower level circulation (850 hPa) shows enhancement of anomalous westerlies (easterlies) over the tropical region of the Indian Ocean (Western Pacific Ocean), which indicates the enhancement of model error with the decrease in lead time. At the upper level circulation (200 hPa) biases in both tropical easterly jet and subtropical westerlies jet tend to decrease as the lead time decreases. Despite enhancement of the prediction skill, mean SST bias seems to be insensitive to the initialization. All these biases are significant and together they make CFSv2 vulnerable to seasonal uncertainties in all the lead times. Overall the zeroth lead (L0) seems to have the best skill, however, in case of Indian summer monsoon rainfall (ISMR), the 3 month lead forecast time (L3) has the maximum ISMR prediction skill. This is valid using different independent datasets, wherein these maximum skill scores are 0.64, 0.42 and 0.57 with respect to the Global Precipitation Climatology Project

  4. A Predictive Approach to Eliminating Errors in Software Code

    NASA Technical Reports Server (NTRS)

    2006-01-01

    NASA s Metrics Data Program Data Repository is a database that stores problem, product, and metrics data. The primary goal of this data repository is to provide project data to the software community. In doing so, the Metrics Data Program collects artifacts from a large NASA dataset, generates metrics on the artifacts, and then generates reports that are made available to the public at no cost. The data that are made available to general users have been sanitized and authorized for publication through the Metrics Data Program Web site by officials representing the projects from which the data originated. The data repository is operated by NASA s Independent Verification and Validation (IV&V) Facility, which is located in Fairmont, West Virginia, a high-tech hub for emerging innovation in the Mountain State. The IV&V Facility was founded in 1993, under the NASA Office of Safety and Mission Assurance, as a direct result of recommendations made by the National Research Council and the Report of the Presidential Commission on the Space Shuttle Challenger Accident. Today, under the direction of Goddard Space Flight Center, the IV&V Facility continues its mission to provide the highest achievable levels of safety and cost-effectiveness for mission-critical software. By extending its data to public users, the facility has helped improve the safety, reliability, and quality of complex software systems throughout private industry and other government agencies. Integrated Software Metrics, Inc., is one of the organizations that has benefited from studying the metrics data. As a result, the company has evolved into a leading developer of innovative software-error prediction tools that help organizations deliver better software, on time and on budget.

  5. Absolute monocyte count predicts overall survival in mantle cell lymphomas: correlation with tumour-associated macrophages.

    PubMed

    Koh, Young Wha; Shin, Su-Jin; Park, Chansik; Yoon, Dok Hyun; Suh, Cheolwon; Huh, Jooryung

    2014-12-01

    Mantle cell lymphoma (MCL) is characterized by a variable clinical course in which patients can experience indolent disease or frequent relapses despite a good initial response to conventional therapy. Risk stratification of MCL is most frequently performed using the MCL International Prognostic Index (MIPI). Recent studies indicate that the peripheral blood absolute monocyte count (AMC) and tumour-associated macrophages may reflect the state of the tumour microenvironment in lymphomas. The significance of AMC and tumour-associated macrophages in the clinical course of MCL is unknown. The prognostic impact of the AMC, of CD68 expression and of CD163 expression was retrospectively examined in 103 MCL samples using the receiver operating characteristic curved. Patients with an AMC ≥ 375 cells/μL at diagnosis were more likely to present with advanced-stage disease (p = 0.026), leukocytosis (p < 0.001), lymphocytosis (p = 0.01) and granulocytosis (p = 0.003). On univariate analysis, a high AMC (≥375 cells/μL) correlated with poorer overall survival (OS) (p = 0.01). Neither CD68 nor CD163 expression was significantly associated with either OS or event-free survival. Multivariate analysis showed that a high AMC was a prognostic factor for OS, independent of the MIPI [hazards ratio (HR), 1.811; 95% confidence interval, 1.018-3.223; p = 0.043]. This study demonstrates that the AMC at the time of diagnosis is an independent prognostic factor for OS in MCL, which suggests the possibility that AMC may be used in addition to the MIPI to predict outcome in patients with MCL.

  6. Design, performance, and calculated error of a Faraday cup for absolute beam current measurements of 600-MeV protons

    NASA Technical Reports Server (NTRS)

    Beck, S. M.

    1975-01-01

    A mobile self-contained Faraday cup system for beam current measurments of nominal 600 MeV protons was designed, constructed, and used at the NASA Space Radiation Effects Laboratory. The cup is of reentrant design with a length of 106.7 cm and an outside diameter of 20.32 cm. The inner diameter is 15.24 cm and the base thickness is 30.48 cm. The primary absorber is commercially available lead hermetically sealed in a 0.32-cm-thick copper jacket. Several possible systematic errors in using the cup are evaluated. The largest source of error arises from high-energy electrons which are ejected from the entrance window and enter the cup. A total systematic error of -0.83 percent is calculated to be the decrease from the true current value. From data obtained in calibrating helium-filled ion chambers with the Faraday cup, the mean energy required to produce one ion pair in helium is found to be 30.76 + or - 0.95 eV for nominal 600 MeV protons. This value agrees well, within experimental error, with reported values of 29.9 eV and 30.2 eV.

  7. Uncertainty in predictions of forest carbon dynamics: separating driver error from model error.

    PubMed

    Spadavecchia, L; Williams, M; Law, B E

    2011-07-01

    We present an analysis of the relative magnitude and contribution of parameter and driver uncertainty to the confidence intervals on estimates of net carbon fluxes. Model parameters may be difficult or impractical to measure, while driver fields are rarely complete, with data gaps due to sensor failure and sparse observational networks. Parameters are generally derived through some optimization method, while driver fields may be interpolated from available data sources. For this study, we used data from a young ponderosa pine stand at Metolius, Central Oregon, and a simple daily model of coupled carbon and water fluxes (DALEC). An ensemble of acceptable parameterizations was generated using an ensemble Kalman filter and eddy covariance measurements of net C exchange. Geostatistical simulations generated an ensemble of meteorological driving variables for the site, consistent with the spatiotemporal autocorrelations inherent in the observational data from 13 local weather stations. Simulated meteorological data were propagated through the model to derive the uncertainty on the CO2 flux resultant from driver uncertainty typical of spatially extensive modeling studies. Furthermore, the model uncertainty was partitioned between temperature and precipitation. With at least one meteorological station within 25 km of the study site, driver uncertainty was relatively small ( 10% of the total net flux), while parameterization uncertainty was larger, 50% of the total net flux. The largest source of driver uncertainty was due to temperature (8% of the total flux). The combined effect of parameter and driver uncertainty was 57% of the total net flux. However, when the nearest meteorological station was > 100 km from the study site, uncertainty in net ecosystem exchange (NEE) predictions introduced by meteorological drivers increased by 88%. Precipitation estimates were a larger source of bias in NEE estimates than were temperature estimates, although the biases partly

  8. Non-conscious prediction and a role for consciousness in correcting prediction errors.

    PubMed

    Pally, Regina

    2005-10-01

    As a result of the evolutionary pressure for survival, the brain relies on a number of non-conscious predictive neural mechanisms which allow for rapid, efficient behavioral responses to the environment. These predictive mechanisms enable the brain to recognize objects by sampling just a few sensory inputs, to anticipate what events are likely to occur and to prepare a response before events actually occur. Consciousness appears to play a role in the detection and correction of prediction errors. The author, a psychotherapist and psychoanalyst, proposes that this monitoring or oversight function of consciousness can be used to understand how conscious awareness facilitates change in the psychotherapeutic treatment of patients who repeat maladaptive patterns of behavior.

  9. Brief optogenetic inhibition of dopamine neurons mimics endogenous negative reward prediction errors.

    PubMed

    Chang, Chun Yun; Esber, Guillem R; Marrero-Garcia, Yasmin; Yau, Hau-Jie; Bonci, Antonello; Schoenbaum, Geoffrey

    2016-01-01

    Correlative studies have strongly linked phasic changes in dopamine activity with reward prediction error signaling. But causal evidence that these brief changes in firing actually serve as error signals to drive associative learning is more tenuous. Although there is direct evidence that brief increases can substitute for positive prediction errors, there is no comparable evidence that similarly brief pauses can substitute for negative prediction errors. In the absence of such evidence, the effect of increases in firing could reflect novelty or salience, variables also correlated with dopamine activity. Here we provide evidence in support of the proposed linkage, showing in a modified Pavlovian over-expectation task that brief pauses in the firing of dopamine neurons in rat ventral tegmental area at the time of reward are sufficient to mimic the effects of endogenous negative prediction errors. These results support the proposal that brief changes in the firing of dopamine neurons serve as full-fledged bidirectional prediction error signals. PMID:26642092

  10. Brief optogenetic inhibition of dopamine neurons mimics endogenous negative reward prediction errors.

    PubMed

    Chang, Chun Yun; Esber, Guillem R; Marrero-Garcia, Yasmin; Yau, Hau-Jie; Bonci, Antonello; Schoenbaum, Geoffrey

    2016-01-01

    Correlative studies have strongly linked phasic changes in dopamine activity with reward prediction error signaling. But causal evidence that these brief changes in firing actually serve as error signals to drive associative learning is more tenuous. Although there is direct evidence that brief increases can substitute for positive prediction errors, there is no comparable evidence that similarly brief pauses can substitute for negative prediction errors. In the absence of such evidence, the effect of increases in firing could reflect novelty or salience, variables also correlated with dopamine activity. Here we provide evidence in support of the proposed linkage, showing in a modified Pavlovian over-expectation task that brief pauses in the firing of dopamine neurons in rat ventral tegmental area at the time of reward are sufficient to mimic the effects of endogenous negative prediction errors. These results support the proposal that brief changes in the firing of dopamine neurons serve as full-fledged bidirectional prediction error signals.

  11. Classifying and Predicting Errors of Inpatient Medication Reconciliation

    PubMed Central

    Pippins, Jennifer R.; Gandhi, Tejal K.; Hamann, Claus; Ndumele, Chima D.; Labonville, Stephanie A.; Diedrichsen, Ellen K.; Carty, Marcy G.; Karson, Andrew S.; Bhan, Ishir; Coley, Christopher M.; Liang, Catherine L.; Turchin, Alexander; McCarthy, Patricia C.

    2008-01-01

    Background Failure to reconcile medications across transitions in care is an important source of potential harm to patients. Little is known about the predictors of unintentional medication discrepancies and how, when, and where they occur. Objective To determine the reasons, timing, and predictors of potentially harmful medication discrepancies. Design Prospective observational study. Patients Admitted general medical patients. Measurements Study pharmacists took gold-standard medication histories and compared them with medical teams’ medication histories, admission and discharge orders. Blinded teams of physicians adjudicated all unexplained discrepancies using a modification of an existing typology. The main outcome was the number of potentially harmful unintentional medication discrepancies per patient (potential adverse drug events or PADEs). Results Among 180 patients, 2066 medication discrepancies were identified, and 257 (12%) were unintentional and had potential for harm (1.4 per patient). Of these, 186 (72%) were due to errors taking the preadmission medication history, while 68 (26%) were due to errors reconciling the medication history with discharge orders. Most PADEs occurred at discharge (75%). In multivariable analyses, low patient understanding of preadmission medications, number of medication changes from preadmission to discharge, and medication history taken by an intern were associated with PADEs. Conclusions Unintentional medication discrepancies are common and more often due to errors taking an accurate medication history than errors reconciling this history with patient orders. Focusing on accurate medication histories, on potential medication errors at discharge, and on identifying high-risk patients for more intensive interventions may improve medication safety during and after hospitalization. PMID:18563493

  12. The conditions that promote fear learning: prediction error and Pavlovian fear conditioning.

    PubMed

    Li, Susan Shi Yuan; McNally, Gavan P

    2014-02-01

    A key insight of associative learning theory is that learning depends on the actions of prediction error: a discrepancy between the actual and expected outcomes of a conditioning trial. When positive, such error causes increments in associative strength and, when negative, such error causes decrements in associative strength. Prediction error can act directly on fear learning by determining the effectiveness of the aversive unconditioned stimulus or indirectly by determining the effectiveness, or associability, of the conditioned stimulus. Evidence from a variety of experimental preparations in human and non-human animals suggest that discrete neural circuits code for these actions of prediction error during fear learning. Here we review the circuits and brain regions contributing to the neural coding of prediction error during fear learning and highlight areas of research (safety learning, extinction, and reconsolidation) that may profit from this approach to understanding learning.

  13. Reward prediction error signals associated with a modified time estimation task.

    PubMed

    Holroyd, Clay B; Krigolson, Olave E

    2007-11-01

    The feedback error-related negativity (fERN) is a component of the human event-related brain potential (ERP) elicited by feedback stimuli. A recent theory holds that the fERN indexes a reward prediction error signal associated with the adaptive modification of behavior. Here we present behavioral and ERP data recorded from participants engaged in a modified time estimation task. As predicted by the theory, our results indicate that fERN amplitude reflects a reward prediction error signal and that the size of this error signal is correlated across participants with changes in task performance.

  14. Predicting children's hyperarticulate speech during human-computer error resolution

    NASA Astrophysics Data System (ADS)

    Oviatt, Sharon; Coulston, Rachel; Darves, Courtney

    2003-04-01

    When speaking to interactive systems, people sometimes hyperarticulate-or adopt a clarified form of speech that has been associated with increased recognition errors. The goal of the present study was to provide a comprehensive assessment of the type and magnitude of linguistic adaptations in children's speech during human-computer error resolution, and to compare these adaptations with those typical of adult hyperarticulation. A study was conducted in which twenty-four 7- to- 10-year-old children interacted with a simulated conversational system, which permitted a comparison of their verbatim repetitions immediately before and after system recognition errors. Matched original-repeat utterance pairs then were analyzed for acoustic, prosodic, and phonological adaptations. Like adult speech, the primary hyperarticulate changes in children's speech included durational phenomena such as lengthening of pauses and the speech segment, and a more deliberate, hyper-clear articulatory style. However, children's speech also displayed large increases in amplitude that are not typical of adult hyperarticulation, as well as substantially larger magnitude adaptations than those observed in adult speech. These results corroborate and generalize the Computer-elicited Hyperarticulate Adaptation Model, and have implications for improved error handling in next-generation spoken language and multimodal systems. [Work supported by NSF Grant No. IIS-0117868.

  15. Some Results on Mean Square Error for Factor Score Prediction

    ERIC Educational Resources Information Center

    Krijnen, Wim P.

    2006-01-01

    For the confirmatory factor model a series of inequalities is given with respect to the mean square error (MSE) of three main factor score predictors. The eigenvalues of these MSE matrices are a monotonic function of the eigenvalues of the matrix gamma[subscript rho] = theta[superscript 1/2] lambda[subscript rho] 'psi[subscript rho] [superscript…

  16. Strength of forelimb lateralization predicts motor errors in an insect

    PubMed Central

    Bell, Adrian T. A.

    2016-01-01

    Lateralized behaviours are widespread in both vertebrates and invertebrates, suggesting that lateralization is advantageous. Yet evidence demonstrating proximate or ultimate advantages remains scarce, particularly in invertebrates or in species with individual-level lateralization. Desert locusts (Schistocerca gregaria) are biased in the forelimb they use to perform targeted reaching across a gap. The forelimb and strength of this bias differed among individuals, indicative of individual-level lateralization. Here we show that strongly biased locusts perform better during gap-crossing, making fewer errors with their preferred forelimb. The number of targeting errors locusts make negatively correlates with the strength of forelimb lateralization. This provides evidence that stronger lateralization confers an advantage in terms of improved motor control in an invertebrate with individual-level lateralization. PMID:27651534

  17. Strength of forelimb lateralization predicts motor errors in an insect.

    PubMed

    Bell, Adrian T A; Niven, Jeremy E

    2016-09-01

    Lateralized behaviours are widespread in both vertebrates and invertebrates, suggesting that lateralization is advantageous. Yet evidence demonstrating proximate or ultimate advantages remains scarce, particularly in invertebrates or in species with individual-level lateralization. Desert locusts (Schistocerca gregaria) are biased in the forelimb they use to perform targeted reaching across a gap. The forelimb and strength of this bias differed among individuals, indicative of individual-level lateralization. Here we show that strongly biased locusts perform better during gap-crossing, making fewer errors with their preferred forelimb. The number of targeting errors locusts make negatively correlates with the strength of forelimb lateralization. This provides evidence that stronger lateralization confers an advantage in terms of improved motor control in an invertebrate with individual-level lateralization. PMID:27651534

  18. Prediction Error Associated with the Perceptual Segmentation of Naturalistic Events

    ERIC Educational Resources Information Center

    Zacks, Jeffrey M.; Kurby, Christopher A.; Eisenberg, Michelle L.; Haroutunian, Nayiri

    2011-01-01

    Predicting the near future is important for survival and plays a central role in theories of perception, language processing, and learning. Prediction failures may be particularly important for initiating the updating of perceptual and memory systems and, thus, for the subjective experience of events. Here, we asked observers to make predictions…

  19. Error criteria for cross validation in the context of chaotic time series prediction.

    PubMed

    Lim, Teck Por; Puthusserypady, Sadasivan

    2006-03-01

    The prediction of a chaotic time series over a long horizon is commonly done by iterating one-step-ahead prediction. Prediction can be implemented using machine learning methods, such as radial basis function networks. Typically, cross validation is used to select prediction models based on mean squared error. The bias-variance dilemma dictates that there is an inevitable tradeoff between bias and variance. However, invariants of chaotic systems are unchanged by linear transformations; thus, the bias component may be irrelevant to model selection in the context of chaotic time series prediction. Hence, the use of error variance for model selection, instead of mean squared error, is examined. Clipping is introduced, as a simple way to stabilize iterated predictions. It is shown that using the error variance for model selection, in combination with clipping, may result in better models.

  20. 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.

  1. Comparison of Transmission Error Predictions with Noise Measurements for Several Spur and Helical Gears

    NASA Technical Reports Server (NTRS)

    Houser, Donald R.; Oswald, Fred B.; Valco, Mark J.; Drago, Raymond J.; Lenski, Joseph W., Jr.

    1994-01-01

    Measured sound power data from eight different spur, single and double helical gear designs are compared with predictions of transmission error by the Load Distribution Program. The sound power data was taken from the recent Army-funded Advanced Rotorcraft Transmission project. Tests were conducted in the NASA gear noise rig. Results of both test data and transmission error predictions are made for each harmonic of mesh frequency at several operating conditions. In general, the transmission error predictions compare favorably with the measured noise levels.

  2. Easy Absolute Values? Absolutely

    ERIC Educational Resources Information Center

    Taylor, Sharon E.; Mittag, Kathleen Cage

    2015-01-01

    The authors teach a problem-solving course for preservice middle-grades education majors that includes concepts dealing with absolute-value computations, equations, and inequalities. Many of these students like mathematics and plan to teach it, so they are adept at symbolic manipulations. Getting them to think differently about a concept that they…

  3. A Foundation for the Accurate Prediction of the Soft Error Vulnerability of Scientific Applications

    SciTech Connect

    Bronevetsky, G; de Supinski, B; Schulz, M

    2009-02-13

    Understanding the soft error vulnerability of supercomputer applications is critical as these systems are using ever larger numbers of devices that have decreasing feature sizes and, thus, increasing frequency of soft errors. As many large scale parallel scientific applications use BLAS and LAPACK linear algebra routines, the soft error vulnerability of these methods constitutes a large fraction of the applications overall vulnerability. This paper analyzes the vulnerability of these routines to soft errors by characterizing how their outputs are affected by injected errors and by evaluating several techniques for predicting how errors propagate from the input to the output of each routine. The resulting error profiles can be used to understand the fault vulnerability of full applications that use these routines.

  4. Estimation of Separation Buffers for Wind-Prediction Error in an Airborne Separation Assistance System

    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.

  5. Long-Period Ground Motion Prediction Equations for Relative, Pseudo-Relative and Absolute Velocity Response Spectra in Japan

    NASA Astrophysics Data System (ADS)

    Dhakal, Y. P.; Kunugi, T.; Suzuki, W.; Aoi, S.

    2014-12-01

    Many of the empirical ground motion prediction equations (GMPE) also known as attenuation relations have been developed for absolute acceleration or pseudo relative velocity response spectra. For a small damping, pseudo and absolute acceleration response spectra are nearly identical and hence interchangeable. It is generally known that the relative and pseudo relative velocity response spectra differ considerably at very short or very long periods, and the two are often considered similar at intermediate periods. However, observations show that the period range at which the two spectra become comparable is different from site to site. Also, the relationship of the above two types of velocity response spectra with absolute velocity response spectra are not discussed well in literature. The absolute velocity response spectra are the peak values of time histories obtained by adding the ground velocities to relative velocity response time histories at individual natural periods. There exists many tall buildings on huge and deep sedimentary basins such as the Kanto basin, and the number of such buildings is growing. Recently, Japan Meteorological Agency (JMA) has proposed four classes of long-period ground motion intensity (http://www.data.jma.go.jp/svd/eew/data/ltpgm/) based on absolute velocity response spectra, which correlate to the difficulty of movement of people in tall buildings. As the researchers are using various types of response spectra for long-period ground motions, it is important to understand the relationships between them to take appropriate measures for disaster prevention applications. In this paper, we, therefore, obtain and discuss the empirical attenuation relationships using the same functional forms for the three types of velocity response spectra computed from observed strong motion records from moderate to large earthquakes in relation to JMA magnitude, hypocentral distance, sediment depths, and AVS30 as predictor variables at periods between

  6. Human neural learning depends on reward prediction errors in the blocking paradigm.

    PubMed

    Tobler, Philippe N; O'doherty, John P; Dolan, Raymond J; Schultz, Wolfram

    2006-01-01

    Learning occurs when an outcome deviates from expectation (prediction error). According to formal learning theory, the defining paradigm demonstrating the role of prediction errors in learning is the blocking test. Here, a novel stimulus is blocked from learning when it is associated with a fully predicted outcome, presumably because the occurrence of the outcome fails to produce a prediction error. We investigated the role of prediction errors in human reward-directed learning using a blocking paradigm and measured brain activation with functional magnetic resonance imaging. Participants showed blocking of behavioral learning with juice rewards as predicted by learning theory. The medial orbitofrontal cortex and the ventral putamen showed significantly lower responses to blocked, compared with nonblocked, reward-predicting stimuli. In reward-predicting control situations, deactivation in orbitofrontal cortex and ventral putamen occurred at the time of unpredicted reward omissions. Responses in discrete parts of orbitofrontal cortex correlated with the degree of behavioral learning during, and after, the learning phase. These data suggest that learning in primary reward structures in the human brain correlates with prediction errors in a manner that complies with principles of formal learning theory.

  7. Aircraft noise-induced awakenings are more reasonably predicted from relative than from absolute sound exposure levels.

    PubMed

    Fidell, Sanford; Tabachnick, Barbara; Mestre, Vincent; Fidell, Linda

    2013-11-01

    Assessment of aircraft noise-induced sleep disturbance is problematic for several reasons. Current assessment methods are based on sparse evidence and limited understandings; predictions of awakening prevalence rates based on indoor absolute sound exposure levels (SELs) fail to account for appreciable amounts of variance in dosage-response relationships and are not freely generalizable from airport to airport; and predicted awakening rates do not differ significantly from zero over a wide range of SELs. Even in conjunction with additional predictors, such as time of night and assumed individual differences in "sensitivity to awakening," nominally SEL-based predictions of awakening rates remain of limited utility and are easily misapplied and misinterpreted. Probabilities of awakening are more closely related to SELs scaled in units of standard deviates of local distributions of aircraft SELs, than to absolute sound levels. Self-selection of residential populations for tolerance of nighttime noise and habituation to airport noise environments offer more parsimonious and useful explanations for differences in awakening rates at disparate airports than assumed individual differences in sensitivity to awakening.

  8. 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

  9. Dopamine reward prediction-error signalling: a two-component response.

    PubMed

    Schultz, Wolfram

    2016-03-01

    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

  10. Chain pooling to minimize prediction error in subset regression. [Monte Carlo studies using population models

    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.

  11. Prediction error in reinforcement learning: a meta-analysis of neuroimaging studies.

    PubMed

    Garrison, Jane; Erdeniz, Burak; Done, John

    2013-08-01

    Activation likelihood estimation (ALE) meta-analyses were used to examine the neural correlates of prediction error in reinforcement learning. The findings are interpreted in the light of current computational models of learning and action selection. In this context, particular consideration is given to the comparison of activation patterns from studies using instrumental and Pavlovian conditioning, and where reinforcement involved rewarding or punishing feedback. The striatum was the key brain area encoding for prediction error, with activity encompassing dorsal and ventral regions for instrumental and Pavlovian reinforcement alike, a finding which challenges the functional separation of the striatum into a dorsal 'actor' and a ventral 'critic'. Prediction error activity was further observed in diverse areas of predominantly anterior cerebral cortex including medial prefrontal cortex and anterior cingulate cortex. Distinct patterns of prediction error activity were found for studies using rewarding and aversive reinforcers; reward prediction errors were observed primarily in the striatum while aversive prediction errors were found more widely including insula and habenula.

  12. Prediction error in reinforcement learning: a meta-analysis of neuroimaging studies.

    PubMed

    Garrison, Jane; Erdeniz, Burak; Done, John

    2013-08-01

    Activation likelihood estimation (ALE) meta-analyses were used to examine the neural correlates of prediction error in reinforcement learning. The findings are interpreted in the light of current computational models of learning and action selection. In this context, particular consideration is given to the comparison of activation patterns from studies using instrumental and Pavlovian conditioning, and where reinforcement involved rewarding or punishing feedback. The striatum was the key brain area encoding for prediction error, with activity encompassing dorsal and ventral regions for instrumental and Pavlovian reinforcement alike, a finding which challenges the functional separation of the striatum into a dorsal 'actor' and a ventral 'critic'. Prediction error activity was further observed in diverse areas of predominantly anterior cerebral cortex including medial prefrontal cortex and anterior cingulate cortex. Distinct patterns of prediction error activity were found for studies using rewarding and aversive reinforcers; reward prediction errors were observed primarily in the striatum while aversive prediction errors were found more widely including insula and habenula. PMID:23567522

  13. An MEG signature corresponding to an axiomatic model of reward prediction error.

    PubMed

    Talmi, Deborah; Fuentemilla, Lluis; Litvak, Vladimir; Duzel, Emrah; Dolan, Raymond J

    2012-01-01

    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.

  14. A simple computational principle predicts vocal adaptation dynamics across age and error size

    PubMed Central

    Kelly, Conor W.; Sober, Samuel J.

    2014-01-01

    The brain uses sensory feedback to correct errors in behavior. Songbirds and humans acquire vocal behaviors by imitating the sounds produced by adults and rely on auditory feedback to correct vocal errors throughout their lifetimes. In both birds and humans, acoustic variability decreases steadily with age following the acquisition of vocal behavior. Prior studies in adults have shown that while sensory errors that fall within the limits of vocal variability evoke robust motor corrections, larger errors do not induce learning. Although such results suggest that younger animals, which have greater vocal variability, might correct large errors more readily than older individuals, it is unknown whether age-dependent changes in variability are accompanied by changes in the speed or magnitude of vocal error correction. We tested the hypothesis that auditory errors evoke greater vocal changes in younger animals and that a common computation determines how sensory information drives motor learning across different ages and error sizes. Consistent with our hypothesis, we found that in songbirds the speed and extent of error correction changes dramatically with age and that age-dependent differences in learning were predicted by a model in which the overlap between sensory errors and the distribution of prior sensory feedback determines the dynamics of adaptation. Our results suggest that the brain employs a simple and robust computational principle to calibrate the rate and magnitude of vocal adaptation across age-dependent changes in behavioral performance and in response to different sensory errors. PMID:25324740

  15. Elevated absolute monocyte count predicts unfavorable outcomes in patients with angioimmunoblastic T-cell lymphoma.

    PubMed

    Yang, Yu-Qiong; Liang, Jin-Hua; Wu, Jia-Zhu; Wang, Li; Qu, Xiao-Yan; Cao, Lei; Zhao, Xiao-Li; Huang, Dong-Ping; Fan, Lei; Li, Jian-Yong; Xu, Wei

    2016-03-01

    This study was aimed at investigating the prognostic significance of the absolute monocyte count (AMC) in peripheral blood in patients with newly diagnosed angioimmunoblastic T cell lymphoma (AITL). AMC was performed in 73 therapy-naive patients with AITL in 2 institutions during 2008-2015, and higher AMC was observed in those with extranodal sites >1, bone marrow involvement, high lactate dehydrogenase level, the EBV infection, no response to treatment and high IPI, PIT, PIAI score group. The best AMC cut-off level at diagnosis was 0.8 × 10(9)/L and the 3-year overall survival (OS) was 64% for patients with low AMC group (≤ 0.8 × 10(9)/L) compared to 10% in high AMC group (>0.8 × 10(9)/L) (P<0.001). Multivariate analysis showed that elevated AMC remained an adverse prognostic parameter. Our results suggest that AMC is an independent prognostic parameter for OS in patients with AITL, and AMC >0.8 × 10(9)/L can routinely be used to identify high-risk patients with unfavorable survival.

  16. Fronto-temporal white matter connectivity predicts reversal learning errors

    PubMed Central

    Alm, Kylie H.; Rolheiser, Tyler; Mohamed, Feroze B.; Olson, Ingrid R.

    2015-01-01

    Each day, we make hundreds of decisions. In some instances, these decisions are guided by our innate needs; in other instances they are guided by memory. Probabilistic reversal learning tasks exemplify the close relationship between decision making and memory, as subjects are exposed to repeated pairings of a stimulus choice with a reward or punishment outcome. After stimulus–outcome associations have been learned, the associated reward contingencies are reversed, and participants are not immediately aware of this reversal. Individual differences in the tendency to choose the previously rewarded stimulus reveal differences in the tendency to make poorly considered, inflexible choices. Lesion studies have strongly linked reversal learning performance to the functioning of the orbitofrontal cortex, the hippocampus, and in some instances, the amygdala. Here, we asked whether individual differences in the microstructure of the uncinate fasciculus, a white matter tract that connects anterior and medial temporal lobe regions to the orbitofrontal cortex, predict reversal learning performance. Diffusion tensor imaging and behavioral paradigms were used to examine this relationship in 33 healthy young adults. The results of tractography revealed a significant negative relationship between reversal learning performance and uncinate axial diffusivity, but no such relationship was demonstrated in a control tract, the inferior longitudinal fasciculus. Our findings suggest that the uncinate might serve to integrate associations stored in the anterior and medial temporal lobes with expectations about expected value based on feedback history, computed in the orbitofrontal cortex. PMID:26150776

  17. Glutamatergic Model Psychoses: Prediction Error, Learning, and Inference

    PubMed Central

    Corlett, Philip R; Honey, Garry D; Krystal, John H; Fletcher, Paul C

    2011-01-01

    Modulating glutamatergic neurotransmission induces alterations in conscious experience that mimic the symptoms of early psychotic illness. We review studies that use intravenous administration of ketamine, focusing on interindividual variability in the profundity of the ketamine experience. We will consider this individual variability within a hypothetical model of brain and cognitive function centered upon learning and inference. Within this model, the brains, neural systems, and even single neurons specify expectations about their inputs and responding to violations of those expectations with new learning that renders future inputs more predictable. We argue that ketamine temporarily deranges this ability by perturbing both the ways in which prior expectations are specified and the ways in which expectancy violations are signaled. We suggest that the former effect is predominantly mediated by NMDA blockade and the latter by augmented and inappropriate feedforward glutamatergic signaling. We suggest that the observed interindividual variability emerges from individual differences in neural circuits that normally underpin the learning and inference processes described. The exact source for that variability is uncertain, although it is likely to arise not only from genetic variation but also from subjects' previous experiences and prior learning. Furthermore, we argue that chronic, unlike acute, NMDA blockade alters the specification of expectancies more profoundly and permanently. Scrutinizing individual differences in the effects of acute and chronic ketamine administration in the context of the Bayesian brain model may generate new insights about the symptoms of psychosis; their underlying cognitive processes and neurocircuitry. PMID:20861831

  18. The recent absolute total np and pp cross section determinations: quality of data description and prediction of experimental observables

    SciTech Connect

    Laptev, Alexander B; Haight, Robert C; Arndt, Richard A; Briscoe, William J; Paris, Mark W; Strakovsky, Igor I; Workman, Ron L

    2010-01-01

    The absolute total cross sections for np and pp scattering below 1000 MeV are determined based on partial-wave analyses (PWAs) of nucleon-nucleon scattering data. These cross sections are compared with the most recent ENDF/B-VII.0 and JENDL-3.3 data files, and the Nijmegen PWA. Systematic deviations from the ENDF/B-VII.0 and JENDL-3.3 evaluations are found to exist in the low-energy region. Comparison of the np evaluation with the result of most recent np total and differential cross section measurements will be discussed. Results of those measurements were not used in the evaluation database. A comparison was done to check a quality of evaluation and its capabilities to predict experimental observables. Excellent agreement was found between the new experimental data and our PWA predictions.

  19. The Human Bathtub: Safety and Risk Predictions Including the Dynamic Probability of Operator Errors

    SciTech Connect

    Duffey, Romney B.; Saull, John W.

    2006-07-01

    Reactor safety and risk are dominated by the potential and major contribution for human error in the design, operation, control, management, regulation and maintenance of the plant, and hence to all accidents. Given the possibility of accidents and errors, now we need to determine the outcome (error) probability, or the chance of failure. Conventionally, reliability engineering is associated with the failure rate of components, or systems, or mechanisms, not of human beings in and interacting with a technological system. The probability of failure requires a prior knowledge of the total number of outcomes, which for any predictive purposes we do not know or have. Analysis of failure rates due to human error and the rate of learning allow a new determination of the dynamic human error rate in technological systems, consistent with and derived from the available world data. The basis for the analysis is the 'learning hypothesis' that humans learn from experience, and consequently the accumulated experience defines the failure rate. A new 'best' equation has been derived for the human error, outcome or failure rate, which allows for calculation and prediction of the probability of human error. We also provide comparisons to the empirical Weibull parameter fitting used in and by conventional reliability engineering and probabilistic safety analysis methods. These new analyses show that arbitrary Weibull fitting parameters and typical empirical hazard function techniques cannot be used to predict the dynamics of human errors and outcomes in the presence of learning. Comparisons of these new insights show agreement with human error data from the world's commercial airlines, the two shuttle failures, and from nuclear plant operator actions and transient control behavior observed in transients in both plants and simulators. The results demonstrate that the human error probability (HEP) is dynamic, and that it may be predicted using the learning hypothesis and the minimum

  20. Period, epoch, and prediction errors of ephemerides from continuous sets of timing measurements

    NASA Astrophysics Data System (ADS)

    Deeg, H. J.

    2015-06-01

    Space missions such as Kepler and CoRoT have led to large numbers of eclipse or transit measurements in nearly continuous time series. This paper shows how to obtain the period error in such measurements from a basic linear least-squares fit, and how to correctly derive the timing error in the prediction of future transit or eclipse events. Assuming strict periodicity, a formula for the period error of these time series is derived, σP = σT (12 / (N3-N))1 / 2, where σP is the period error, σT the timing error of a single measurement, and N the number of measurements. Compared to the iterative method for period error estimation by Mighell & Plavchan (2013), this much simpler formula leads to smaller period errors, whose correctness has been verified through simulations. For the prediction of times of future periodic events, usual linear ephemeris were epoch errors are quoted for the first time measurement, are prone to an overestimation of the error of that prediction. This may be avoided by a correction for the duration of the time series. An alternative is the derivation of ephemerides whose reference epoch and epoch error are given for the centre of the time series. For long continuous or near-continuous time series whose acquisition is completed, such central epochs should be the preferred way for the quotation of linear ephemerides. While this work was motivated from the analysis of eclipse timing measures in space-based light curves, it should be applicable to any other problem with an uninterrupted sequence of discrete timings for which the determination of a zero point, of a constant period and of the associated errors is needed.

  1. A Fully Bayesian Approach to Improved Calibration and Prediction of Groundwater Models With Structure Error

    NASA Astrophysics Data System (ADS)

    Xu, T.; Valocchi, A. J.

    2014-12-01

    Effective water resource management typically relies on numerical models to analyse groundwater flow and solute transport processes. These models are usually subject to model structure error due to simplification and/or misrepresentation of the real system. As a result, the model outputs may systematically deviate from measurements, thus violating a key assumption for traditional regression-based calibration and uncertainty analysis. On the other hand, model structure error induced bias can be described statistically in an inductive, data-driven way based on historical model-to-measurement misfit. We adopt a fully Bayesian approach that integrates a Gaussian process error model to account for model structure error to the calibration, prediction and uncertainty analysis of groundwater models. The posterior distributions of parameters of the groundwater model and the Gaussian process error model are jointly inferred using DREAM, an efficient Markov chain Monte Carlo sampler. We test the usefulness of the fully Bayesian approach towards a synthetic case study of surface-ground water interaction under changing pumping conditions. We first illustrate through this example that traditional least squares regression without accounting for model structure error yields biased parameter estimates due to parameter compensation as well as biased predictions. In contrast, the Bayesian approach gives less biased parameter estimates. Moreover, the integration of a Gaussian process error model significantly reduces predictive bias and leads to prediction intervals that are more consistent with observations. The results highlight the importance of explicit treatment of model structure error especially in circumstances where subsequent decision-making and risk analysis require accurate prediction and uncertainty quantification. In addition, the data-driven error modelling approach is capable of extracting more information from observation data than using a groundwater model alone.

  2. Inadvertent interchange of electrocardiogram limb lead connections: analysis of predicted consequences part II: double interconnection errors.

    PubMed

    Rowlands, Derek J

    2012-01-01

    Limb lead connection errors are known to be very common in clinical practice. The consequences of all possible single limb lead interconnection errors were analyzed in an earlier publication (J Electrocardiology 2008;41:84-90). With a single limb lead interconnection error, 6 combinations of limb lead connections are possible. Two of these combinations give rise to records in which the limb lead morphology is uninterpretable. Such records show a "flat line" in lead II or III. Three of the errors give rise to records that are fully interpretable once the specific interconnection error has been identified (although one of the errors cannot reliably be recognized in the absence of a previous record for comparison). One of the errors produces no change in the electrocardiogram recording. In all cases, the precordial leads are interpretable, although there are very minor changes in the voltages. This communication predicts the changes in limb lead appearances consequent upon all possible double limb lead interchanges and illustrates these with records electively taken with such double interconnection errors. There are only 3 possible double limb lead interconnection errors. In 2 of the possible combinations, interpretation of the limb leads is impossible, and each of these errors gives rise to a flat line in lead I. In the third combination, the record is fully interpretable once the abnormality has been identified. In all 3 types, the precordial leads are interpretable, although there are very minor changes in the voltages.

  3. Improving the accuracy of computed 13C NMR shift predictions by specific environment error correction: fragment referencing.

    PubMed

    Andrews, Keith G; Spivey, Alan C

    2013-11-15

    The accuracy of both Gauge-including atomic orbital (GIAO) and continuous set of gauge transformations (CSGT) (13)C NMR spectra prediction by Density Functional Theory (DFT) at the B3LYP/6-31G** level is shown to be usefully enhanced by employing a 'fragment referencing' method for predicting chemical shifts without recourse to empirical scaling. Fragment referencing refers to a process of reducing the error in calculating a particular NMR shift by consulting a similar molecule for which the error in the calculation is easily deduced. The absolute accuracy of the chemical shifts predicted when employing fragment referencing relative to conventional techniques (e.g., using TMS or MeOH/benzene dual referencing) is demonstrated to be improved significantly for a range of substrates, which illustrates the superiority of the technique particularly for systems with similar chemical shifts arising from different chemical environments. The technique is particularly suited to molecules of relatively low molecular weight containing 'non-standard' magnetic environments, e.g., α to halogen atoms, which are poorly predicted by other methods. The simplicity and speed of the technique mean that it can be employed to resolve routine structural assignment problems that require a degree of accuracy not provided by standard incremental or hierarchically ordered spherical description of environment (HOSE) algorithms. The approach is also demonstrated to be applicable when employing the MP2 method at 6-31G**, cc-pVDZ, aug-cc-pVDZ, and cc-pVTZ levels, although none of these offer advantage in terms of accuracy of prediction over the B3LYP/6-31G** DFT method.

  4. Medial–Frontal Stimulation Enhances Learning in Schizophrenia by Restoring Prediction Error Signaling

    PubMed Central

    Reinhart, Robert M.G.; Zhu, Julia

    2015-01-01

    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. SIGNIFICANCE STATEMENT 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. PMID:26338333

  5. Drivers of coupled model ENSO error dynamics and the spring predictability barrier

    NASA Astrophysics Data System (ADS)

    Larson, Sarah M.; Kirtman, Ben P.

    2016-07-01

    Despite recent improvements in ENSO simulations, ENSO predictions ultimately remain limited by error growth and model inadequacies. Determining the accompanying dynamical processes that drive the growth of certain types of errors may help the community better recognize which error sources provide an intrinsic limit to predictability. This study applies a dynamical analysis to previously developed CCSM4 error ensemble experiments that have been used to model noise-driven error growth. Analysis reveals that ENSO-independent error growth is instigated via a coupled instability mechanism. Daily error fields indicate that persistent stochastic zonal wind stress perturbations (τx^' } ) near the equatorial dateline activate the coupled instability, first driving local SST and anomalous zonal current changes that then induce upwelling anomalies and a clear thermocline response. In particular, March presents a window of opportunity for stochastic τx^' } to impose a lasting influence on the evolution of eastern Pacific SST through December, suggesting that stochastic τx^' } is an important contributor to the spring predictability barrier. Stochastic winds occurring in other months only temporarily affect eastern Pacific SST for 2-3 months. Comparison of a control simulation with an ENSO cycle and the ENSO-independent error ensemble experiments reveals that once the instability is initiated, the subsequent error growth is modulated via an ENSO-like mechanism, namely the seasonal strength of the Bjerknes feedback. Furthermore, unlike ENSO events that exhibit growth through the fall, the growth of ENSO-independent SST errors terminates once the seasonal strength of the Bjerknes feedback weakens in fall. Results imply that the heat content supplied by the subsurface precursor preceding the onset of an ENSO event is paramount to maintaining the growth of the instability (or event) through fall.

  6. High capacity reversible watermarking for audio by histogram shifting and predicted error expansion.

    PubMed

    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.

  7. High Capacity Reversible Watermarking for Audio by Histogram Shifting and Predicted Error Expansion

    PubMed Central

    Wang, Fei; 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. PMID:25097883

  8. Quantifying the Effect of Lidar Turbulence Error on Wind Power Prediction

    SciTech Connect

    Newman, Jennifer F.; Clifton, Andrew

    2016-01-01

    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 of 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

  9. How we learn to make decisions: rapid propagation of reinforcement learning prediction errors in humans.

    PubMed

    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

  10. How we learn to make decisions: rapid propagation of reinforcement learning prediction errors in humans.

    PubMed

    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

  11. Advanced error-prediction LDPC with temperature compensation for highly reliable SSDs

    NASA Astrophysics Data System (ADS)

    Tokutomi, Tsukasa; Tanakamaru, Shuhei; Iwasaki, Tomoko Ogura; Takeuchi, Ken

    2015-09-01

    To improve the reliability of NAND Flash memory based solid-state drives (SSDs), error-prediction LDPC (EP-LDPC) has been proposed for multi-level-cell (MLC) NAND Flash memory (Tanakamaru et al., 2012, 2013), which is effective for long retention times. However, EP-LDPC is not as effective for triple-level cell (TLC) NAND Flash memory, because TLC NAND Flash has higher error rates and is more sensitive to program-disturb error. Therefore, advanced error-prediction LDPC (AEP-LDPC) has been proposed for TLC NAND Flash memory (Tokutomi et al., 2014). AEP-LDPC can correct errors more accurately by precisely describing the error phenomena. In this paper, the effects of AEP-LDPC are investigated in a 2×nm TLC NAND Flash memory with temperature characterization. Compared with LDPC-with-BER-only, the SSD's data-retention time is increased by 3.4× and 9.5× at room-temperature (RT) and 85 °C, respectively. Similarly, the acceptable BER is increased by 1.8× and 2.3×, respectively. Moreover, AEP-LDPC can correct errors with pre-determined tables made at higher temperatures to shorten the measurement time before shipping. Furthermore, it is found that one table can cover behavior over a range of temperatures in AEP-LDPC. As a result, the total table size can be reduced to 777 kBytes, which makes this approach more practical.

  12. Lossless compression of hyperspectral images based on the prediction error block

    NASA Astrophysics Data System (ADS)

    Li, Yongjun; Li, Yunsong; Song, Juan; Liu, Weijia; Li, Jiaojiao

    2016-05-01

    A lossless compression algorithm of hyperspectral image based on distributed source coding is proposed, which is used to compress the spaceborne hyperspectral data effectively. In order to make full use of the intra-frame correlation and inter-frame correlation, the prediction error block scheme are introduced. Compared with the scalar coset based distributed compression method (s-DSC) proposed by E.Magli et al., that is , the bitrate of the whole block is determined by its maximum prediction error, and the s-DSC-classify scheme proposed by Song Juan that is based on classification and coset coding, the prediction error block scheme could reduce the bitrate efficiently. Experimental results on hyperspectral images show that the proposed scheme can offer both high compression performance and low encoder complexity and decoder complexity, which is available for on-board compression of hyperspectral images.

  13. Ventral-striatal/nucleus-accumbens sensitivity to prediction errors during classification learning.

    PubMed

    Rodriguez, P F; Aron, A R; Poldrack, R A

    2006-04-01

    A prominent theory in neuroscience suggests reward learning is driven by the discrepancy between a subject's expectation of an outcome and the actual outcome itself. Furthermore, it is postulated that midbrain dopamine neurons relay this mismatch to target regions including the ventral striatum. Using functional MRI (fMRI), we tested striatal responses to prediction errors for probabilistic classification learning with purely cognitive feedback. We used a version of the Rescorla-Wagner model to generate prediction errors for each subject and then entered these in a parametric analysis of fMRI activity. Activation in ventral striatum/nucleus-accumbens (Nacc) increased parametrically with prediction error for negative feedback. This result extends recent neuroimaging findings in reward learning by showing that learning with cognitive feedback also depends on the same circuitry and dopaminergic signaling mechanisms.

  14. Auditory Mismatch Negativity and Repetition Suppression Deficits in Schizophrenia Explained by Irregular Computation of Prediction Error

    PubMed Central

    Rentzsch, Johannes; Shen, Christina; Jockers-Scherübl, Maria C.; Gallinat, Jürgen; Neuhaus, Andres H.

    2015-01-01

    Background The predictive coding model is rapidly gaining attention in schizophrenia research. It posits the neuronal computation of residual variance (‘prediction error’) between sensory information and top-down expectation through multiple hierarchical levels. Event-related potentials (ERP) reflect cortical processing stages that are increasingly interpreted in the light of the predictive coding hypothesis. Both mismatch negativity (MMN) and repetition suppression (RS) measures are considered a prediction error correlates based on error detection and error minimization, respectively. Methods Twenty-five schizophrenia patients and 25 healthy controls completed auditory tasks designed to elicit MMN and RS responses that were investigated using repeated measures models and strong spatio-temporal a priori hypothesis based on previous research. Separate correlations were performed for controls and schizophrenia patients, using age and clinical variables as covariates. Results MMN and RS deficits were largely replicated in our sample of schizophrenia patients. Moreover, MMN and RS measures were strongly correlated in healthy controls, while no correlation was found in schizophrenia patients. Single-trial analyses indicated significantly lower signal-to-noise ratio during prediction error computation in schizophrenia. Conclusions This study provides evidence that auditory ERP components relevant for schizophrenia research can be reconciled in the light of the predictive coding framework. The lack of any correlation between the investigated measures in schizophrenia patients suggests a disruption of predictive coding mechanisms in general. More specifically, these results suggest that schizophrenia is associated with an irregular computation of residual variance between sensory input and top-down models, i.e. prediction error. PMID:25955846

  15. Quantitative Vapor-phase IR Intensities and DFT Computations to Predict Absolute IR Spectra based on Molecular Structure: I. Alkanes

    SciTech Connect

    Williams, Stephen D.; Johnson, Timothy J.; Sharpe, Steven W.; Yavelak, Veronica; Oats, R. P.; Brauer, Carolyn S.

    2013-11-13

    Recently recorded quantitative IR spectra of a variety of gas-phase alkanes are shown to have integrated intensities in both the C-H stretching and C-H bending regions that depend linearly on the molecular size, i.e. the number of C-H bonds. This result is well predicted from CH4 to C15H32 by DFT computations of IR spectra at the B3LYP/6-31+G(d,p) level of DFT theory. A simple model predicting the absolute IR band intensities of alkanes based only on structural formula is proposed: For the C-H stretching band near 2930 cm-1 this is given by (in km/mol): CH¬_str = (34±3)*CH – (41±60) where CH is number of C-H bonds in the alkane. The linearity is explained in terms of coordinated motion of methylene groups rather than the summed intensities of autonomous -CH2- units. The effect of alkyl chain length on the intensity of a C-H bending mode is explored and interpreted in terms of conformer distribution. The relative intensity contribution of a methyl mode compared to the total C-H stretch intensity is shown to be linear in the number of terminal methyl groups in the alkane, and can be used to predict quantitative spectra a priori based on structure alone.

  16. Midbrain dopamine neurons compute inferred and cached value prediction errors in a common framework

    PubMed Central

    Sadacca, Brian F; Jones, Joshua L; Schoenbaum, Geoffrey

    2016-01-01

    Midbrain dopamine neurons have been proposed to signal reward prediction errors as defined in temporal difference (TD) learning algorithms. While these models have been extremely powerful in interpreting dopamine activity, they typically do not use value derived through inference in computing errors. This is important because much real world behavior – and thus many opportunities for error-driven learning – is based on such predictions. Here, we show that error-signaling rat dopamine neurons respond to the inferred, model-based value of cues that have not been paired with reward and do so in the same framework as they track the putative cached value of cues previously paired with reward. This suggests that dopamine neurons access a wider variety of information than contemplated by standard TD models and that, while their firing conforms to predictions of TD models in some cases, they may not be restricted to signaling errors from TD predictions. DOI: http://dx.doi.org/10.7554/eLife.13665.001 PMID:26949249

  17. Cognitive strategies regulate fictive, but not reward prediction error signals in a sequential investment task.

    PubMed

    Gu, Xiaosi; Kirk, Ulrich; Lohrenz, Terry M; Montague, P Read

    2014-08-01

    Computational models of reward processing suggest that foregone or fictive outcomes serve as important information sources for learning and augment those generated by experienced rewards (e.g. reward prediction errors). An outstanding question is how these learning signals interact with top-down cognitive influences, such as cognitive reappraisal strategies. Using a sequential investment task and functional magnetic resonance imaging, we show that the reappraisal strategy selectively attenuates the influence of fictive, but not reward prediction error signals on investment behavior; such behavioral effect is accompanied by changes in neural activity and connectivity in the anterior insular cortex, a brain region thought to integrate subjective feelings with high-order cognition. Furthermore, individuals differ in the extent to which their behaviors are driven by fictive errors versus reward prediction errors, and the reappraisal strategy interacts with such individual differences; a finding also accompanied by distinct underlying neural mechanisms. These findings suggest that the variable interaction of cognitive strategies with two important classes of computational learning signals (fictive, reward prediction error) represent one contributing substrate for the variable capacity of individuals to control their behavior based on foregone rewards. These findings also expose important possibilities for understanding the lack of control in addiction based on possibly foregone rewarding outcomes.

  18. Radar prediction of absolute rain fade distributions for earth-satellite paths and general methods for extrapolation of fade statistics to other locations

    NASA Technical Reports Server (NTRS)

    Goldhirsh, J.

    1982-01-01

    The first absolute rain fade distribution method described establishes absolute fade statistics at a given site by means of a sampled radar data base. The second method extrapolates absolute fade statistics from one location to another, given simultaneously measured fade and rain rate statistics at the former. Both methods employ similar conditional fade statistic concepts and long term rain rate distributions. Probability deviations in the 2-19% range, with an 11% average, were obtained upon comparison of measured and predicted levels at given attenuations. The extrapolation of fade distributions to other locations at 28 GHz showed very good agreement with measured data at three sites located in the continental temperate region.

  19. Predicting Human Error in Air Traffic Control Decision Support Tools and Free Flight Concepts

    NASA Technical Reports Server (NTRS)

    Mogford, Richard; Kopardekar, Parimal

    2001-01-01

    The document is a set of briefing slides summarizing the work the Advanced Air Transportation Technologies (AATT) Project is doing on predicting air traffic controller and airline pilot human error when using new decision support software tools and when involved in testing new air traffic control concepts. Previous work in this area is reviewed as well as research being done jointly with the FAA. Plans for error prediction work in the AATT Project are discussed. The audience is human factors researchers and aviation psychologists from government and industry.

  20. Neural Activities Underlying the Feedback Express Salience Prediction Errors for Appetitive and Aversive Stimuli

    PubMed Central

    Gu, Yan; Hu, Xueping; Pan, Weigang; Yang, Chun; Wang, Lijun; Li, Yiyuan; Chen, Antao

    2016-01-01

    Feedback information is essential for us to adapt appropriately to the environment. The feedback-related negativity (FRN), a frontocentral negative deflection after the delivery of feedback, has been found to be larger for outcomes that are worse than expected, and it reflects a reward prediction error derived from the midbrain dopaminergic projections to the anterior cingulate cortex (ACC), as stated in reinforcement learning theory. In contrast, the prediction of response-outcome (PRO) model claims that the neural activity in the mediofrontal cortex (mPFC), especially the ACC, is sensitive to the violation of expectancy, irrespective of the valence of feedback. Additionally, increasing evidence has demonstrated significant activities in the striatum, anterior insula and occipital lobe for unexpected outcomes independently of their valence. Thus, the neural mechanism of the feedback remains under dispute. Here, we investigated the feedback with monetary reward and electrical pain shock in one task via functional magnetic resonance imaging. The results revealed significant prediction-error-related activities in the bilateral fusiform gyrus, right middle frontal gyrus and left cingulate gyrus for both money and pain. This implies that some regions underlying the feedback may signal a salience prediction error rather than a reward prediction error. PMID:27694920

  1. Absolute Summ

    NASA Astrophysics Data System (ADS)

    Phillips, Alfred, Jr.

    Summ means the entirety of the multiverse. It seems clear, from the inflation theories of A. Guth and others, that the creation of many universes is plausible. We argue that Absolute cosmological ideas, not unlike those of I. Newton, may be consistent with dynamic multiverse creations. As suggested in W. Heisenberg's uncertainty principle, and with the Anthropic Principle defended by S. Hawking, et al., human consciousness, buttressed by findings of neuroscience, may have to be considered in our models. Predictability, as A. Einstein realized with Invariants and General Relativity, may be required for new ideas to be part of physics. We present here a two postulate model geared to an Absolute Summ. The seedbed of this work is part of Akhnaton's philosophy (see S. Freud, Moses and Monotheism). Most important, however, is that the structure of human consciousness, manifest in Kenya's Rift Valley 200,000 years ago as Homo sapiens, who were the culmination of the six million year co-creation process of Hominins and Nature in Africa, allows us to do the physics that we do. .

  2. Surprise signals in the supplementary eye field: rectified prediction errors drive exploration-exploitation transitions.

    PubMed

    Kawaguchi, Norihiko; Sakamoto, Kazuhiro; Saito, Naohiro; Furusawa, Yoshito; Tanji, Jun; Aoki, Masashi; Mushiake, Hajime

    2015-02-01

    Visual search is coordinated adaptively by monitoring and predicting the environment. The supplementary eye field (SEF) plays a role in oculomotor control and outcome evaluation. However, it is not clear whether the SEF is involved in adjusting behavioral modes based on preceding feedback. We hypothesized that the SEF drives exploration-exploitation transitions by generating "surprise signals" or rectified prediction errors, which reflect differences between predicted and actual outcomes. To test this hypothesis, we introduced an oculomotor two-target search task in which monkeys were required to find two valid targets among four identical stimuli. After they detected the valid targets, they exploited their knowledge of target locations to obtain a reward by choosing the two valid targets alternately. Behavioral analysis revealed two distinct types of oculomotor search patterns: exploration and exploitation. We found that two types of SEF neurons represented the surprise signals. The error-surprise neurons showed enhanced activity when the monkey received the first error feedback after the target pair change, and this activity was followed by an exploratory oculomotor search pattern. The correct-surprise neurons showed enhanced activity when the monkey received the first correct feedback after an error trial, and this increased activity was followed by an exploitative, fixed-type search pattern. Our findings suggest that error-surprise neurons are involved in the transition from exploitation to exploration and that correct-surprise neurons are involved in the transition from exploration to exploitation.

  3. Lateral habenula neurons signal errors in the prediction of reward information.

    PubMed

    Bromberg-Martin, Ethan S; Hikosaka, Okihide

    2011-08-21

    Humans and animals have the ability to predict future events, which they cultivate by continuously searching their environment for sources of predictive information. However, little is known about the neural systems that motivate this behavior. We hypothesized that information-seeking is assigned value by the same circuits that support reward-seeking, such that neural signals encoding reward prediction errors (RPEs) include analogous information prediction errors (IPEs). To test this, we recorded from neurons in the lateral habenula, a nucleus that encodes RPEs, while monkeys chose between cues that provided different chances to view information about upcoming rewards. We found that a subpopulation of lateral habenula neurons transmitted signals resembling IPEs, responding when reward information was unexpectedly cued, delivered or denied. These signals evaluated information sources reliably, even when the monkey's decisions did not. These neurons could provide a common instructive signal for reward-seeking and information-seeking behavior.

  4. Errors in reward prediction are reflected in the event-related brain potential.

    PubMed

    Holroyd, Clay B; Nieuwenhuis, Sander; Yeung, Nick; Cohen, Jonathan D

    2003-12-19

    The error-related negativity (ERN) is a negative deflection in the event-related brain potential associated with error processing. A recent theory holds that the ERN is elicited by the impact of a reward prediction error signal carried by the mesencephalic dopamine system on anterior cingulate cortex. The theory predicts that larger ERNs should be elicited by unexpected unfavorable outcomes than by expected unfavorable outcomes. We tested the theory in an experiment in which the frequency of occurrence of reward was varied by condition, reasoning that the system that produces the ERN would come to expect non-reward when rewards were infrequent. Consistent with the theory, we found that larger ERNs were elicited by unexpected absences of reward.

  5. A New Local Modelling Approach Based on Predicted Errors for Near-Infrared Spectral Analysis.

    PubMed

    Chang, Haitao; Zhu, Lianqing; Lou, Xiaoping; Meng, Xiaochen; Guo, Yangkuan; Wang, Zhongyu

    2016-01-01

    Over the last decade, near-infrared spectroscopy, together with the use of chemometrics models, has been widely employed as an analytical tool in several industries. However, most chemical processes or analytes are multivariate and nonlinear in nature. To solve this problem, local errors regression method is presented in order to build an accurate calibration model in this paper, where a calibration subset is selected by a new similarity criterion which takes the full information of spectra, chemical property, and predicted errors. After the selection of calibration subset, the partial least squares regression is applied to build calibration model. The performance of the proposed method is demonstrated through a near-infrared spectroscopy dataset of pharmaceutical tablets. Compared with other local strategies with different similarity criterions, it has been shown that the proposed local errors regression can result in a significant improvement in terms of both prediction ability and calculation speed. PMID:27446631

  6. A New Local Modelling Approach Based on Predicted Errors for Near-Infrared Spectral Analysis

    PubMed Central

    Chang, Haitao; Lou, Xiaoping; Meng, Xiaochen; Guo, Yangkuan; Wang, Zhongyu

    2016-01-01

    Over the last decade, near-infrared spectroscopy, together with the use of chemometrics models, has been widely employed as an analytical tool in several industries. However, most chemical processes or analytes are multivariate and nonlinear in nature. To solve this problem, local errors regression method is presented in order to build an accurate calibration model in this paper, where a calibration subset is selected by a new similarity criterion which takes the full information of spectra, chemical property, and predicted errors. After the selection of calibration subset, the partial least squares regression is applied to build calibration model. The performance of the proposed method is demonstrated through a near-infrared spectroscopy dataset of pharmaceutical tablets. Compared with other local strategies with different similarity criterions, it has been shown that the proposed local errors regression can result in a significant improvement in terms of both prediction ability and calculation speed. PMID:27446631

  7. Dopamine Neurons, Input Integration, and Reward Prediction Errors: E Pluribus Unum.

    PubMed

    Floresco, Stan B

    2016-09-21

    Dopamine neurons encode errors in reward prediction, yet understanding how they integrate information from different subcortical inputs to generate these signals has remained elusive. In this issue of Neuron, Tian et al. (2016) shed new light onto these underlying mechanisms. PMID:27657447

  8. Error-rate prediction for programmable circuits: methodology, tools and studied cases

    NASA Astrophysics Data System (ADS)

    Velazco, Raoul

    2013-05-01

    This work presents an approach to predict the error rates due to Single Event Upsets (SEU) occurring in programmable circuits as a consequence of the impact or energetic particles present in the environment the circuits operate. For a chosen application, the error-rate is predicted by combining the results obtained from radiation ground testing and the results of fault injection campaigns performed off-beam during which huge numbers of SEUs are injected during the execution of the studied application. The goal of this strategy is to obtain accurate results about different applications' error rates, without using particle accelerator facilities, thus significantly reducing the cost of the sensitivity evaluation. As a case study, this methodology was applied a complex processor, the Power PC 7448 executing a program issued from a real space application and a crypto-processor application implemented in an SRAM-based FPGA and accepted to be embedded in the payload of a scientific satellite of NASA. The accuracy of predicted error rates was confirmed by comparing, for the same circuit and application, predictions with measures issued from radiation ground testing performed at the cyclotron Cyclone cyclotron of HIF (Heavy Ion Facility) of Louvain-la-Neuve (Belgium).

  9. Prediction Error Demarcates the Transition from Retrieval, to Reconsolidation, to New Learning

    ERIC Educational Resources Information Center

    Sevenster, Dieuwke; Beckers, Tom; Kindt, Merel

    2014-01-01

    Although disrupting reconsolidation is promising in targeting emotional memories, the conditions under which memory becomes labile are still unclear. The current study showed that post-retrieval changes in expectancy as an index for prediction error may serve as a read-out for the underlying processes engaged by memory reactivation. Minor…

  10. Wavelet based error correction and predictive uncertainty of a hydrological forecasting system

    NASA Astrophysics Data System (ADS)

    Bogner, Konrad; Pappenberger, Florian; Thielen, Jutta; de Roo, Ad

    2010-05-01

    River discharge predictions most often show errors with scaling properties of unknown source and statistical structure that degrade the quality of forecasts. This is especially true for lead-time ranges greater then a few days. Since the European Flood Alert System (EFAS) provides discharge forecasts up to ten days ahead, it is necessary to take these scaling properties into consideration. For example the range of scales for the error that occurs at the spring time will be caused by long lasting snowmelt processes, and is by far larger then the error, that appears during the summer period and is caused by convective rain fields of short duration. The wavelet decomposition is an excellent way to provide the detailed model error at different levels in order to estimate the (unobserved) state variables more precisely. A Vector-AutoRegressive model with eXogenous input (VARX) is fitted for the different levels of wavelet decomposition simultaneously and after predicting the next time steps ahead for each scale, a reconstruction formula is applied to transform the predictions in the wavelet domain back to the original time domain. The Bayesian Uncertainty Processor (BUP) developed by Krzysztofowicz is an efficient method to estimate the full predictive uncertainty, which is derived by integrating the hydrological model uncertainty and the meteorological input uncertainty. A hydrological uncertainty processor has been applied to the error corrected discharge series at first in order to derive the predictive conditional distribution under the hypothesis that there is no input uncertainty. The uncertainty of the forecasted meteorological input forcing the hydrological model is derived from the combination of deterministic weather forecasts and ensemble predictions systems (EPS) and the Input Processor maps this input uncertainty into the output uncertainty under the hypothesis that there is no hydrological uncertainty. The main objective of this Bayesian forecasting system

  11. Neural predictive error signal correlates with depressive illness severity in a game paradigm.

    PubMed

    Steele, J D; Meyer, M; Ebmeier, K P

    2004-09-01

    Considerable experimental evidence supports the existence of predictive error signals in various brain regions during associative learning in animals and humans. These regions include the prefrontal cortex, temporal lobe, cerebellum and monoamine systems. Various quantitative theories have been developed to describe behaviour during learning, including Rescorla-Wagner, Temporal Difference and Kalman filter models. These theories may also account for neural error signals. Reviews of imaging studies of depressive illness have consistently implicated the prefrontal and temporal lobes as having abnormal function, and sometimes structure, whilst the monoamine systems are directly influenced by antidepressant medication. It was hypothesised that such abnormalities may be associated with a dysfunction of associative learning that would be reflected by different predictive error signals in depressed patients when compared with healthy controls. This was tested with 30 subjects, 15 with a major depressive illness, using a gambling paradigm and fMRI. Consistent with the hypothesis, depressed patients differed from controls in having an increased error signal. Additionally, for some brain regions, the magnitude of the error signal correlated with Hamilton depression rating of illness severity. Structural equation modelling was used to investigate hypothesised change in effective connectivity between prespecified regions of interest in the limbic and paralimbic system. Again, differences were found that in some cases correlated with illness severity. These results are discussed in the context of quantitative theories of brain function, clinical features of depressive illness and treatments. PMID:15325374

  12. A dynamic model to predict modulation sidebands of a planetary gear set having manufacturing errors

    NASA Astrophysics Data System (ADS)

    Inalpolat, Murat; Kahraman, Ahmet

    2010-02-01

    In this study, a nonlinear time-varying dynamic model is proposed to predict modulation sidebands of planetary gear sets. This discrete dynamic model includes periodically time-varying gear mesh stiffnesses and the nonlinearities associated with tooth separations. The model uses forms of gear mesh interface excitations that are amplitude and frequency modulated due to a class of gear manufacturing errors to predict dynamic forces at all sun-planet and ring-planet gear meshes. The predicted gear mesh force spectra are shown to exhibit well-defined modulation sidebands at frequencies associated with the rotational speeds of gears relative to the planet carrier. This model is further combined with a previously developed model that accounts for amplitude modulations due to rotation of the carrier to predict acceleration spectra at a fixed position in the planetary transmission housing. Individual contributions of each gear error in the form of amplitude and frequency modulations are illustrated through an example analysis. Comparisons are made to measured spectra to demonstrate the capability of the model in predicting the sidebands of a planetary gear set with gear manufacturing errors and a rotating carrier.

  13. Predicting diagnostic error in radiology via eye-tracking and image analytics: Preliminary investigation in mammography

    SciTech Connect

    Voisin, Sophie; Tourassi, Georgia D.; Pinto, Frank; Morin-Ducote, Garnetta; Hudson, Kathleen B.

    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 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: 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.

  14. Predicting diagnostic error in Radiology via eye-tracking and image analytics: Application in mammography

    SciTech Connect

    Voisin, Sophie; Pinto, Frank M; Morin-Ducote, Garnetta; Hudson, Kathy; Tourassi, Georgia

    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 BIRADs 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.

  15. A Conceptual Framework for Predicting Error in Complex Human-Machine Environments

    NASA Technical Reports Server (NTRS)

    Freed, Michael; Remington, Roger; Null, Cynthia H. (Technical Monitor)

    1998-01-01

    We present a Goals, Operators, Methods, and Selection Rules-Model Human Processor (GOMS-MHP) style model-based approach to the problem of predicting human habit capture errors. Habit captures occur when the model fails to allocate limited cognitive resources to retrieve task-relevant information from memory. Lacking the unretrieved information, decision mechanisms act in accordance with implicit default assumptions, resulting in error when relied upon assumptions prove incorrect. The model helps interface designers identify situations in which such failures are especially likely.

  16. Per-beam, planar IMRT QA passing rates do not predict clinically relevant patient dose errors

    SciTech Connect

    Nelms, Benjamin E.; Zhen Heming; Tome, Wolfgang A.

    2011-02-15

    Purpose: The purpose of this work is to determine the statistical correlation between per-beam, planar IMRT QA passing rates and several clinically relevant, anatomy-based dose errors for per-patient IMRT QA. The intent is to assess the predictive power of a common conventional IMRT QA performance metric, the Gamma passing rate per beam. Methods: Ninety-six unique data sets were created by inducing four types of dose errors in 24 clinical head and neck IMRT plans, each planned with 6 MV Varian 120-leaf MLC linear accelerators using a commercial treatment planning system and step-and-shoot delivery. The error-free beams/plans were used as ''simulated measurements'' (for generating the IMRT QA dose planes and the anatomy dose metrics) to compare to the corresponding data calculated by the error-induced plans. The degree of the induced errors was tuned to mimic IMRT QA passing rates that are commonly achieved using conventional methods. Results: Analysis of clinical metrics (parotid mean doses, spinal cord max and D1cc, CTV D95, and larynx mean) vs IMRT QA Gamma analysis (3%/3 mm, 2/2, 1/1) showed that in all cases, there were only weak to moderate correlations (range of Pearson's r-values: -0.295 to 0.653). Moreover, the moderate correlations actually had positive Pearson's r-values (i.e., clinically relevant metric differences increased with increasing IMRT QA passing rate), indicating that some of the largest anatomy-based dose differences occurred in the cases of high IMRT QA passing rates, which may be called ''false negatives.'' The results also show numerous instances of false positives or cases where low IMRT QA passing rates do not imply large errors in anatomy dose metrics. In none of the cases was there correlation consistent with high predictive power of planar IMRT passing rates, i.e., in none of the cases did high IMRT QA Gamma passing rates predict low errors in anatomy dose metrics or vice versa. Conclusions: There is a lack of correlation between

  17. Peripheral blood absolute lymphocyte/monocyte ratio recovery during ABVD treatment cycles predicts clinical outcomes in classical Hodgkin lymphoma.

    PubMed

    Porrata, L F; Ristow, K M; Habermann, T M; Macon, W R; Witzig, T E; Colgan, J P; Inwards, D J; Ansell, S M; Micallef, I N; Johnston, P B; Nowakowski, G; Thompson, C A; Markovic, S N

    2013-01-01

    The peripheral blood absolute lymphocyte/monocyte count ratio at diagnosis (ALC/AMC-DX) predicts survival in classical Hodgkin lymphoma (cHL). However, a limitation of the ALC/AMC-DX is the inability to assess sequentially the host/tumor interaction during treatment. Therefore, we retrospectively examined the ALC/AMC ratio, as a surrogate marker of host immunity (ALC) and tumor microenvironment (AMC), at each adriamycin, bleomycin, vinblastine and dacarbazine treatment cycle as a predictor for clinical outcomes. From 1990 until 2008, 190 cHL patients were diagnosed, treated and followed at Mayo Clinic Rochester and qualified for the study. The ALC/AMC ratio at each treatment cycle was a predictor for overall survival (OS) and progression-free survival (PFS). An ALC/AMC ratio 1.1 versus ALC/AMC <1.1 during treatment cycles was an independent predictor for OS (hazard ratio (HR)=0.14; 95% confidence interval (CI): 0.04-0.40; P<0.0002) and for PFS (HR=0.19; 95% CI: 0.05-0.82; P<0.03). The ALC/AMC ratio during treatment cycles is a predictor for survival and provides a platform to develop therapeutic modalities to manipulate the ALC/AMC ratio during chemotherapy to improve clinical outcomes in cHL.

  18. Neurophysiology of Reward-Guided Behavior: Correlates Related to Predictions, Value, Motivation, Errors, Attention, and Action.

    PubMed

    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. PMID:26276036

  19. Effects of optimal initial errors on predicting the seasonal reduction of the upstream Kuroshio transport

    NASA Astrophysics Data System (ADS)

    Zhang, Kun; Wang, Qiang; Mu, Mu; Liang, Peng

    2016-10-01

    With the Regional Ocean Modeling System (ROMS), we realistically simulated the transport variations of the upstream Kuroshio (referring to the Kuroshio from its origin to the south of Taiwan), particularly for the seasonal transport reduction. Then, we investigated the effects of the optimal initial errors estimated by the conditional nonlinear optimal perturbation (CNOP) approach on predicting the seasonal transport reduction. Two transport reduction events (denoted as Event 1 and Event 2) were chosen, and CNOP1 and CNOP2 were obtained for each event. By examining the spatial structures of the two types of CNOPs, we found that the dominant amplitudes are located around (128°E, 17°N) horizontally and in the upper 1000 m vertically. For each event, the two CNOPs caused large prediction errors. Specifically, at the prediction time, CNOP1 (CNOP2) develops into an anticyclonic (cyclonic) eddy-like structure centered around 124°E, leading to the increase (decrease) of the upstream Kuroshio transport. By investigating the time evolution of the CNOPs in Event 1, we found that the eddy-like structures originating from east of Luzon gradually grow and simultaneously propagate westward. The eddy-energetic analysis indicated that the errors obtain energy from the background state through barotropic and baroclinic instabilities and that the latter plays a more important role. These results suggest that improving the initial conditions in east of Luzon could lead to better prediction of the upstream Kuroshio transport variation.

  20. Neurophysiology of Reward-Guided Behavior: Correlates Related to Predictions, Value, Motivation, Errors, Attention, and Action.

    PubMed

    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.

  1. Dopamine prediction errors in reward learning and addiction: from theory to neural circuitry

    PubMed Central

    Keiflin, Ronald; Janak, Patricia H.

    2015-01-01

    Summary Midbrain dopamine (DA) neurons are proposed to signal reward prediction error (RPE), a fundamental parameter in associative learning models. This RPE hypothesis provides a compelling theoretical framework for understanding DA function in reward learning and addiction. New studies support a causal role for DA-mediated RPE activity in promoting learning about natural reward; however, this question has not been explicitly tested in the context of drug addiction. In this review, we integrate theoretical models with experimental findings on the activity of DA systems, and on the causal role of specific neuronal projections and cell types, to provide a circuit-based framework for probing DA-RPE function in addiction. By examining error-encoding DA neurons in the neural network in which they are embedded, hypotheses regarding circuit-level adaptations that possibly contribute to pathological error-signaling and addiction can be formulated and tested. PMID:26494275

  2. Dopamine Prediction Errors in Reward Learning and Addiction: From Theory to Neural Circuitry.

    PubMed

    Keiflin, Ronald; Janak, Patricia H

    2015-10-21

    Midbrain dopamine (DA) neurons are proposed to signal reward prediction error (RPE), a fundamental parameter in associative learning models. This RPE hypothesis provides a compelling theoretical framework for understanding DA function in reward learning and addiction. New studies support a causal role for DA-mediated RPE activity in promoting learning about natural reward; however, this question has not been explicitly tested in the context of drug addiction. In this review, we integrate theoretical models with experimental findings on the activity of DA systems, and on the causal role of specific neuronal projections and cell types, to provide a circuit-based framework for probing DA-RPE function in addiction. By examining error-encoding DA neurons in the neural network in which they are embedded, hypotheses regarding circuit-level adaptations that possibly contribute to pathological error signaling and addiction can be formulated and tested. PMID:26494275

  3. Dopamine Prediction Errors in Reward Learning and Addiction: From Theory to Neural Circuitry.

    PubMed

    Keiflin, Ronald; Janak, Patricia H

    2015-10-21

    Midbrain dopamine (DA) neurons are proposed to signal reward prediction error (RPE), a fundamental parameter in associative learning models. This RPE hypothesis provides a compelling theoretical framework for understanding DA function in reward learning and addiction. New studies support a causal role for DA-mediated RPE activity in promoting learning about natural reward; however, this question has not been explicitly tested in the context of drug addiction. In this review, we integrate theoretical models with experimental findings on the activity of DA systems, and on the causal role of specific neuronal projections and cell types, to provide a circuit-based framework for probing DA-RPE function in addiction. By examining error-encoding DA neurons in the neural network in which they are embedded, hypotheses regarding circuit-level adaptations that possibly contribute to pathological error signaling and addiction can be formulated and tested.

  4. A machine learning approach to the accurate prediction of multi-leaf collimator positional errors

    NASA Astrophysics Data System (ADS)

    Carlson, Joel N. K.; Park, Jong Min; Park, So-Yeon; In Park, Jong; Choi, Yunseok; Ye, Sung-Joon

    2016-03-01

    Discrepancies between planned and delivered movements of multi-leaf collimators (MLCs) are an important source of errors in dose distributions during radiotherapy. In this work we used machine learning techniques to train models to predict these discrepancies, assessed the accuracy of the model predictions, and examined the impact these errors have on quality assurance (QA) procedures and dosimetry. Predictive leaf motion parameters for the models were calculated from the plan files, such as leaf position and velocity, whether the leaf was moving towards or away from the isocenter of the MLC, and many others. Differences in positions between synchronized DICOM-RT planning files and DynaLog files reported during QA delivery were used as a target response for training of the models. The final model is capable of predicting MLC positions during delivery to a high degree of accuracy. For moving MLC leaves, predicted positions were shown to be significantly closer to delivered positions than were planned positions. By incorporating predicted positions into dose calculations in the TPS, increases were shown in gamma passing rates against measured dose distributions recorded during QA delivery. For instance, head and neck plans with 1%/2 mm gamma criteria had an average increase in passing rate of 4.17% (SD  =  1.54%). This indicates that the inclusion of predictions during dose calculation leads to a more realistic representation of plan delivery. To assess impact on the patient, dose volumetric histograms (DVH) using delivered positions were calculated for comparison with planned and predicted DVHs. In all cases, predicted dose volumetric parameters were in closer agreement to the delivered parameters than were the planned parameters, particularly for organs at risk on the periphery of the treatment area. By incorporating the predicted positions into the TPS, the treatment planner is given a more realistic view of the dose distribution as it will truly be

  5. Dosimetric impact of geometric errors due to respiratory motion prediction on dynamic multileaf collimator-based four-dimensional radiation delivery

    SciTech Connect

    Vedam, S.; Docef, A.; Fix, M.; Murphy, M.; Keall, P.

    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 effects 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

  6. Influence of Precision of Emission Characteristic Parameters on Model Prediction Error of VOCs/Formaldehyde from Dry Building Material

    PubMed Central

    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 (C0), 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 C0, 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 C0 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

  7. Neural correlates of error monitoring in adolescents prospectively predict initiation of tobacco use.

    PubMed

    Anokhin, Andrey P; Golosheykin, Simon

    2015-12-01

    Deficits in self-regulation of behavior can play an important role in the initiation of substance use and progression to regular use and dependence. One of the distinct component processes of self-regulation is error monitoring, i.e. detection of a conflict between the intended and actually executed action. Here we examined whether a neural marker of error monitoring, Error-Related Negativity (ERN), predicts future initiation of tobacco use. ERN was assessed in a prospective longitudinal sample at ages 12, 14, and 16 using a flanker task. ERN amplitude showed a significant increase with age during adolescence. Reduced ERN amplitude at ages 14 and 16, as well as slower rate of its developmental changes significantly predicted initiation of tobacco use by age 18 but not transition to regular tobacco use or initiation of marijuana and alcohol use. The present results suggest that attenuated development of the neural mechanisms of error monitoring during adolescence can increase the risk for initiation of tobacco use. The present results also suggest that the role of distinct neurocognitive component processes involved in behavioral regulation may be limited to specific stages of addiction. PMID:26296779

  8. Effects of error covariance structure on estimation of model averaging weights and predictive performance

    USGS Publications Warehouse

    Lu, Dan; Ye, Ming; Meyer, Philip D.; Curtis, Gary P.; Shi, Xiaoqing; Niu, Xu-Feng; Yabusaki, Steve B.

    2013-01-01

    When conducting model averaging for assessing groundwater conceptual model uncertainty, the averaging weights are often evaluated using model selection criteria such as AIC, AICc, BIC, and KIC (Akaike Information Criterion, Corrected Akaike Information Criterion, Bayesian Information Criterion, and Kashyap Information Criterion, respectively). However, this method often leads to an unrealistic situation in which the best model receives overwhelmingly large averaging weight (close to 100%), which cannot be justified by available data and knowledge. It was found in this study that this problem was caused by using the covariance matrix, CE, of measurement errors for estimating the negative log likelihood function common to all the model selection criteria. This problem can be resolved by using the covariance matrix, Cek, of total errors (including model errors and measurement errors) to account for the correlation between the total errors. An iterative two-stage method was developed in the context of maximum likelihood inverse modeling to iteratively infer the unknown Cek from the residuals during model calibration. The inferred Cek was then used in the evaluation of model selection criteria and model averaging weights. While this method was limited to serial data using time series techniques in this study, it can be extended to spatial data using geostatistical techniques. The method was first evaluated in a synthetic study and then applied to an experimental study, in which alternative surface complexation models were developed to simulate column experiments of uranium reactive transport. It was found that the total errors of the alternative models were temporally correlated due to the model errors. The iterative two-stage method using Cekresolved the problem that the best model receives 100% model averaging weight, and the resulting model averaging weights were supported by the calibration results and physical understanding of the alternative models. Using Cek

  9. Effects of modeling errors on trajectory predictions in air traffic control automation

    NASA Technical Reports Server (NTRS)

    Jackson, Michael R. C.; Zhao, Yiyuan; Slattery, Rhonda

    1996-01-01

    Air traffic control automation synthesizes aircraft trajectories for the generation of advisories. Trajectory computation employs models of aircraft performances and weather conditions. In contrast, actual trajectories are flown in real aircraft under actual conditions. Since synthetic trajectories are used in landing scheduling and conflict probing, it is very important to understand the differences between computed trajectories and actual trajectories. This paper examines the effects of aircraft modeling errors on the accuracy of trajectory predictions in air traffic control automation. Three-dimensional point-mass aircraft equations of motion are assumed to be able to generate actual aircraft flight paths. Modeling errors are described as uncertain parameters or uncertain input functions. Pilot or autopilot feedback actions are expressed as equality constraints to satisfy control objectives. A typical trajectory is defined by a series of flight segments with different control objectives for each flight segment and conditions that define segment transitions. A constrained linearization approach is used to analyze trajectory differences caused by various modeling errors by developing a linear time varying system that describes the trajectory errors, with expressions to transfer the trajectory errors across moving segment transitions. A numerical example is presented for a complete commercial aircraft descent trajectory consisting of several flight segments.

  10. Mediofrontal event-related potentials in response to positive, negative and unsigned prediction errors.

    PubMed

    Sambrook, Thomas D; Goslin, Jeremy

    2014-08-01

    Reinforcement learning models make use of reward prediction errors (RPEs), the difference between an expected and obtained reward. There is evidence that the brain computes RPEs, but an outstanding question is whether positive RPEs ("better than expected") and negative RPEs ("worse than expected") are represented in a single integrated system. An electrophysiological component, feedback related negativity, has been claimed to encode an RPE but its relative sensitivity to the utility of positive and negative RPEs remains unclear. This study explored the question by varying the utility of positive and negative RPEs in a design that controlled for other closely related properties of feedback and could distinguish utility from salience. It revealed a mediofrontal sensitivity to utility, for positive RPEs at 275-310ms and for negative RPEs at 310-390ms. These effects were preceded and succeeded by a response consistent with an unsigned prediction error, or "salience" coding.

  11. Predicting Pilot Error in Nextgen: Pilot Performance Modeling and Validation Efforts

    NASA Technical Reports Server (NTRS)

    Wickens, Christopher; Sebok, Angelia; Gore, Brian; Hooey, Becky

    2012-01-01

    We review 25 articles presenting 5 general classes of computational models to predict pilot error. This more targeted review is placed within the context of the broader review of computational models of pilot cognition and performance, including such aspects as models of situation awareness or pilot-automation interaction. Particular emphasis is placed on the degree of validation of such models against empirical pilot data, and the relevance of the modeling and validation efforts to Next Gen technology and procedures.

  12. Updating memories--the role of prediction errors in memory reconsolidation.

    PubMed

    Exton-McGuinness, Marc T J; Lee, Jonathan L C; Reichelt, Amy C

    2015-02-01

    Memories are not static imprints of past experience, but rather are dynamic entities which enable us to predict outcomes of future situations and inform appropriate behaviours. In order to maintain the relevance of existing memories to our daily lives, memories can be updated with new information via a process of reconsolidation. In this review we describe recent experimental advances in the reconsolidation of both appetitive and aversive memory, and explore the neuronal mechanisms that underpin the conditions under which reconsolidation will occur. We propose that a prediction error signal, originating from dopaminergic midbrain neurons, is necessary for destabilisation and subsequent reconsolidation of a memory. PMID:25453746

  13. Updating memories--the role of prediction errors in memory reconsolidation.

    PubMed

    Exton-McGuinness, Marc T J; Lee, Jonathan L C; Reichelt, Amy C

    2015-02-01

    Memories are not static imprints of past experience, but rather are dynamic entities which enable us to predict outcomes of future situations and inform appropriate behaviours. In order to maintain the relevance of existing memories to our daily lives, memories can be updated with new information via a process of reconsolidation. In this review we describe recent experimental advances in the reconsolidation of both appetitive and aversive memory, and explore the neuronal mechanisms that underpin the conditions under which reconsolidation will occur. We propose that a prediction error signal, originating from dopaminergic midbrain neurons, is necessary for destabilisation and subsequent reconsolidation of a memory.

  14. Efficient reversible watermarking based on adaptive prediction-error expansion and pixel selection.

    PubMed

    Li, Xiaolong; Yang, Bin; Zeng, Tieyong

    2011-12-01

    Prediction-error expansion (PEE) is an important technique of reversible watermarking which can embed large payloads into digital images with low distortion. In this paper, the PEE technique is further investigated and an efficient reversible watermarking scheme is proposed, by incorporating in PEE two new strategies, namely, adaptive embedding and pixel selection. Unlike conventional PEE which embeds data uniformly, we propose to adaptively embed 1 or 2 bits into expandable pixel according to the local complexity. This avoids expanding pixels with large prediction-errors, and thus, it reduces embedding impact by decreasing the maximum modification to pixel values. Meanwhile, adaptive PEE allows very large payload in a single embedding pass, and it improves the capacity limit of conventional PEE. We also propose to select pixels of smooth area for data embedding and leave rough pixels unchanged. In this way, compared with conventional PEE, a more sharply distributed prediction-error histogram is obtained and a better visual quality of watermarked image is observed. With these improvements, our method outperforms conventional PEE. Its superiority over other state-of-the-art methods is also demonstrated experimentally.

  15. Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning

    PubMed Central

    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

  16. Belief about nicotine selectively modulates value and reward prediction error signals in smokers

    PubMed Central

    Gu, Xiaosi; Lohrenz, Terry; Salas, Ramiro; Baldwin, Philip R.; Soltani, Alireza; Kirk, Ulrich; Cinciripini, Paul M.; Montague, P. Read

    2015-01-01

    Little is known about how prior beliefs impact biophysically described processes in the presence of neuroactive drugs, which presents a profound challenge to the understanding of the mechanisms and treatments of addiction. We engineered smokers’ prior beliefs about the presence of nicotine in a cigarette smoked before a functional magnetic resonance imaging session where subjects carried out a sequential choice task. Using a model-based approach, we show that smokers’ beliefs about nicotine specifically modulated learning signals (value and reward prediction error) defined by a computational model of mesolimbic dopamine systems. Belief of “no nicotine in cigarette” (compared with “nicotine in cigarette”) strongly diminished neural responses in the striatum to value and reward prediction errors and reduced the impact of both on smokers’ choices. These effects of belief could not be explained by global changes in visual attention and were specific to value and reward prediction errors. Thus, by modulating the expression of computationally explicit signals important for valuation and choice, beliefs can override the physical presence of a potent neuroactive compound like nicotine. These selective effects of belief demonstrate that belief can modulate model-based parameters important for learning. The implications of these findings may be far ranging because belief-dependent effects on learning signals could impact a host of other behaviors in addiction as well as in other mental health problems. PMID:25605923

  17. Belief about nicotine selectively modulates value and reward prediction error signals in smokers.

    PubMed

    Gu, Xiaosi; Lohrenz, Terry; Salas, Ramiro; Baldwin, Philip R; Soltani, Alireza; Kirk, Ulrich; Cinciripini, Paul M; Montague, P Read

    2015-02-24

    Little is known about how prior beliefs impact biophysically described processes in the presence of neuroactive drugs, which presents a profound challenge to the understanding of the mechanisms and treatments of addiction. We engineered smokers' prior beliefs about the presence of nicotine in a cigarette smoked before a functional magnetic resonance imaging session where subjects carried out a sequential choice task. Using a model-based approach, we show that smokers' beliefs about nicotine specifically modulated learning signals (value and reward prediction error) defined by a computational model of mesolimbic dopamine systems. Belief of "no nicotine in cigarette" (compared with "nicotine in cigarette") strongly diminished neural responses in the striatum to value and reward prediction errors and reduced the impact of both on smokers' choices. These effects of belief could not be explained by global changes in visual attention and were specific to value and reward prediction errors. Thus, by modulating the expression of computationally explicit signals important for valuation and choice, beliefs can override the physical presence of a potent neuroactive compound like nicotine. These selective effects of belief demonstrate that belief can modulate model-based parameters important for learning. The implications of these findings may be far ranging because belief-dependent effects on learning signals could impact a host of other behaviors in addiction as well as in other mental health problems.

  18. Testosterone and reward prediction-errors in healthy men and men with schizophrenia.

    PubMed

    Morris, R W; Purves-Tyson, T D; Weickert, C Shannon; Rothmond, D; Lenroot, R; Weickert, T W

    2015-11-01

    Sex hormones impact reward processing, which is dysfunctional in schizophrenia; however, the degree to which testosterone levels relate to reward-related brain activity in healthy men and the extent to which this relationship may be altered in men with schizophrenia has not been determined. We used functional magnetic resonance imaging (fMRI) to measure neural responses in the striatum during reward prediction-errors and hormone assays to measure testosterone and prolactin in serum. To determine if testosterone can have a direct effect on dopamine neurons, we also localized and measured androgen receptors in human midbrain with immunohistochemistry and quantitative PCR. We found correlations between testosterone and prediction-error related activity in the ventral striatum of healthy men, but not in men with schizophrenia, such that testosterone increased the size of positive and negative prediction-error related activity in a valence-specific manner. We also identified midbrain dopamine neurons that were androgen receptor immunoreactive, and found that androgen receptor (AR) mRNA was positively correlated with tyrosine hydroxylase (TH) mRNA in human male substantia nigra. The results suggest that sex steroid receptors can potentially influence midbrain dopamine biosynthesis, and higher levels of serum testosterone are linked to better discrimination of motivationally-relevant signals in the ventral striatum, putatively by modulation of the dopamine biosynthesis pathway via AR ligand binding. However, the normal relationship between serum testosterone and ventral striatum activity during reward learning appears to be disrupted in schizophrenia. PMID:26232868

  19. Offset-corrected Δ -Kohn-Sham scheme for semiempirical prediction of absolute x-ray photoelectron energies in molecules and solids

    NASA Astrophysics Data System (ADS)

    Walter, Michael; Moseler, Michael; Pastewka, Lars

    2016-07-01

    Absolute binding energies of core electrons in molecules and bulk materials can be efficiently calculated by spin paired density-function theory employing a Δ -Kohn-Sham (Δ KS ) scheme corrected by offsets that are highly transferable. These offsets depend on core level and atomic species and can be determined by comparing Δ KS energies to experimental molecular x-ray photoelectron spectra. We demonstrate the correct prediction of absolute and relative binding energies on a wide range of molecules, metals, and insulators.

  20. The predicted CLARREO sampling error of the inter-annual SW variability

    NASA Astrophysics Data System (ADS)

    Doelling, D. R.; Keyes, D. F.; Nguyen, C.; Macdonnell, D.; Young, D. F.

    2009-12-01

    The NRC Decadal Survey has called for SI traceability of long-term hyper-spectral flux measurements in order to monitor climate variability. This mission is called the Climate Absolute Radiance and Refractivity Observatory (CLARREO) and is currently defining its mission requirements. The requirements are focused on the ability to measure decadal change of key climate variables at very high accuracy. The accuracy goals are set using anticipated climate change magnitudes, but the accuracy achieved for any given climate variable must take into account the temporal and spatial sampling errors based on satellite orbits and calibration accuracy. The time period to detect a significant trend in the CLARREO record depends on the magnitude of the sampling calibration errors relative to the current inter-annual variability. The largest uncertainty in climate feedbacks remains the effect of changing clouds on planetary energy balance. Some regions on earth have strong diurnal cycles, such as maritime stratus and afternoon land convection; other regions have strong seasonal cycles, such as the monsoon. However, when monitoring inter-annual variability these cycles are only important if the strength of these cycles vary on decadal time scales. This study will attempt to determine the best satellite constellations to reduce sampling error and to compare the error with the current inter-annual variability signal to ensure the viability of the mission. The study will incorporate Clouds and the Earth's Radiant Energy System (CERES) (Monthly TOA/Surface Averages) SRBAVG product TOA LW and SW climate quality fluxes. The fluxes are derived by combining Terra (10:30 local equator crossing time) CERES fluxes with 3-hourly 5-geostationary satellite estimated broadband fluxes, which are normalized using the CERES fluxes, to complete the diurnal cycle. These fluxes were saved hourly during processing and considered the truth dataset. 90°, 83° and 74° inclination precessionary orbits as

  1. Early adversity disrupts the adult use of aversive prediction errors to reduce fear in uncertainty

    PubMed Central

    Wright, Kristina M.; DiLeo, Alyssa; McDannald, Michael A.

    2015-01-01

    Early life adversity increases anxiety in adult rodents and primates, and increases the risk for developing post-traumatic disorder (PTSD) in humans. We hypothesized that early adversity impairs the use of learning signals -negative, aversive prediction errors–to reduce fear in uncertainty. To test this hypothesis, we gave adolescent rats a battery of adverse experiences then assessed adult performance in probabilistic Pavlovian fear conditioning and fear extinction. Rats were confronted with three cues associated with different probabilities of foot shock: one cue never predicted shock, another cue predicted shock with uncertainty, and a final cue always predicted shock. Control rats initially acquired fear to all cues, but rapidly reduced fear to the non-predictive and uncertain cues. Early adversity rats were slower to reduce fear to the non-predictive cue and never fully reduced fear to the uncertain cue. In extinction, all cues were presented in the absence of shock. Fear to the uncertain cue in discrimination, but not early adversity itself, predicted the reduction of fear in extinction. These results demonstrate early adversity impairs the use of negative aversive prediction errors to reduce fear, especially in situations of uncertainty. PMID:26379520

  2. Effects of rapid eye movement sleep deprivation on fear extinction recall and prediction error signaling.

    PubMed

    Spoormaker, Victor I; Schröter, Manuel S; Andrade, Kátia C; Dresler, Martin; Kiem, Sara A; Goya-Maldonado, Roberto; Wetter, Thomas C; Holsboer, Florian; Sämann, Philipp G; Czisch, Michael

    2012-10-01

    In a temporal difference learning approach of classical conditioning, a theoretical error signal shifts from outcome deliverance to the onset of the conditioned stimulus. Omission of an expected outcome results in a negative prediction error signal, which is the initial step towards successful extinction and may therefore be relevant for fear extinction recall. As studies in rodents have observed a bidirectional relationship between fear extinction and rapid eye movement (REM) sleep, we aimed to test the hypothesis that REM sleep deprivation impairs recall of fear extinction through prediction error signaling in humans. In a three-day design with polysomnographically controlled REM sleep deprivation, 18 young, healthy subjects performed a fear conditioning, extinction and recall of extinction task with visual stimuli, and mild electrical shocks during combined functional magnetic resonance imaging (fMRI) and skin conductance response (SCR) measurements. Compared to the control group, the REM sleep deprivation group had increased SCR scores to a previously extinguished stimulus at early recall of extinction trials, which was associated with an altered fMRI time-course in the left middle temporal gyrus. Post-hoc contrasts corrected for measures of NREM sleep variability also revealed between-group differences primarily in the temporal lobe. Our results demonstrate altered prediction error signaling during recall of fear extinction after REM sleep deprivation, which may further our understanding of anxiety disorders in which disturbed sleep and impaired fear extinction learning coincide. Moreover, our findings are indicative of REM sleep related plasticity in regions that also show an increase in activity during REM sleep.

  3. Phasic dopamine release in the rat nucleus accumbens symmetrically encodes a reward prediction error term.

    PubMed

    Hart, Andrew S; Rutledge, Robb B; Glimcher, Paul W; Phillips, Paul E M

    2014-01-15

    Making predictions about the rewards associated with environmental stimuli and updating those predictions through feedback is an essential aspect of adaptive behavior. Theorists have argued that dopamine encodes a reward prediction error (RPE) signal that is used in such a reinforcement learning process. Recent work with fMRI has demonstrated that the BOLD signal in dopaminergic target areas meets both necessary and sufficient conditions of an axiomatic model of the RPE hypothesis. However, there has been no direct evidence that dopamine release itself also meets necessary and sufficient criteria for encoding an RPE signal. Further, the fact that dopamine neurons have low tonic firing rates that yield a limited dynamic range for encoding negative RPEs has led to significant debate about whether positive and negative prediction errors are encoded on a similar scale. To address both of these issues, we used fast-scan cyclic voltammetry to measure reward-evoked dopamine release at carbon fiber electrodes chronically implanted in the nucleus accumbens core of rats trained on a probabilistic decision-making task. We demonstrate that dopamine concentrations transmit a bidirectional RPE signal with symmetrical encoding of positive and negative RPEs. Our findings strengthen the case that changes in dopamine concentration alone are sufficient to encode the full range of RPEs necessary for reinforcement learning.

  4. Filtering and Predicting Complex Nonlinear Turbulent Dynamical Systems with Model Error

    NASA Astrophysics Data System (ADS)

    Chen, Nan

    This dissertation includes five topics in filtering and predicting complex turbulent systems with model error from noisy partial observations. An efficient and accurate model calibration is the prerequisite of filtering and prediction. The first topic involves adopting Bayesian inference that incorporates data augmentation in a Markov chain Monte Carlo algorithm to estimate the parameters in a reduced model that describes nature with hidden instability. A novel pre-estimation of hidden processes greatly enhances the efficiency of the algorithm. The model equipped with the estimated parameters succeeds in predicting the extreme events in nature. The filtering and prediction of the Madden-Julian oscillation (MJO) and relevant tropical waves have significant implications for extended range forecasting. A physics-constrained low-order nonlinear stochastic model involving correlated multiplicative noise defined through energy conserving nonlinear interaction is developed to predict two MJO indices with different features. The special structure of the model allows efficient data assimilation and ensemble initialization algorithms for the hidden variables. Utilizing an information-theoretic framework for model calibration, the model has significant skill for determining the predictability limits of the MJO. Filtering the stochastic skeleton model for the MJO with noisy partial observations is another central topic. A nonlinear filter, which captures the inherent nonlinearity of the system, is proposed and judicious model error is included. An effectively balanced reduced filter involving a simple fast-wave averaging strategy is developed, which facilitates filtering the moisture and other fast-oscillating modes and enhances the total computational efficiency. Both filters succeed in filtering the MJO and other large-scale features. The last two topics focus on filtering complex turbulent systems within a conditional Gaussian framework. Despite the conditional Gaussianity

  5. Quantifying the predictive consequences of model error with linear subspace analysis

    USGS Publications Warehouse

    White, Jeremy T.; Doherty, John E.; Hughes, Joseph D.

    2014-01-01

    All computer models are simplified and imperfect simulators of complex natural systems. The discrepancy arising from simplification induces bias in model predictions, which may be amplified by the process of model calibration. This paper presents a new method to identify and quantify the predictive consequences of calibrating a simplified computer model. The method is based on linear theory, and it scales efficiently to the large numbers of parameters and observations characteristic of groundwater and petroleum reservoir models. The method is applied to a range of predictions made with a synthetic integrated surface-water/groundwater model with thousands of parameters. Several different observation processing strategies and parameterization/regularization approaches are examined in detail, including use of the Karhunen-Loève parameter transformation. Predictive bias arising from model error is shown to be prediction specific and often invisible to the modeler. The amount of calibration-induced bias is influenced by several factors, including how expert knowledge is applied in the design of parameterization schemes, the number of parameters adjusted during calibration, how observations and model-generated counterparts are processed, and the level of fit with observations achieved through calibration. Failure to properly implement any of these factors in a prediction-specific manner may increase the potential for predictive bias in ways that are not visible to the calibration and uncertainty analysis process.

  6. Putting reward in art: A tentative prediction error account of visual art.

    PubMed

    Van de Cruys, Sander; Wagemans, Johan

    2011-01-01

    The predictive coding model is increasingly and fruitfully used to explain a wide range of findings in perception. Here we discuss the potential of this model in explaining the mechanisms underlying aesthetic experiences. Traditionally art appreciation has been associated with concepts such as harmony, perceptual fluency, and the so-called good Gestalt. We observe that more often than not great artworks blatantly violate these characteristics. Using the concept of prediction error from the predictive coding approach, we attempt to resolve this contradiction. We argue that artists often destroy predictions that they have first carefully built up in their viewers, and thus highlight the importance of negative affect in aesthetic experience. However, the viewer often succeeds in recovering the predictable pattern, sometimes on a different level. The ensuing rewarding effect is derived from this transition from a state of uncertainty to a state of increased predictability. We illustrate our account with several example paintings and with a discussion of art movements and individual differences in preference. On a more fundamental level, our theorizing leads us to consider the affective implications of prediction confirmation and violation. We compare our proposal to other influential theories on aesthetics and explore its advantages and limitations.

  7. Putting reward in art: A tentative prediction error account of visual art

    PubMed Central

    Van de Cruys, Sander; Wagemans, Johan

    2011-01-01

    The predictive coding model is increasingly and fruitfully used to explain a wide range of findings in perception. Here we discuss the potential of this model in explaining the mechanisms underlying aesthetic experiences. Traditionally art appreciation has been associated with concepts such as harmony, perceptual fluency, and the so-called good Gestalt. We observe that more often than not great artworks blatantly violate these characteristics. Using the concept of prediction error from the predictive coding approach, we attempt to resolve this contradiction. We argue that artists often destroy predictions that they have first carefully built up in their viewers, and thus highlight the importance of negative affect in aesthetic experience. However, the viewer often succeeds in recovering the predictable pattern, sometimes on a different level. The ensuing rewarding effect is derived from this transition from a state of uncertainty to a state of increased predictability. We illustrate our account with several example paintings and with a discussion of art movements and individual differences in preference. On a more fundamental level, our theorizing leads us to consider the affective implications of prediction confirmation and violation. We compare our proposal to other influential theories on aesthetics and explore its advantages and limitations. PMID:23145260

  8. Central difference predictive filter for attitude determination with low precision sensors and model errors

    NASA Astrophysics Data System (ADS)

    Cao, Lu; Chen, Xiaoqian; Misra, Arun K.

    2014-12-01

    Attitude determination is one of the key technologies for Attitude Determination and Control System (ADCS) of a satellite. However, serious model errors may exist which will affect the estimation accuracy of ACDS, especially for a small satellite with low precision sensors. In this paper, a central difference predictive filter (CDPF) is proposed for attitude determination of small satellites with model errors and low precision sensors. The new filter is proposed by introducing the Stirling's polynomial interpolation formula to extend the traditional predictive filter (PF). It is shown that the proposed filter has higher accuracy for the estimation of system states than the traditional PF. It is known that the unscented Kalman filter (UKF) has also been used in the ADCS of small satellites with low precision sensors. In order to evaluate the performance of the proposed filter, the UKF is also employed to compare it with the CDPF. Numerical simulations show that the proposed CDPF is more effective and robust in dealing with model errors and low precision sensors compared with the UKF or traditional PF.

  9. Ancient documents bleed-through evaluation and its application for predicting OCR error rates

    NASA Astrophysics Data System (ADS)

    Rabeux, V.; Journet, N.; Domenger, J. P.

    2011-01-01

    This article presents a way to evaluate the bleed-through defect on very old document images. We design measures to quantify and evaluate the verso ink bleeding through the paper onto the recto side. Measuring the bleed-through defect alows us to perform statistical analysis that are able to predict the feasibility of different post-scan tasks. In this article we choose to illustrate our measures by creating two OCR error rate predicting models based bleed-through evaluation. Two models are proposed, one for Abbyy FineReader * which is a very power-full commercial OCR and OCRopus † which is sponsored by Google. Both prediction models appears to be very accurate when calculating various statistic indicators.

  10. Mean Expected Error in Prediction of Total Body Water: A True Accuracy Comparison between Bioimpedance Spectroscopy and Single Frequency Regression Equations

    PubMed Central

    Abtahi, Shirin; Abtahi, Farhad; Ellegård, Lars; Johannsson, Gudmundur; Bosaeus, Ingvar

    2015-01-01

    For several decades electrical bioimpedance (EBI) has been used to assess body fluid distribution and body composition. Despite the development of several different approaches for assessing total body water (TBW), it remains uncertain whether bioimpedance spectroscopic (BIS) approaches are more accurate than single frequency regression equations. The main objective of this study was to answer this question by calculating the expected accuracy of a single measurement for different EBI methods. The results of this study showed that all methods produced similarly high correlation and concordance coefficients, indicating good accuracy as a method. Even the limits of agreement produced from the Bland-Altman analysis indicated that the performance of single frequency, Sun's prediction equations, at population level was close to the performance of both BIS methods; however, when comparing the Mean Absolute Percentage Error value between the single frequency prediction equations and the BIS methods, a significant difference was obtained, indicating slightly better accuracy for the BIS methods. Despite the higher accuracy of BIS methods over 50 kHz prediction equations at both population and individual level, the magnitude of the improvement was small. Such slight improvement in accuracy of BIS methods is suggested insufficient to warrant their clinical use where the most accurate predictions of TBW are required, for example, when assessing over-fluidic status on dialysis. To reach expected errors below 4-5%, novel and individualized approaches must be developed to improve the accuracy of bioimpedance-based methods for the advent of innovative personalized health monitoring applications. PMID:26137489

  11. Reassessing Domain Architecture Evolution of Metazoan Proteins: Major Impact of Gene Prediction Errors

    PubMed Central

    Nagy, Alinda; Szláma, György; Szarka, Eszter; Trexler, Mária; Bányai, László; Patthy, László

    2011-01-01

    In view of the fact that appearance of novel protein domain architectures (DA) is closely associated with biological innovations, there is a growing interest in the genome-scale reconstruction of the evolutionary history of the domain architectures of multidomain proteins. In such analyses, however, it is usually ignored that a significant proportion of Metazoan sequences analyzed is mispredicted and that this may seriously affect the validity of the conclusions. To estimate the contribution of errors in gene prediction to differences in DA of predicted proteins, we have used the high quality manually curated UniProtKB/Swiss-Prot database as a reference. For genome-scale analysis of domain architectures of predicted proteins we focused on RefSeq, EnsEMBL and NCBI's GNOMON predicted sequences of Metazoan species with completely sequenced genomes. Comparison of the DA of UniProtKB/Swiss-Prot sequences of worm, fly, zebrafish, frog, chick, mouse, rat and orangutan with those of human Swiss-Prot entries have identified relatively few cases where orthologs had different DA, although the percentage with different DA increased with evolutionary distance. In contrast with this, comparison of the DA of human, orangutan, rat, mouse, chicken, frog, zebrafish, worm and fly RefSeq, EnsEMBL and NCBI's GNOMON predicted protein sequences with those of the corresponding/orthologous human Swiss-Prot entries identified a significantly higher proportion of domain architecture differences than in the case of the comparison of Swiss-Prot entries. Analysis of RefSeq, EnsEMBL and NCBI's GNOMON predicted protein sequences with DAs different from those of their Swiss-Prot orthologs confirmed that the higher rate of domain architecture differences is due to errors in gene prediction, the majority of which could be corrected with our FixPred protocol. We have also demonstrated that contamination of databases with incomplete, abnormal or mispredicted sequences introduces a bias in DA

  12. Modeling workplace contact networks: The effects of organizational structure, architecture, and reporting errors on epidemic predictions

    PubMed Central

    Potter, Gail E.; Smieszek, Timo; Sailer, Kerstin

    2015-01-01

    Face-to-face social contacts are potentially important transmission routes for acute respiratory infections, and understanding the contact network can improve our ability to predict, contain, and control epidemics. Although workplaces are important settings for infectious disease transmission, few studies have collected workplace contact data and estimated workplace contact networks. We use contact diaries, architectural distance measures, and institutional structures to estimate social contact networks within a Swiss research institute. Some contact reports were inconsistent, indicating reporting errors. We adjust for this with a latent variable model, jointly estimating the true (unobserved) network of contacts and duration-specific reporting probabilities. We find that contact probability decreases with distance, and that research group membership, role, and shared projects are strongly predictive of contact patterns. Estimated reporting probabilities were low only for 0–5 min contacts. Adjusting for reporting error changed the estimate of the duration distribution, but did not change the estimates of covariate effects and had little effect on epidemic predictions. Our epidemic simulation study indicates that inclusion of network structure based on architectural and organizational structure data can improve the accuracy of epidemic forecasting models. PMID:26634122

  13. Model-based influences on humans’ choices and striatal prediction errors

    PubMed Central

    Daw, Nathaniel D.; Gershman, Samuel J.; Seymour, Ben; Dayan, Peter; Dolan, Raymond J.

    2011-01-01

    Summary The mesostriatal dopamine system is prominently implicated in model-free reinforcement learning, with fMRI BOLD signals in ventral striatum notably covarying with model-free prediction errors. However, latent learning and devaluation studies show that behavior also shows hallmarks of model-based planning, and the interaction between model-based and model-free values, prediction errors and preferences is underexplored. We designed a multistep decision task in which model-based and model-free influences on human choice behavior could be distinguished. By showing that choices reflected both influences we could then test the purity of the ventral striatal BOLD signal as a model-free report. Contrary to expectations, the signal reflected both model-free and model-based predictions in proportions matching those that best explained choice behavior. These results challenge the notion of a separate model-free learner and suggest a more integrated computational architecture for high-level human decision-making. PMID:21435563

  14. Detection of microcalcifications in mammograms using error of prediction and statistical measures

    NASA Astrophysics Data System (ADS)

    Acha, Begoña; Serrano, Carmen; Rangayyan, Rangaraj M.; Leo Desautels, J. E.

    2009-01-01

    A two-stage method for detecting microcalcifications in mammograms is presented. In the first stage, the determination of the candidates for microcalcifications is performed. For this purpose, a 2-D linear prediction error filter is applied, and for those pixels where the prediction error is larger than a threshold, a statistical measure is calculated to determine whether they are candidates for microcalcifications or not. In the second stage, a feature vector is derived for each candidate, and after a classification step using a support vector machine, the final detection is performed. The algorithm is tested with 40 mammographic images, from Screen Test: The Alberta Program for the Early Detection of Breast Cancer with 50-μm resolution, and the results are evaluated using a free-response receiver operating characteristics curve. Two different analyses are performed: an individual microcalcification detection analysis and a cluster analysis. In the analysis of individual microcalcifications, detection sensitivity values of 0.75 and 0.81 are obtained at 2.6 and 6.2 false positives per image, on the average, respectively. The best performance is characterized by a sensitivity of 0.89, a specificity of 0.99, and a positive predictive value of 0.79. In cluster analysis, a sensitivity value of 0.97 is obtained at 1.77 false positives per image, and a value of 0.90 is achieved at 0.94 false positive per image.

  15. Nodal predictive error model and Bayesian approach for thermal diffusivity and heat source mapping

    NASA Astrophysics Data System (ADS)

    Massard, H.; Fudym, Olivier; Orlande, H. R. B.; Batsale, J. C.

    2010-07-01

    This article aims at solving a two-dimensional inverse heat conduction problem in order to retrieve both the thermal diffusivity and heat source field in a thin plate. A spatial random heat pulse is applied to the plate and the thermal response is analysed. The inverse approach is based on the minimisation of a nodal predictive error model, which yields a linear estimation problem. As a result of this approach, the sensitivity matrix is directly filled with experimental data, and thus is partially noisy. Bayesian estimators, such as the Maximum A Posteriori and a Markov Chain Monte Carlo approach (Metropolis-Hastings), are implemented and compared with the Ordinary Least Squares solution. Simulated temperature measurements are used in the inverse analysis. The nodal strategy relies on the availability of temperature measurements with fine spatial resolution and high frequency, typical of nowadays infrared cameras. The effects of both the measurement errors and of the model errors on the inverse problem solution are also analysed.

  16. Influence of the occlusion effect over the prediction-error feedback cancellation system in hearing aids.

    PubMed

    Coelho Borges, Renata; Holsbach Costa, Marcio

    2015-08-01

    This work presents a theoretical analysis of the prediction-error method-based adaptive feedback canceller in hearing aid applications. The studied scene takes into account the occlusion effect caused by the partial or complete closing of the ventilation opening. Such a situation may occur in high gain applications to avoid undesired whistling. Deterministic recursive equations and steady-state conditions were derived for the mean weight behaviour of the predictor and the adaptive filter. The expected theoretical predictions were compared to Monte Carlo simulations, showing very accurate agreement. The simulation results suggest the steady-state performance of this feedback canceller is not affected by the occlusion effect, however the occlusion is still perceived, being annoying to the user. PMID:26736855

  17. Measured and predicted root-mean-square errors in square and triangular antenna mesh facets

    NASA Technical Reports Server (NTRS)

    Fichter, W. B.

    1989-01-01

    Deflection shapes of square and equilateral triangular facets of two tricot-knit, gold plated molybdenum wire mesh antenna materials were measured and compared, on the basis of root mean square (rms) differences, with deflection shapes predicted by linear membrane theory, for several cases of biaxial mesh tension. The two mesh materials contained approximately 10 and 16 holes per linear inch, measured diagonally with respect to the course and wale directions. The deflection measurement system employed a non-contact eddy current proximity probe and an electromagnetic distance sensing probe in conjunction with a precision optical level. Despite experimental uncertainties, rms differences between measured and predicted deflection shapes suggest the following conclusions: that replacing flat antenna facets with facets conforming to parabolically curved structural members yields smaller rms surface error; that potential accuracy gains are greater for equilateral triangular facets than for square facets; and that linear membrane theory can be a useful tool in the design of tricot knit wire mesh antennas.

  18. Climbing fibers encode a temporal-difference prediction error during cerebellar learning in mice

    PubMed Central

    Ohmae, Shogo; Medina, Javier F.

    2016-01-01

    Climbing fiber inputs to Purkinje cells are thought to play a teaching role by generating the instructive signals that drive cerebellar learning. To investigate how these instructive signals are encoded, we recorded the activity of individual climbing fibers during cerebellar-dependent eyeblink conditioning in mice. Our findings show that climbing fibers signal both the unexpected delivery and the unexpected omission of the periocular airpuff that serves as the instructive signal for eyeblink conditioning. In addition, we report the surprising discovery that climbing fibers activated by periocular airpuffs also respond to stimuli from other sensory modalities, if those stimuli are novel or if they predict that the periocular airpuff is about to be presented. This pattern of climbing fiber activity is strikingly similar to the responses of dopamine neurons during reinforcement learning, which have been shown to encode a particular type of instructive signal known as a temporal difference prediction error. PMID:26551541

  19. Addressing Conceptual Model Uncertainty in the Evaluation of Model Prediction Errors

    NASA Astrophysics Data System (ADS)

    Carrera, J.; Pool, M.

    2014-12-01

    Model predictions are uncertain because of errors in model parameters, future forcing terms, and model concepts. The latter remain the largest and most difficult to assess source of uncertainty in long term model predictions. We first review existing methods to evaluate conceptual model uncertainty. We argue that they are highly sensitive to the ingenuity of the modeler, in the sense that they rely on the modeler's ability to propose alternative model concepts. Worse, we find that the standard practice of stochastic methods leads to poor, potentially biased and often too optimistic, estimation of actual model errors. This is bad news because stochastic methods are purported to properly represent uncertainty. We contend that the problem does not lie on the stochastic approach itself, but on the way it is applied. Specifically, stochastic inversion methodologies, which demand quantitative information, tend to ignore geological understanding, which is conceptually rich. We illustrate some of these problems with the application to Mar del Plata aquifer, where extensive data are available for nearly a century. Geologically based models, where spatial variability is handled through zonation, yield calibration fits similar to geostatiscally based models, but much better predictions. In fact, the appearance of the stochastic T fields is similar to the geologically based models only in areas with high density of data. We take this finding to illustrate the ability of stochastic models to accommodate many data, but also, ironically, their inability to address conceptual model uncertainty. In fact, stochastic model realizations tend to be too close to the "most likely" one (i.e., they do not really realize the full conceptualuncertainty). The second part of the presentation is devoted to argue that acknowledging model uncertainty may lead to qualitatively different decisions than just working with "most likely" model predictions. Therefore, efforts should concentrate on

  20. Prediction error and trace dominance determine the fate of fear memories after post-training manipulations.

    PubMed

    Alfei, Joaquín M; Ferrer Monti, Roque I; Molina, Victor A; Bueno, Adrián M; Urcelay, Gonzalo P

    2015-08-01

    Different mnemonic outcomes have been observed when associative memories are reactivated by CS exposure and followed by amnestics. These outcomes include mere retrieval, destabilization-reconsolidation, a transitional period (which is insensitive to amnestics), and extinction learning. However, little is known about the interaction between initial learning conditions and these outcomes during a reinforced or nonreinforced reactivation. Here we systematically combined temporally specific memories with different reactivation parameters to observe whether these four outcomes are determined by the conditions established during training. First, we validated two training regimens with different temporal expectations about US arrival. Then, using Midazolam (MDZ) as an amnestic agent, fear memories in both learning conditions were submitted to retraining either under identical or different parameters to the original training. Destabilization (i.e., susceptibly to MDZ) occurred when reactivation was reinforced, provided the occurrence of a temporal prediction error about US arrival. In subsequent experiments, both treatments were systematically reactivated by nonreinforced context exposure of different lengths, which allowed to explore the interaction between training and reactivation lengths. These results suggest that temporal prediction error and trace dominance determine the extent to which reactivation produces the different outcomes.

  1. Delusions and prediction error: clarifying the roles of behavioural and brain responses

    PubMed Central

    Corlett, Philip Robert; Fletcher, Paul Charles

    2015-01-01

    Griffiths and colleagues provided a clear and thoughtful review of the prediction error model of delusion formation [Cognitive Neuropsychiatry, 2014 April 4 (Epub ahead of print)]. As well as reviewing the central ideas and concluding that the existing evidence base is broadly supportive of the model, they provide a detailed critique of some of the experiments that we have performed to study it. Though they conclude that the shortcomings that they identify in these experiments do not fundamentally challenge the prediction error model, we nevertheless respond to these criticisms. We begin by providing a more detailed outline of the model itself as there are certain important aspects of it that were not covered in their review. We then respond to their specific criticisms of the empirical evidence. We defend the neuroimaging contrasts that we used to explore this model of psychosis arguing that, while any single contrast entails some ambiguity, our assumptions have been justified by our extensive background work before and since. PMID:25559871

  2. Prediction error and trace dominance determine the fate of fear memories after post-training manipulations

    PubMed Central

    Alfei, Joaquín M.; Ferrer Monti, Roque I.; Molina, Victor A.; Bueno, Adrián M.

    2015-01-01

    Different mnemonic outcomes have been observed when associative memories are reactivated by CS exposure and followed by amnestics. These outcomes include mere retrieval, destabilization–reconsolidation, a transitional period (which is insensitive to amnestics), and extinction learning. However, little is known about the interaction between initial learning conditions and these outcomes during a reinforced or nonreinforced reactivation. Here we systematically combined temporally specific memories with different reactivation parameters to observe whether these four outcomes are determined by the conditions established during training. First, we validated two training regimens with different temporal expectations about US arrival. Then, using Midazolam (MDZ) as an amnestic agent, fear memories in both learning conditions were submitted to retraining either under identical or different parameters to the original training. Destabilization (i.e., susceptibly to MDZ) occurred when reactivation was reinforced, provided the occurrence of a temporal prediction error about US arrival. In subsequent experiments, both treatments were systematically reactivated by nonreinforced context exposure of different lengths, which allowed to explore the interaction between training and reactivation lengths. These results suggest that temporal prediction error and trace dominance determine the extent to which reactivation produces the different outcomes. PMID:26179232

  3. A two-dimensional matrix correction for off-axis portal dose prediction errors

    SciTech Connect

    Bailey, Daniel W.; Kumaraswamy, Lalith; Bakhtiari, Mohammad; Podgorsak, Matthew B.

    2013-05-15

    Purpose: This study presents a follow-up to a modified calibration procedure for portal dosimetry published by Bailey et al. ['An effective correction algorithm for off-axis portal dosimetry errors,' Med. Phys. 36, 4089-4094 (2009)]. A commercial portal dose prediction system exhibits disagreement of up to 15% (calibrated units) between measured and predicted images as off-axis distance increases. The previous modified calibration procedure accounts for these off-axis effects in most regions of the detecting surface, but is limited by the simplistic assumption of radial symmetry. Methods: We find that a two-dimensional (2D) matrix correction, applied to each calibrated image, accounts for off-axis prediction errors in all regions of the detecting surface, including those still problematic after the radial correction is performed. The correction matrix is calculated by quantitative comparison of predicted and measured images that span the entire detecting surface. The correction matrix was verified for dose-linearity, and its effectiveness was verified on a number of test fields. The 2D correction was employed to retrospectively examine 22 off-axis, asymmetric electronic-compensation breast fields, five intensity-modulated brain fields (moderate-high modulation) manipulated for far off-axis delivery, and 29 intensity-modulated clinical fields of varying complexity in the central portion of the detecting surface. Results: Employing the matrix correction to the off-axis test fields and clinical fields, predicted vs measured portal dose agreement improves by up to 15%, producing up to 10% better agreement than the radial correction in some areas of the detecting surface. Gamma evaluation analyses (3 mm, 3% global, 10% dose threshold) of predicted vs measured portal dose images demonstrate pass rate improvement of up to 75% with the matrix correction, producing pass rates that are up to 30% higher than those resulting from the radial correction technique alone. As in

  4. Prediction error and somatosensory insula activation in women recovered from anorexia nervosa

    PubMed Central

    Frank, Guido K.W.; Collier, Shaleise; Shott, Megan E.; O’Reilly, Randall C.

    2016-01-01

    Background 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. Methods Women recovered from restricting-type anorexia nervosa and healthy control women underwent fMRI during application of a prediction error taste reward learning paradigm. Results 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. Limitations This was a cross-sectional study, and the sample size was modest. Conclusion 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

  5. Absolute wealth and world region strongly predict overweight among women (ages 18-49) in 360 populations across 36 developing countries.

    PubMed

    Hruschka, Daniel J; Brewis, Alexandra A

    2013-07-01

    This paper proposes a benchmark for comparing SES gradients across countries, based on gross domestic product apportioned to members of differing wealth categories within countries. Using this approach, we estimate absolute wealth in 360 populations in 36 developing countries and model its relationship with overweight (BMI≥25) among non-pregnant women ages 18-49. A simple model based on absolute wealth alone strongly predicts odds of overweight (R(2)=0.59), a relationship that holds both between countries and between different groups in the same country (10 populations for each of 36 countries). Moreover, world region modifies this relationship, accounting for an additional 22% of variance (R(2)=0.81). This allows us to extract a basic pattern: rising rates of overweight in lower and middle income countries closely track increasing economic resources, and the shape of that gradient differs by region in systematic ways.

  6. Testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models

    NASA Astrophysics Data System (ADS)

    Kruse Christensen, Nikolaj; Christensen, Steen; Ferre, Ty Paul A.

    2016-05-01

    In spite of geophysics being used increasingly, it is often unclear how and when the integration of geophysical data and models can best improve the construction and predictive capability of groundwater models. This paper uses a newly developed HYdrogeophysical TEst-Bench (HYTEB) that is a collection of geological, groundwater and geophysical modeling and inversion software to demonstrate alternative uses of electromagnetic (EM) data for groundwater modeling in a hydrogeological environment consisting of various types of glacial deposits with typical hydraulic conductivities and electrical resistivities covering impermeable bedrock with low resistivity (clay). The synthetic 3-D reference system is designed so that there is a perfect relationship between hydraulic conductivity and electrical resistivity. For this system it is investigated to what extent groundwater model calibration and, often more importantly, model predictions can be improved by including in the calibration process electrical resistivity estimates obtained from TEM data. In all calibration cases, the hydraulic conductivity field is highly parameterized and the estimation is stabilized by (in most cases) geophysics-based regularization. For the studied system and inversion approaches it is found that resistivities estimated by sequential hydrogeophysical inversion (SHI) or joint hydrogeophysical inversion (JHI) should be used with caution as estimators of hydraulic conductivity or as regularization means for subsequent hydrological inversion. The limited groundwater model improvement obtained by using the geophysical data probably mainly arises from the way these data are used here: the alternative inversion approaches propagate geophysical estimation errors into the hydrologic model parameters. It was expected that JHI would compensate for this, but the hydrologic data were apparently insufficient to secure such compensation. With respect to reducing model prediction error, it depends on the type

  7. Harsh Parenting and Fearfulness in Toddlerhood Interact to Predict Amplitudes of Preschool Error-Related Negativity

    PubMed Central

    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

  8. Harsh parenting and fearfulness in toddlerhood interact to predict amplitudes of preschool error-related negativity.

    PubMed

    Brooker, Rebecca J; Buss, Kristin A

    2014-07-01

    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.

  9. Absolute Measurements of Macrophage Migration Inhibitory Factor and Interleukin-1-β mRNA Levels Accurately Predict Treatment Response in Depressed Patients

    PubMed Central

    Ferrari, Clarissa; Uher, Rudolf; Bocchio-Chiavetto, Luisella; Riva, Marco Andrea; Pariante, Carmine M.

    2016-01-01

    Background: Increased levels of inflammation have been associated with a poorer response to antidepressants in several clinical samples, but these findings have had been limited by low reproducibility of biomarker assays across laboratories, difficulty in predicting response probability on an individual basis, and unclear molecular mechanisms. Methods: Here we measured absolute mRNA values (a reliable quantitation of number of molecules) of Macrophage Migration Inhibitory Factor and interleukin-1β in a previously published sample from a randomized controlled trial comparing escitalopram vs nortriptyline (GENDEP) as well as in an independent, naturalistic replication sample. We then used linear discriminant analysis to calculate mRNA values cutoffs that best discriminated between responders and nonresponders after 12 weeks of antidepressants. As Macrophage Migration Inhibitory Factor and interleukin-1β might be involved in different pathways, we constructed a protein-protein interaction network by the Search Tool for the Retrieval of Interacting Genes/Proteins. Results: We identified cutoff values for the absolute mRNA measures that accurately predicted response probability on an individual basis, with positive predictive values and specificity for nonresponders of 100% in both samples (negative predictive value=82% to 85%, sensitivity=52% to 61%). Using network analysis, we identified different clusters of targets for these 2 cytokines, with Macrophage Migration Inhibitory Factor interacting predominantly with pathways involved in neurogenesis, neuroplasticity, and cell proliferation, and interleukin-1β interacting predominantly with pathways involved in the inflammasome complex, oxidative stress, and neurodegeneration. Conclusion: We believe that these data provide a clinically suitable approach to the personalization of antidepressant therapy: patients who have absolute mRNA values above the suggested cutoffs could be directed toward earlier access to more

  10. Temporal dynamics of prediction error processing during reward-based decision making.

    PubMed

    Philiastides, Marios G; Biele, Guido; Vavatzanidis, Niki; Kazzer, Philipp; Heekeren, Hauke R

    2010-10-15

    Adaptive decision making depends on the accurate representation of rewards associated with potential choices. These representations can be acquired with reinforcement learning (RL) mechanisms, which use the prediction error (PE, the difference between expected and received rewards) as a learning signal to update reward expectations. While EEG experiments have highlighted the role of feedback-related potentials during performance monitoring, important questions about the temporal sequence of feedback processing and the specific function of feedback-related potentials during reward-based decision making remain. Here, we hypothesized that feedback processing starts with a qualitative evaluation of outcome-valence, which is subsequently complemented by a quantitative representation of PE magnitude. Results of a model-based single-trial analysis of EEG data collected during a reversal learning task showed that around 220ms after feedback outcomes are initially evaluated categorically with respect to their valence (positive vs. negative). Around 300ms, and parallel to the maintained valence-evaluation, the brain also represents quantitative information about PE magnitude, thus providing the complete information needed to update reward expectations and to guide adaptive decision making. Importantly, our single-trial EEG analysis based on PEs from an RL model showed that the feedback-related potentials do not merely reflect error awareness, but rather quantitative information crucial for learning reward contingencies.

  11. Predicting the geographic distribution of a species from presence-only data subject to detection errors

    USGS Publications Warehouse

    Dorazio, Robert M.

    2012-01-01

    Several models have been developed to predict the geographic distribution of a species by combining measurements of covariates of occurrence at locations where the species is known to be present with measurements of the same covariates at other locations where species occurrence status (presence or absence) is unknown. In the absence of species detection errors, spatial point-process models and binary-regression models for case-augmented surveys provide consistent estimators of a species’ geographic distribution without prior knowledge of species prevalence. In addition, these regression models can be modified to produce estimators of species abundance that are asymptotically equivalent to those of the spatial point-process models. However, if species presence locations are subject to detection errors, neither class of models provides a consistent estimator of covariate effects unless the covariates of species abundance are distinct and independently distributed from the covariates of species detection probability. These analytical results are illustrated using simulation studies of data sets that contain a wide range of presence-only sample sizes. Analyses of presence-only data of three avian species observed in a survey of landbirds in western Montana and northern Idaho are compared with site-occupancy analyses of detections and nondetections of these species.

  12. Temporal dynamics of prediction error processing during reward-based decision making.

    PubMed

    Philiastides, Marios G; Biele, Guido; Vavatzanidis, Niki; Kazzer, Philipp; Heekeren, Hauke R

    2010-10-15

    Adaptive decision making depends on the accurate representation of rewards associated with potential choices. These representations can be acquired with reinforcement learning (RL) mechanisms, which use the prediction error (PE, the difference between expected and received rewards) as a learning signal to update reward expectations. While EEG experiments have highlighted the role of feedback-related potentials during performance monitoring, important questions about the temporal sequence of feedback processing and the specific function of feedback-related potentials during reward-based decision making remain. Here, we hypothesized that feedback processing starts with a qualitative evaluation of outcome-valence, which is subsequently complemented by a quantitative representation of PE magnitude. Results of a model-based single-trial analysis of EEG data collected during a reversal learning task showed that around 220ms after feedback outcomes are initially evaluated categorically with respect to their valence (positive vs. negative). Around 300ms, and parallel to the maintained valence-evaluation, the brain also represents quantitative information about PE magnitude, thus providing the complete information needed to update reward expectations and to guide adaptive decision making. Importantly, our single-trial EEG analysis based on PEs from an RL model showed that the feedback-related potentials do not merely reflect error awareness, but rather quantitative information crucial for learning reward contingencies. PMID:20510376

  13. Discrete coding of stimulus value, reward expectation, and reward prediction error in the dorsal striatum.

    PubMed

    Oyama, Kei; Tateyama, Yukina; Hernádi, István; Tobler, Philippe N; Iijima, Toshio; Tsutsui, Ken-Ichiro

    2015-11-01

    To investigate how the striatum integrates sensory information with reward information for behavioral guidance, we recorded single-unit activity in the dorsal striatum of head-fixed rats participating in a probabilistic Pavlovian conditioning task with auditory conditioned stimuli (CSs) in which reward probability was fixed for each CS but parametrically varied across CSs. We found that the activity of many neurons was linearly correlated with the reward probability indicated by the CSs. The recorded neurons could be classified according to their firing patterns into functional subtypes coding reward probability in different forms such as stimulus value, reward expectation, and reward prediction error. These results suggest that several functional subgroups of dorsal striatal neurons represent different kinds of information formed through extensive prior exposure to CS-reward contingencies. PMID:26378201

  14. The modulation of savouring by prediction error and its effects on choice

    PubMed Central

    Iigaya, Kiyohito; Story, Giles W; Kurth-Nelson, Zeb; Dolan, Raymond J; Dayan, Peter

    2016-01-01

    When people anticipate uncertain future outcomes, they often prefer to know their fate in advance. Inspired by an idea in behavioral economics that the anticipation of rewards is itself attractive, we hypothesized that this preference of advance information arises because reward prediction errors carried by such information can boost the level of anticipation. We designed new empirical behavioral studies to test this proposal, and confirmed that subjects preferred advance reward information more strongly when they had to wait for rewards for a longer time. We formulated our proposal in a reinforcement-learning model, and we showed that our model could account for a wide range of existing neuronal and behavioral data, without appealing to ambiguous notions such as an explicit value for information. We suggest that such boosted anticipation significantly drives risk-seeking behaviors, most pertinently in gambling. DOI: http://dx.doi.org/10.7554/eLife.13747.001 PMID:27101365

  15. Human dorsal striatum encodes prediction errors during observational learning of instrumental actions.

    PubMed

    Cooper, Jeffrey C; Dunne, Simon; Furey, Teresa; O'Doherty, John P

    2012-01-01

    The dorsal striatum plays a key role in the learning and expression of instrumental reward associations that are acquired through direct experience. However, not all learning about instrumental actions require direct experience. Instead, humans and other animals are also capable of acquiring instrumental actions by observing the experiences of others. In this study, we investigated the extent to which human dorsal striatum is involved in observational as well as experiential instrumental reward learning. Human participants were scanned with fMRI while they observed a confederate over a live video performing an instrumental conditioning task to obtain liquid juice rewards. Participants also performed a similar instrumental task for their own rewards. Using a computational model-based analysis, we found reward prediction errors in the dorsal striatum not only during the experiential learning condition but also during observational learning. These results suggest a key role for the dorsal striatum in learning instrumental associations, even when those associations are acquired purely by observing others.

  16. Prediction and standard error estimation for a finite universe total when a stratum is not sampled

    SciTech Connect

    Wright, T.

    1994-01-01

    In the context of a universe of trucks operating in the United States in 1990, this paper presents statistical methodology for estimating a finite universe total on a second occasion when a part of the universe is sampled and the remainder of the universe is not sampled. Prediction is used to compensate for the lack of data from the unsampled portion of the universe. The sample is assumed to be a subsample of an earlier sample where stratification is used on both occasions before sample selection. Accounting for births and deaths in the universe between the two points in time, the detailed sampling plan, estimator, standard error, and optimal sample allocation, are presented with a focus on the second occasion. If prior auxiliary information is available, the methodology is also applicable to a first occasion.

  17. Observing others stay or switch - How social prediction errors are integrated into reward reversal learning.

    PubMed

    Ihssen, Niklas; Mussweiler, Thomas; Linden, David E J

    2016-08-01

    Reward properties of stimuli can undergo sudden changes, and the detection of these 'reversals' is often made difficult by the probabilistic nature of rewards/punishments. Here we tested whether and how humans use social information (someone else's choices) to overcome uncertainty during reversal learning. We show a substantial social influence during reversal learning, which was modulated by the type of observed behavior. Participants frequently followed observed conservative choices (no switches after punishment) made by the (fictitious) other player but ignored impulsive choices (switches), even though the experiment was set up so that both types of response behavior would be similarly beneficial/detrimental (Study 1). Computational modeling showed that participants integrated the observed choices as a 'social prediction error' instead of ignoring or blindly following the other player. Modeling also confirmed higher learning rates for 'conservative' versus 'impulsive' social prediction errors. Importantly, this 'conservative bias' was boosted by interpersonal similarity, which in conjunction with the lack of effects observed in a non-social control experiment (Study 2) confirmed its social nature. A third study suggested that relative weighting of observed impulsive responses increased with increased volatility (frequency of reversals). Finally, simulations showed that in the present paradigm integrating social and reward information was not necessarily more adaptive to maximize earnings than learning from reward alone. Moreover, integrating social information increased accuracy only when conservative and impulsive choices were weighted similarly during learning. These findings suggest that to guide decisions in choice contexts that involve reward reversals humans utilize social cues conforming with their preconceptions more strongly than cues conflicting with them, especially when the other is similar. PMID:27128170

  18. Quantifying uncertainty for predictions with model error in non-Gaussian systems with intermittency

    NASA Astrophysics Data System (ADS)

    Branicki, Michal; Majda, Andrew J.

    2012-09-01

    This paper discusses a range of important mathematical issues arising in applications of a newly emerging stochastic-statistical framework for quantifying and mitigating uncertainties associated with prediction of partially observed and imperfectly modelled complex turbulent dynamical systems. The need for such a framework is particularly severe in climate science where the true climate system is vastly more complicated than any conceivable model; however, applications in other areas, such as neural networks and materials science, are just as important. The mathematical tools employed here rely on empirical information theory and fluctuation-dissipation theorems (FDTs) and it is shown that they seamlessly combine into a concise systematic framework for measuring and optimizing consistency and sensitivity of imperfect models. Here, we utilize a simple statistically exactly solvable ‘perfect’ system with intermittent hidden instabilities and with time-periodic features to address a number of important issues encountered in prediction of much more complex dynamical systems. These problems include the role and mitigation of model error due to coarse-graining, moment closure approximations, and the memory of initial conditions in producing short, medium and long-range predictions. Importantly, based on a suite of increasingly complex imperfect models of the perfect test system, we show that the predictive skill of the imperfect models and their sensitivity to external perturbations is improved by ensuring their consistency on the statistical attractor (i.e. the climate) with the perfect system. Furthermore, the discussed link between climate fidelity and sensitivity via the FDT opens up an enticing prospect of developing techniques for improving imperfect model sensitivity based on specific tests carried out in the training phase of the unperturbed statistical equilibrium/climate.

  19. Improving filtering and prediction of spatially extended turbulent systems with model errors through stochastic parameter estimation

    SciTech Connect

    Gershgorin, B.; Harlim, J. Majda, A.J.

    2010-01-01

    The filtering and predictive skill for turbulent signals is often limited by the lack of information about the true dynamics of the system and by our inability to resolve the assumed dynamics with sufficiently high resolution using the current computing power. The standard approach is to use a simple yet rich family of constant parameters to account for model errors through parameterization. This approach can have significant skill by fitting the parameters to some statistical feature of the true signal; however in the context of real-time prediction, such a strategy performs poorly when intermittent transitions to instability occur. Alternatively, we need a set of dynamic parameters. One strategy for estimating parameters on the fly is a stochastic parameter estimation through partial observations of the true signal. In this paper, we extend our newly developed stochastic parameter estimation strategy, the Stochastic Parameterization Extended Kalman Filter (SPEKF), to filtering sparsely observed spatially extended turbulent systems which exhibit abrupt stability transition from time to time despite a stable average behavior. For our primary numerical example, we consider a turbulent system of externally forced barotropic Rossby waves with instability introduced through intermittent negative damping. We find high filtering skill of SPEKF applied to this toy model even in the case of very sparse observations (with only 15 out of the 105 grid points observed) and with unspecified external forcing and damping. Additive and multiplicative bias corrections are used to learn the unknown features of the true dynamics from observations. We also present a comprehensive study of predictive skill in the one-mode context including the robustness toward variation of stochastic parameters, imperfect initial conditions and finite ensemble effect. Furthermore, the proposed stochastic parameter estimation scheme applied to the same spatially extended Rossby wave system demonstrates

  20. Revised Absolute Configuration of Sibiricumin A: Substituent Effects in Simplified Model Structures Used for Quantum Mechanical Predictions of Chiroptical Properties.

    PubMed

    Zhao, Dan; Li, Zheng-Qiu; Cao, Fei; Liang, Miao-Miao; Pittman, Charles U; Zhu, Hua-Jie; Li, Li; Yu, Shi-Shan

    2016-08-01

    This study discusses the choice of different simplified models used in computations of electronic circular dichroism (ECD) spectra and other chiroptical characteristics used to determine the absolute configuration (AC) of the complex natural product sibiricumin A. Sections of molecules containing one chiral center with one near an aromatic group have large effects on the ECD spectra. Conversely, when the phenyl group is present on a substituent without a nonstereogenic center, removal of this section will have little effect on ECD spectra. However, these nonstereogenic-center-containing sections have large effects on calculated optical rotations (OR) values since the OR value is more sensitive to the geometries of sections in a molecule. In this study, the wrong AC of sibiricumin A was reassigned as (7R,8S,1'R,7'R,8'S)-. Chirality 28:612-617, 2016. © 2016 Wiley Periodicals, Inc. PMID:27428019

  1. The fate of memory: Reconsolidation and the case of Prediction Error.

    PubMed

    Fernández, Rodrigo S; Boccia, Mariano M; Pedreira, María E

    2016-09-01

    The ability to make predictions based on stored information is a general coding strategy. A Prediction-Error (PE) is a mismatch between expected and current events. It was proposed as the process by which memories are acquired. But, our memories like ourselves are subject to change. Thus, an acquired memory can become active and update its content or strength by a labilization-reconsolidation process. Within the reconsolidation framework, PE drives the updating of consolidated memories. Moreover, memory features, such as strength and age, are crucial boundary conditions that limit the initiation of the reconsolidation process. In order to disentangle these boundary conditions, we review the role of surprise, classical models of conditioning, and their neural correlates. Several forms of PE were found to be capable of inducing memory labilization-reconsolidation. Notably, many of the PE findings mirror those of memory-reconsolidation, suggesting a strong link between these signals and memory process. Altogether, the aim of the present work is to integrate a psychological and neuroscientific analysis of PE into a general framework for memory-reconsolidation.

  2. The fate of memory: Reconsolidation and the case of Prediction Error.

    PubMed

    Fernández, Rodrigo S; Boccia, Mariano M; Pedreira, María E

    2016-09-01

    The ability to make predictions based on stored information is a general coding strategy. A Prediction-Error (PE) is a mismatch between expected and current events. It was proposed as the process by which memories are acquired. But, our memories like ourselves are subject to change. Thus, an acquired memory can become active and update its content or strength by a labilization-reconsolidation process. Within the reconsolidation framework, PE drives the updating of consolidated memories. Moreover, memory features, such as strength and age, are crucial boundary conditions that limit the initiation of the reconsolidation process. In order to disentangle these boundary conditions, we review the role of surprise, classical models of conditioning, and their neural correlates. Several forms of PE were found to be capable of inducing memory labilization-reconsolidation. Notably, many of the PE findings mirror those of memory-reconsolidation, suggesting a strong link between these signals and memory process. Altogether, the aim of the present work is to integrate a psychological and neuroscientific analysis of PE into a general framework for memory-reconsolidation. PMID:27287939

  3. EEG oscillatory patterns are associated with error prediction during music performance and are altered in musician's dystonia.

    PubMed

    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

  4. EFFECT OF MEASUREMENT ERRORS ON PREDICTED COSMOLOGICAL CONSTRAINTS FROM SHEAR PEAK STATISTICS WITH LARGE SYNOPTIC SURVEY TELESCOPE

    SciTech Connect

    Bard, D.; Chang, C.; Kahn, S. M.; Gilmore, K.; Marshall, S.; Kratochvil, J. M.; Huffenberger, K. M.; May, M.; AlSayyad, Y.; Connolly, A.; Gibson, R. R.; Jones, L.; Krughoff, S.; Ahmad, Z.; Bankert, J.; Grace, E.; Hannel, M.; Lorenz, S.; Haiman, Z.; Jernigan, J. G.; and others

    2013-09-01

    We study the effect of galaxy shape measurement errors on predicted cosmological constraints from the statistics of shear peak counts with the Large Synoptic Survey Telescope (LSST). We use the LSST Image Simulator in combination with cosmological N-body simulations to model realistic shear maps for different cosmological models. We include both galaxy shape noise and, for the first time, measurement errors on galaxy shapes. We find that the measurement errors considered have relatively little impact on the constraining power of shear peak counts for LSST.

  5. Improved model predictive control of resistive wall modes by error field estimator in EXTRAP T2R

    NASA Astrophysics Data System (ADS)

    Setiadi, A. C.; Brunsell, P. R.; Frassinetti, L.

    2016-12-01

    Many implementations of a model-based approach for toroidal plasma have shown better control performance compared to the conventional type of feedback controller. One prerequisite of model-based control is the availability of a control oriented model. This model can be obtained empirically through a systematic procedure called system identification. Such a model is used in this work to design a model predictive controller to stabilize multiple resistive wall modes in EXTRAP T2R reversed-field pinch. Model predictive control is an advanced control method that can optimize the future behaviour of a system. Furthermore, this paper will discuss an additional use of the empirical model which is to estimate the error field in EXTRAP T2R. Two potential methods are discussed that can estimate the error field. The error field estimator is then combined with the model predictive control and yields better radial magnetic field suppression.

  6. Inferring reward prediction errors in patients with schizophrenia: a dynamic reward task for reinforcement learning.

    PubMed

    Li, Chia-Tzu; Lai, Wen-Sung; Liu, Chih-Min; Hsu, Yung-Fong

    2014-01-01

    Abnormalities in the dopamine system have long been implicated in explanations of reinforcement learning and psychosis. The updated reward prediction error (RPE)-a discrepancy between the predicted and actual rewards-is thought to be encoded by dopaminergic neurons. Dysregulation of dopamine systems could alter the appraisal of stimuli and eventually lead to schizophrenia. Accordingly, the measurement of RPE provides a potential behavioral index for the evaluation of brain dopamine activity and psychotic symptoms. Here, we assess two features potentially crucial to the RPE process, namely belief formation and belief perseveration, via a probability learning task and reinforcement-learning modeling. Forty-five patients with schizophrenia [26 high-psychosis and 19 low-psychosis, based on their p1 and p3 scores in the positive-symptom subscales of the Positive and Negative Syndrome Scale (PANSS)] and 24 controls were tested in a feedback-based dynamic reward task for their RPE-related decision making. While task scores across the three groups were similar, matching law analysis revealed that the reward sensitivities of both psychosis groups were lower than that of controls. Trial-by-trial data were further fit with a reinforcement learning model using the Bayesian estimation approach. Model fitting results indicated that both psychosis groups tend to update their reward values more rapidly than controls. Moreover, among the three groups, high-psychosis patients had the lowest degree of choice perseveration. Lumping patients' data together, we also found that patients' perseveration appears to be negatively correlated (p = 0.09, trending toward significance) with their PANSS p1 + p3 scores. Our method provides an alternative for investigating reward-related learning and decision making in basic and clinical settings.

  7. On the improvement of neural cryptography using erroneous transmitted information with error prediction.

    PubMed

    Allam, Ahmed M; Abbas, Hazem M

    2010-12-01

    Neural cryptography deals with the problem of "key exchange" between two neural networks using the mutual learning concept. The two networks exchange their outputs (in bits) and the key between the two communicating parties is eventually represented in the final learned weights, when the two networks are said to be synchronized. Security of neural synchronization is put at risk if an attacker is capable of synchronizing with any of the two parties during the training process. Therefore, diminishing the probability of such a threat improves the reliability of exchanging the output bits through a public channel. The synchronization with feedback algorithm is one of the existing algorithms that enhances the security of neural cryptography. This paper proposes three new algorithms to enhance the mutual learning process. They mainly depend on disrupting the attacker confidence in the exchanged outputs and input patterns during training. The first algorithm is called "Do not Trust My Partner" (DTMP), which relies on one party sending erroneous output bits, with the other party being capable of predicting and correcting this error. The second algorithm is called "Synchronization with Common Secret Feedback" (SCSFB), where inputs are kept partially secret and the attacker has to train its network on input patterns that are different from the training sets used by the communicating parties. The third algorithm is a hybrid technique combining the features of the DTMP and SCSFB. The proposed approaches are shown to outperform the synchronization with feedback algorithm in the time needed for the parties to synchronize.

  8. Human Dorsal Striatum Encodes Prediction Errors during Observational Learning of Instrumental Actions

    PubMed Central

    Cooper, Jeffrey C.; Dunne, Simon; Furey, Teresa; O’Doherty, John P.

    2013-01-01

    The dorsal striatum plays a key role in the learning and expression of instrumental reward associations that are acquired through direct experience. However, not all learning about instrumental actions require direct experience. Instead, humans and other animals are also capable of acquiring instrumental actions by observing the experiences of others. In this study, we investigated the extent to which human dorsal striatum is involved in observational as well as experiential instrumental reward learning. Human participants were scanned with fMRI while they observed a confederate over a live video performing an instrumental conditioning task to obtain liquid juice rewards. Participants also performed a similar instrumental task for their own rewards. Using a computational model-based analysis, we found reward prediction errors in the dorsal striatum not only during the experiential learning condition but also during observational learning. These results suggest a key role for the dorsal striatum in learning instrumental associations, even when those associations are acquired purely by observing others. PMID:21812568

  9. Prediction errors to emotional expressions: the roles of the amygdala in social referencing.

    PubMed

    Meffert, Harma; Brislin, Sarah J; White, Stuart F; Blair, James R

    2015-04-01

    Social referencing paradigms in humans and observational learning paradigms in animals suggest that emotional expressions are important for communicating valence. It has been proposed that these expressions initiate stimulus-reinforcement learning. Relatively little is known about the role of emotional expressions in reinforcement learning, particularly in the context of social referencing. In this study, we examined object valence learning in the context of a social referencing paradigm. Participants viewed objects and faces that turned toward the objects and displayed a fearful, happy or neutral reaction to them, while judging the gender of these faces. Notably, amygdala activation was larger when the expressions following an object were less expected. Moreover, when asked, participants were both more likely to want to approach, and showed stronger amygdala responses to, objects associated with happy relative to objects associated with fearful expressions. This suggests that the amygdala plays two roles in social referencing: (i) initiating learning regarding the valence of an object as a function of prediction errors to expressions displayed toward this object and (ii) orchestrating an emotional response to the object when value judgments are being made regarding this object.

  10. [Prediction of spatial distribution of forest carbon storage in Heilongjiang Province using spatial error model].

    PubMed

    Liu, Chang; Li, Feng-Ri; Zhen, Zhen

    2014-10-01

    Abstract: Based on the data from Chinese National Forest Inventory (CNFI) and Key Ecological Benefit Forest Monitoring plots (5075 in total) in Heilongjiang Province in 2010 and concurrent meteorological data coming from 59 meteorological stations located in Heilongjiang, Jilin and Inner Mongolia, this paper established a spatial error model (SEM) by GeoDA using carbon storage as dependent variable and several independent variables, including diameter of living trees (DBH), number of trees per hectare (TPH), elevation (Elev), slope (Slope), and product of precipitation and temperature (Rain_Temp). Global Moran's I was computed for describing overall spatial autocorrelations of model results at different spatial scales. Local Moran's I was calculated at the optimal bandwidth (25 km) to present spatial distribution residuals. Intra-block spatial variances were computed to explain spatial heterogeneity of residuals. Finally, a spatial distribution map of carbon storage in Heilongjiang was visualized based on predictions. The results showed that the distribution of forest carbon storage in Heilongjiang had spatial effect and was significantly influenced by stand, topographic and meteorological factors, especially average DBH. SEM could solve the spatial autocorrelation and heterogeneity well. There were significant spatial differences in distribution of forest carbon storage. The carbon storage was mainly distributed in Zhangguangcai Mountain, Xiao Xing'an Mountain and Da Xing'an Mountain where dense, forests existed, rarely distributed in Songnen Plains, while Wanda Mountain had moderate-level carbon storage. PMID:25796882

  11. A resilient and quick data compression method of prediction errors for space missions

    NASA Astrophysics Data System (ADS)

    Portell, Jordi; Villafranca, Alberto G.; García-Berro, Enrique

    2009-08-01

    It has passed more than a decade since the Consultative Committee for Space Data Systems (CCSDS) made its recommendation for lossless data compression. The CCSDS standard is commonly used for scientific missions because it is a general-purpose lossless compression technique with a low computational cost which results in acceptable compression ratios. At the core of this compression algorithm it is the Rice coding method. Its performance rapidly degrades in the presence of noise and outliers, as the Rice coder is conceived for noiseless data following geometric distributions. To overcome this problem we present here a new coder, the so-called Prediction Error Coder (PEC), as well as its fully adaptive version (FAPEC) which we show is a reliable alternative to the CCSDS standard. We show that PEC and FAPEC achieve large compression ratios even when high levels of noise are present in the data. This is done testing our compressors with synthetic and real data, and comparing the compression ratios and processor requirements with those obtained using the CCSDS standard.

  12. Prediction errors to emotional expressions: the roles of the amygdala in social referencing.

    PubMed

    Meffert, Harma; Brislin, Sarah J; White, Stuart F; Blair, James R

    2015-04-01

    Social referencing paradigms in humans and observational learning paradigms in animals suggest that emotional expressions are important for communicating valence. It has been proposed that these expressions initiate stimulus-reinforcement learning. Relatively little is known about the role of emotional expressions in reinforcement learning, particularly in the context of social referencing. In this study, we examined object valence learning in the context of a social referencing paradigm. Participants viewed objects and faces that turned toward the objects and displayed a fearful, happy or neutral reaction to them, while judging the gender of these faces. Notably, amygdala activation was larger when the expressions following an object were less expected. Moreover, when asked, participants were both more likely to want to approach, and showed stronger amygdala responses to, objects associated with happy relative to objects associated with fearful expressions. This suggests that the amygdala plays two roles in social referencing: (i) initiating learning regarding the valence of an object as a function of prediction errors to expressions displayed toward this object and (ii) orchestrating an emotional response to the object when value judgments are being made regarding this object. PMID:24939872

  13. Altered neural reward and loss processing and prediction error signalling in depression

    PubMed Central

    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

  14. Altered neural reward and loss processing and prediction error signalling in depression.

    PubMed

    Ubl, Bettina; Kuehner, Christine; Kirsch, Peter; Ruttorf, Michaela; Diener, Carsten; Flor, Herta

    2015-08-01

    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

  15. Altered neural reward and loss processing and prediction error signalling in depression.

    PubMed

    Ubl, Bettina; Kuehner, Christine; Kirsch, Peter; Ruttorf, Michaela; Diener, Carsten; Flor, Herta

    2015-08-01

    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.

  16. Scaling of Perceptual Errors Can Predict the Shape of Neural Tuning Curves

    NASA Astrophysics Data System (ADS)

    Shouval, Harel Z.; Agarwal, Animesh; Gavornik, Jeffrey P.

    2013-04-01

    Weber’s law, first characterized in the 19th century, states that errors estimating the magnitude of perceptual stimuli scale linearly with stimulus intensity. This linear relationship is found in most sensory modalities, generalizes to temporal interval estimation, and even applies to some abstract variables. Despite its generality and long experimental history, the neural basis of Weber’s law remains unknown. This work presents a simple theory explaining the conditions under which Weber’s law can result from neural variability and predicts that the tuning curves of neural populations which adhere to Weber’s law will have a log-power form with parameters that depend on spike-count statistics. The prevalence of Weber’s law suggests that it might be optimal in some sense. We examine this possibility, using variational calculus, and show that Weber’s law is optimal only when observed real-world variables exhibit power-law statistics with a specific exponent. Our theory explains how physiology gives rise to the behaviorally characterized Weber’s law and may represent a general governing principle relating perception to neural activity.

  17. Prediction of DVH parameter changes due to setup errors for breast cancer treatment based on 2D portal dosimetry

    SciTech Connect

    Nijsten, S. M. J. J. G.; Elmpt, W. J. C. van; Mijnheer, B. J.; Minken, A. W. H.; Persoon, L. C. G. G.; Lambin, P.; Dekker, A. L. A. J.

    2009-01-15

    Electronic portal imaging devices (EPIDs) are increasingly used for portal dosimetry applications. In our department, EPIDs are clinically used for two-dimensional (2D) transit dosimetry. Predicted and measured portal dose images are compared to detect dose delivery errors caused for instance by setup errors or organ motion. The aim of this work is to develop a model to predict dose-volume histogram (DVH) changes due to setup errors during breast cancer treatment using 2D transit dosimetry. First, correlations between DVH parameter changes and 2D gamma parameters are investigated for different simulated setup errors, which are described by a binomial logistic regression model. The model calculates the probability that a DVH parameter changes more than a specific tolerance level and uses several gamma evaluation parameters for the planning target volume (PTV) projection in the EPID plane as input. Second, the predictive model is applied to clinically measured portal images. Predicted DVH parameter changes are compared to calculated DVH parameter changes using the measured setup error resulting from a dosimetric registration procedure. Statistical accuracy is investigated by using receiver operating characteristic (ROC) curves and values for the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values. Changes in the mean PTV dose larger than 5%, and changes in V{sub 90} and V{sub 95} larger than 10% are accurately predicted based on a set of 2D gamma parameters. Most pronounced changes in the three DVH parameters are found for setup errors in the lateral-medial direction. AUC, sensitivity, specificity, and negative predictive values were between 85% and 100% while the positive predictive values were lower but still higher than 54%. Clinical predictive value is decreased due to the occurrence of patient rotations or breast deformations during treatment, but the overall reliability of the predictive model remains high. Based on our

  18. Why don't we learn to accurately forecast feelings? How misremembering our predictions blinds us to past forecasting errors.

    PubMed

    Meyvis, Tom; Ratner, Rebecca K; Levav, Jonathan

    2010-11-01

    Why do affective forecasting errors persist in the face of repeated disconfirming evidence? Five studies demonstrate that people misremember their forecasts as consistent with their experience and thus fail to perceive the extent of their forecasting error. As a result, people do not learn from past forecasting errors and fail to adjust subsequent forecasts. In the context of a Super Bowl loss (Study 1), a presidential election (Studies 2 and 3), an important purchase (Study 4), and the consumption of candies (Study 5), individuals mispredicted their affective reactions to these experiences and subsequently misremembered their predictions as more accurate than they actually had been. The findings indicate that this recall error results from people's tendency to anchor on their current affective state when trying to recall their affective forecasts. Further, those who showed larger recall errors were less likely to learn to adjust their subsequent forecasts and reminding people of their actual forecasts enhanced learning. These results suggest that a failure to accurately recall one's past predictions contributes to the perpetuation of forecasting errors.

  19. Estimating Prediction Uncertainty from Geographical Information System Raster Processing: A User's Manual for the Raster Error Propagation Tool (REPTool)

    USGS Publications Warehouse

    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.

  20. Highly porous thermal protection materials: Modelling and prediction of the methodical experimental errors

    NASA Astrophysics Data System (ADS)

    Cherepanov, Valery V.; Alifanov, Oleg M.; Morzhukhina, Alena V.; Budnik, Sergey A.

    2016-11-01

    The formation mechanisms and the main factors affecting the systematic error of thermocouples were investigated. According to the results of experimental studies and mathematical modelling it was established that in highly porous heat resistant materials for aerospace application the thermocouple errors are determined by two competing mechanisms provided correlation between the errors and the difference between radiation and conduction heat fluxes. The comparative analysis was carried out and some features of the methodical error formation related to the distances from the heated surface were established.

  1. Effective Prediction of Errors by Non-native Speakers Using Decision Tree for Speech Recognition-Based CALL System

    NASA Astrophysics Data System (ADS)

    Wang, Hongcui; Kawahara, Tatsuya

    CALL (Computer Assisted Language Learning) systems using ASR (Automatic Speech Recognition) for second language learning have received increasing interest recently. However, it still remains a challenge to achieve high speech recognition performance, including accurate detection of erroneous utterances by non-native speakers. Conventionally, possible error patterns, based on linguistic knowledge, are added to the lexicon and language model, or the ASR grammar network. However, this approach easily falls in the trade-off of coverage of errors and the increase of perplexity. To solve the problem, we propose a method based on a decision tree to learn effective prediction of errors made by non-native speakers. An experimental evaluation with a number of foreign students learning Japanese shows that the proposed method can effectively generate an ASR grammar network, given a target sentence, to achieve both better coverage of errors and smaller perplexity, resulting in significant improvement in ASR accuracy.

  2. Absorbed in the task: Personality measures predict engagement during task performance as tracked by error negativity and asymmetrical frontal activity.

    PubMed

    Tops, Mattie; Boksem, Maarten A S

    2010-12-01

    We hypothesized that interactions between traits and context predict task engagement, as measured by the amplitude of the error-related negativity (ERN), performance, and relative frontal activity asymmetry (RFA). In Study 1, we found that drive for reward, absorption, and constraint independently predicted self-reported persistence. We hypothesized that, during a prolonged monotonous task, absorption would predict initial ERN amplitudes, constraint would delay declines in ERN amplitudes and deterioration of performance, and drive for reward would predict left RFA when a reward could be obtained. Study 2, employing EEG recordings, confirmed our predictions. The results showed that most traits that have in previous research been related to ERN amplitudes have a relationship with the motivational trait persistence in common. In addition, trait-context combinations that are likely associated with increased engagement predict larger ERN amplitudes and RFA. Together, these results support the hypothesis that engagement may be a common underlying factor predicting ERN amplitude.

  3. Online Visual Feedback during Error-Free Channel Trials Leads to Active Unlearning of Movement Dynamics: Evidence for Adaptation to Trajectory Prediction Errors

    PubMed Central

    Lago-Rodriguez, Angel; Miall, R. Chris

    2016-01-01

    Prolonged exposure to movement perturbations leads to creation of motor memories which decay towards previous states when the perturbations are removed. However, it remains unclear whether this decay is due only to a spontaneous and passive recovery of the previous state. It has recently been reported that activation of reinforcement-based learning mechanisms delays the onset of the decay. This raises the question whether other motor learning mechanisms may also contribute to the retention and/or decay of the motor memory. Therefore, we aimed to test whether mechanisms of error-based motor adaptation are active during the decay of the motor memory. Forty-five right-handed participants performed point-to-point reaching movements under an external dynamic perturbation. We measured the expression of the motor memory through error-clamped (EC) trials, in which lateral forces constrained movements to a straight line towards the target. We found greater and faster decay of the motor memory for participants who had access to full online visual feedback during these EC trials (Cursor group), when compared with participants who had no EC feedback regarding movement trajectory (Arc group). Importantly, we did not find between-group differences in adaptation to the external perturbation. In addition, we found greater decay of the motor memory when we artificially increased feedback errors through the manipulation of visual feedback (Augmented-Error group). Our results then support the notion of an active decay of the motor memory, suggesting that adaptive mechanisms are involved in correcting for the mismatch between predicted movement trajectories and actual sensory feedback, which leads to greater and faster decay of the motor memory. PMID:27721748

  4. The Vertical Error Characteristics of GOES-derived Winds: Description and Impact on Numerical Weather Prediction

    NASA Technical Reports Server (NTRS)

    Rao, P. Anil; Velden, Christopher S.; Braun, Scott A.; Einaudi, Franco (Technical Monitor)

    2001-01-01

    Errors in the height assignment of some satellite-derived winds exist because the satellites sense radiation emitted from a finite layer of the atmosphere rather than a specific level. Potential problems in data assimilation may arise because the motion of a measured layer is often represented by a single-level value. In this research, cloud and water vapor motion winds that are derived from the Geostationary Operational Environmental Satellites (GOES winds) are compared to collocated rawinsonde observations (RAOBs). An important aspect of this work is that in addition to comparisons at each assigned height, the GOES winds are compared to the entire profile of the collocated RAOB data to determine the vertical error characteristics of the GOES winds. The impact of these results on numerical weather prediction is then investigated. The comparisons at individual vector height assignments indicate that the error of the GOES winds range from approx. 3 to 10 m/s and generally increase with height. However, if taken as a percentage of the total wind speed, accuracy is better at upper levels. As expected, comparisons with the entire profile of the collocated RAOBs indicate that clear-air water vapor winds represent deeper layers than do either infrared or water vapor cloud-tracked winds. This is because in cloud-free regions the signal from water vapor features may result from emittance over a thicker layer. To further investigate characteristics of the clear-air water vapor winds, they are stratified into two categories that are dependent on the depth of the layer represented by the vector. It is found that if the vertical gradient of moisture is smooth and uniform from near the height assignment upwards, the clear-air water vapor wind tends to represent a relatively deep layer. The information from the comparisons is then used in numerical model simulations of two separate events to determine the forecast impacts. Four simulations are performed for each case: 1) A

  5. Preschool Speech Error Patterns Predict Articulation and Phonological Awareness Outcomes in Children with Histories of Speech Sound Disorders

    ERIC Educational Resources Information Center

    Preston, Jonathan L.; Hull, Margaret; Edwards, Mary Louise

    2013-01-01

    Purpose: To determine if speech error patterns in preschoolers with speech sound disorders (SSDs) predict articulation and phonological awareness (PA) outcomes almost 4 years later. Method: 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…

  6. Computer program to minimize prediction error in models from experiments with 16 hypercube points and 0 to 6 center points

    NASA Technical Reports Server (NTRS)

    Holms, A. G.

    1982-01-01

    A previous report described a backward deletion procedure of model selection that was optimized for minimum prediction error and which used a multiparameter combination of the F - distribution and an order statistics distribution of Cochran's. A computer program is described that applies the previously optimized procedure to real data. The use of the program is illustrated by examples.

  7. Implications of Indeterminate Factor-Error Covariances for Factor Construction, Prediction, and Determinacy

    ERIC Educational Resources Information Center

    Krijnen, Wim P.

    2006-01-01

    The assumptions of the model for factor analysis do not exclude a class of indeterminate covariances between factors and error variables (Grayson, 2003). The construction of all factors of the model for factor analysis is generalized to incorporate indeterminate factor-error covariances. A necessary and sufficient condition is given for…

  8. Beyond reward prediction errors: the role of dopamine in movement kinematics

    PubMed Central

    Barter, Joseph W.; Li, Suellen; Lu, Dongye; Bartholomew, Ryan A.; Rossi, Mark A.; Shoemaker, Charles T.; Salas-Meza, Daniel; Gaidis, Erin; Yin, Henry H.

    2015-01-01

    We recorded activity of dopamine (DA) neurons in the substantia nigra pars compacta in unrestrained mice while monitoring their movements with video tracking. Our approach allows an unbiased examination of the continuous relationship between single unit activity and behavior. Although DA neurons show characteristic burst firing following cue or reward presentation, as previously reported, their activity can be explained by the representation of actual movement kinematics. Unlike neighboring pars reticulata GABAergic output neurons, which can represent vector components of position, DA neurons represent vector components of velocity or acceleration. We found neurons related to movements in four directions—up, down, left, right. For horizontal movements, there is significant lateralization of neurons: the left nigra contains more rightward neurons, whereas the right nigra contains more leftward neurons. The relationship between DA activity and movement kinematics was found on both appetitive trials using sucrose and aversive trials using air puff, showing that these neurons belong to a velocity control circuit that can be used for any number of purposes, whether to seek reward or to avoid harm. In support of this conclusion, mimicry of the phasic activation of DA neurons with selective optogenetic stimulation could also generate movements. Contrary to the popular hypothesis that DA neurons encode reward prediction errors, our results suggest that nigrostriatal DA plays an essential role in controlling the kinematics of voluntary movements. We hypothesize that DA signaling implements gain adjustment for adaptive transition control, and describe a new model of the basal ganglia (BG) in which DA functions to adjust the gain of the transition controller. This model has significant implications for our understanding of movement disorders implicating DA and the BG. PMID:26074791

  9. Value and Prediction Error in Medial Frontal Cortex: Integrating the Single-Unit and Systems Levels of Analysis

    PubMed Central

    Silvetti, Massimo; Seurinck, Ruth; Verguts, Tom

    2011-01-01

    The role of the anterior cingulate cortex (ACC) in cognition has been extensively investigated with several techniques, including single-unit recordings in rodents and monkeys and EEG and fMRI in humans. This has generated a rich set of data and points of view. Important theoretical functions proposed for ACC are value estimation, error detection, error-likelihood estimation, conflict monitoring, and estimation of reward volatility. A unified view is lacking at this time, however. Here we propose that online value estimation could be the key function underlying these diverse data. This is instantiated in the reward value and prediction model (RVPM). The model contains units coding for the value of cues (stimuli or actions) and units coding for the differences between such values and the actual reward (prediction errors). We exposed the model to typical experimental paradigms from single-unit, EEG, and fMRI research to compare its overall behavior with the data from these studies. The model reproduced the ACC behavior of previous single-unit, EEG, and fMRI studies on reward processing, error processing, conflict monitoring, error-likelihood estimation, and volatility estimation, unifying the interpretations of the role performed by the ACC in some aspects of cognition. PMID:21886616

  10. Predicting error in detecting mammographic masses among radiology trainees using statistical models based on BI-RADS features

    SciTech Connect

    Grimm, Lars J. Ghate, Sujata V.; Yoon, Sora C.; Kim, Connie; Kuzmiak, Cherie M.; Mazurowski, Maciej A.

    2014-03-15

    Purpose: The purpose of this study is to explore Breast Imaging-Reporting and Data System (BI-RADS) features as predictors of individual errors made by trainees when detecting masses in mammograms. Methods: Ten radiology trainees and three expert breast imagers reviewed 100 mammograms comprised of bilateral medial lateral oblique and craniocaudal views on a research workstation. The cases consisted of normal and biopsy proven benign and malignant masses. For cases with actionable abnormalities, the experts recorded breast (density and axillary lymph nodes) and mass (shape, margin, and density) features according to the BI-RADS lexicon, as well as the abnormality location (depth and clock face). For each trainee, a user-specific multivariate model was constructed to predict the trainee's likelihood of error based on BI-RADS features. The performance of the models was assessed using area under the receive operating characteristic curves (AUC). Results: Despite the variability in errors between different trainees, the individual models were able to predict the likelihood of error for the trainees with a mean AUC of 0.611 (range: 0.502–0.739, 95% Confidence Interval: 0.543–0.680,p < 0.002). Conclusions: Patterns in detection errors for mammographic masses made by radiology trainees can be modeled using BI-RADS features. These findings may have potential implications for the development of future educational materials that are personalized to individual trainees.

  11. Value and prediction error in medial frontal cortex: integrating the single-unit and systems levels of analysis.

    PubMed

    Silvetti, Massimo; Seurinck, Ruth; Verguts, Tom

    2011-01-01

    The role of the anterior cingulate cortex (ACC) in cognition has been extensively investigated with several techniques, including single-unit recordings in rodents and monkeys and EEG and fMRI in humans. This has generated a rich set of data and points of view. Important theoretical functions proposed for ACC are value estimation, error detection, error-likelihood estimation, conflict monitoring, and estimation of reward volatility. A unified view is lacking at this time, however. Here we propose that online value estimation could be the key function underlying these diverse data. This is instantiated in the reward value and prediction model (RVPM). The model contains units coding for the value of cues (stimuli or actions) and units coding for the differences between such values and the actual reward (prediction errors). We exposed the model to typical experimental paradigms from single-unit, EEG, and fMRI research to compare its overall behavior with the data from these studies. The model reproduced the ACC behavior of previous single-unit, EEG, and fMRI studies on reward processing, error processing, conflict monitoring, error-likelihood estimation, and volatility estimation, unifying the interpretations of the role performed by the ACC in some aspects of cognition.

  12. Adjoint-Based, Three-Dimensional Error Prediction and Grid Adaptation

    NASA Technical Reports Server (NTRS)

    Park, Michael A.

    2002-01-01

    Engineering computational fluid dynamics (CFD) analysis and design applications focus on output functions (e.g., lift, drag). Errors in these output functions are generally unknown and conservatively accurate solutions may be computed. Computable error estimates can offer the possibility to minimize computational work for a prescribed error tolerance. Such an estimate can be computed by solving the flow equations and the linear adjoint problem for the functional of interest. The computational mesh can be modified to minimize the uncertainty of a computed error estimate. This robust mesh-adaptation procedure automatically terminates when the simulation is within a user specified error tolerance. This procedure for estimating and adapting to error in a functional is demonstrated for three-dimensional Euler problems. An adaptive mesh procedure that links to a Computer Aided Design (CAD) surface representation is demonstrated for wing, wing-body, and extruded high lift airfoil configurations. The error estimation and adaptation procedure yielded corrected functions that are as accurate as functions calculated on uniformly refined grids with ten times as many grid points.

  13. Offline modeling for product quality prediction of mineral processing using modeling error PDF shaping and entropy minimization.

    PubMed

    Ding, Jinliang; Chai, Tianyou; Wang, Hong

    2011-03-01

    This paper presents a novel offline modeling for product quality prediction of mineral processing which consists of a number of unit processes in series. The prediction of the product quality of the whole mineral process (i.e., the mixed concentrate grade) plays an important role and the establishment of its predictive model is a key issue for the plantwide optimization. For this purpose, a hybrid modeling approach of the mixed concentrate grade prediction is proposed, which consists of a linear model and a nonlinear model. The least-squares support vector machine is adopted to establish the nonlinear model. The inputs of the predictive model are the performance indices of each unit process, while the output is the mixed concentrate grade. In this paper, the model parameter selection is transformed into the shape control of the probability density function (PDF) of the modeling error. In this context, both the PDF-control-based and minimum-entropy-based model parameter selection approaches are proposed. Indeed, this is the first time that the PDF shape control idea is used to deal with system modeling, where the key idea is to turn model parameters so that either the modeling error PDF is controlled to follow a target PDF or the modeling error entropy is minimized. The experimental results using the real plant data and the comparison of the two approaches are discussed. The results show the effectiveness of the proposed approaches. PMID:21233046

  14. Offline modeling for product quality prediction of mineral processing using modeling error PDF shaping and entropy minimization.

    PubMed

    Ding, Jinliang; Chai, Tianyou; Wang, Hong

    2011-03-01

    This paper presents a novel offline modeling for product quality prediction of mineral processing which consists of a number of unit processes in series. The prediction of the product quality of the whole mineral process (i.e., the mixed concentrate grade) plays an important role and the establishment of its predictive model is a key issue for the plantwide optimization. For this purpose, a hybrid modeling approach of the mixed concentrate grade prediction is proposed, which consists of a linear model and a nonlinear model. The least-squares support vector machine is adopted to establish the nonlinear model. The inputs of the predictive model are the performance indices of each unit process, while the output is the mixed concentrate grade. In this paper, the model parameter selection is transformed into the shape control of the probability density function (PDF) of the modeling error. In this context, both the PDF-control-based and minimum-entropy-based model parameter selection approaches are proposed. Indeed, this is the first time that the PDF shape control idea is used to deal with system modeling, where the key idea is to turn model parameters so that either the modeling error PDF is controlled to follow a target PDF or the modeling error entropy is minimized. The experimental results using the real plant data and the comparison of the two approaches are discussed. The results show the effectiveness of the proposed approaches.

  15. The application of SHERPA (Systematic Human Error Reduction and Prediction Approach) in the development of compensatory cognitive rehabilitation strategies for stroke patients with left and right brain damage.

    PubMed

    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.

  16. Absolute calibration of optical flats

    DOEpatents

    Sommargren, Gary E.

    2005-04-05

    The invention uses the phase shifting diffraction interferometer (PSDI) to provide a true point-by-point measurement of absolute flatness over the surface of optical flats. Beams exiting the fiber optics in a PSDI have perfect spherical wavefronts. The measurement beam is reflected from the optical flat and passed through an auxiliary optic to then be combined with the reference beam on a CCD. The combined beams include phase errors due to both the optic under test and the auxiliary optic. Standard phase extraction algorithms are used to calculate this combined phase error. The optical flat is then removed from the system and the measurement fiber is moved to recombine the two beams. The newly combined beams include only the phase errors due to the auxiliary optic. When the second phase measurement is subtracted from the first phase measurement, the absolute phase error of the optical flat is obtained.

  17. Localization and delocalization errors in density functional theory and implications for band-gap prediction.

    PubMed

    Mori-Sánchez, Paula; Cohen, Aron J; Yang, Weitao

    2008-04-11

    The band-gap problem and other systematic failures of approximate exchange-correlation functionals are explained from an analysis of total energy for fractional charges. The deviation from the correct intrinsic linear behavior in finite systems leads to delocalization and localization errors in large and bulk systems. Functionals whose energy is convex for fractional charges such as the local density approximation display an incorrect apparent linearity in the bulk limit, due to the delocalization error. Concave functionals also have an incorrect apparent linearity in the bulk calculation, due to the localization error and imposed symmetry. This resolves an apparent paradox and identifies the physical nature of the error to be addressed to obtain accurate band gaps from density functional theory.

  18. Variability and Prediction of Measurement Error in Kolb's Learning Style Inventory Scores: A Reliability Generalization Study.

    ERIC Educational Resources Information Center

    Henson, Robin K.; Hwang, Dae-Yeop

    2002-01-01

    Conducted a reliability generalization study of Kolb's Learning Style Inventory (LSI; D. Kolb, 1976). Results for 34 studies indicate that internal consistency and test-retest reliabilities for LSI scores fluctuate considerably and contribute to deleterious cumulative measurement error. (SLD)

  19. Measurement and Predition Errors in Body Composition Assessment and the Search for the Perfect Prediction Equation.

    ERIC Educational Resources Information Center

    Katch, Frank I.; Katch, Victor L.

    1980-01-01

    Sources of error in body composition assessment by laboratory and field methods can be found in hydrostatic weighing, residual air volume, skinfolds, and circumferences. Statistical analysis can and should be used in the measurement of body composition. (CJ)

  20. Aberrant synchrony in the somatosensory cortices predicts motor performance errors in children with cerebral palsy.

    PubMed

    Kurz, Max J; Heinrichs-Graham, Elizabeth; Arpin, David J; Becker, Katherine M; Wilson, Tony W

    2014-02-01

    Cerebral palsy (CP) results from a perinatal brain injury that often results in sensory impairments and greater errors in motor performance. Although these impairments have been well catalogued, the relationship between sensory processing networks and errors in motor performance has not been well explored. Children with CP and typically developing age-matched controls participated in this investigation. We used high-density magnetoencephalography to measure event-related oscillatory changes in the somatosensory cortices following tactile stimulation to the bottom of the foot. In addition, we quantified the amount of variability or errors in the isometric ankle joint torques as these children attempted to match a target. Our results showed that neural populations in the somatosensory cortices of children with CP were desynchronized by the tactile stimulus, whereas those of typically developing children were clearly synchronized. Such desynchronization suggests that children with CP were unable to fully integrate the external stimulus into ongoing sensorimotor computations. Our results also indicated that children with CP had a greater amount of errors in their motor output when they attempted to match the target force, and this amount of error was negatively correlated with the degree of synchronization present in the somatosensory cortices. These results are the first to show that the motor performance errors of children with CP are linked with neural synchronization within the somatosensory cortices.

  1. Error metrics for predicting discrimination of original and spectrally altered musical instrument sounds

    NASA Astrophysics Data System (ADS)

    Beauchamp, James W.; Horner, Andrew

    2003-10-01

    The correspondence of various error metrics to human discrimination data was investigated. Time-varying harmonic amplitude data were obtained from spectral analysis of eight musical instrument sounds (bassoon, clarinet, flute, horn, oboe, saxophone, trumpet, and violin). The data were altered using fixed random multipliers on the harmonic amplitudes, and the sounds were additively resynthesized with estimated average spectral errors ranging from 1% to 50%. Listeners attempted to discriminate the randomly altered sounds from reference sounds resynthesized from the original data. Then, various error metrics were used to calculate the spectral differences between the original and altered sounds, and the R2 correspondence between the error metrics and the discrimination data was measured. A relative-amplitude spectral error metric gave the best correspondence to average subject discrimination data, capturing over 90% of the variation relative to a Fourth-order regression curve, although other formulas gave similar results. Error metrics which used a small number of representative analysis frames gave results which compared favorably to using all frames of the analysis.

  2. A nuclear plant accident diagnosis method to support prediction of errors of commission

    SciTech Connect

    Chang, Y. H. J.; Coyne, K.; Mosleh, A.

    2006-07-01

    The identification and mitigation of operator errors of commission (EOCs) continue to be a major focus of nuclear plant human reliability research. Current Human Reliability Analysis (HRA) methods for predicting EOCs generally rely on the availability of operating procedures or extensive use of expert judgment. Consequently, an analysis for EOCs cannot easily be performed for actions that may be taken outside the scope of the operating procedures. Additionally, current HRA techniques rarely capture an operator's 'creative' problem-solving behavior. However, a nuclear plant operator knowledge base developed for the use with the IDAC (Information, Decision, and Action in Crew context) cognitive model shows potential for addressing these limitations. This operator knowledge base currently includes an event-symptom diagnosis matrix for a pressurized water reactor (PWR) nuclear plant. The diagnosis matrix defines a probabilistic relationship between observed symptoms and plant events that models the operator's heuristic process for classifying a plant state. Observed symptoms are obtained from a dynamic thermal-hydraulic plant model and can be modified to account for the limitations of human perception and cognition. A fuzzy-logic inference technique is used to calculate the operator's confidence, or degree of belief, that a given plant event has occurred based on the observed symptoms. An event diagnosis can be categorized as either: (a) a generalized flow imbalance of basic thermal-hydraulic properties (e.g., a mass or energy flow imbalance in the reactor coolant system), or (b) a specific event type, such as a steam generator tube rupture or a reactor trip. When an operator is presented with incomplete or contradictory information, this diagnosis approach provides a means to identify situations where an operator might be misled to perform unsafe actions based on an incorrect diagnosis. This knowledge base model could also support identification of potential EOCs when

  3. Exploring the Fundamental Dynamics of Error-Based Motor Learning Using a Stationary Predictive-Saccade Task

    PubMed Central

    Wong, Aaron L.; Shelhamer, Mark

    2011-01-01

    The maintenance of movement accuracy uses prior performance errors to correct future motor plans; this motor-learning process ensures that movements remain quick and accurate. The control of predictive saccades, in which anticipatory movements are made to future targets before visual stimulus information becomes available, serves as an ideal paradigm to analyze how the motor system utilizes prior errors to drive movements to a desired goal. Predictive saccades constitute a stationary process (the mean and to a rough approximation the variability of the data do not vary over time, unlike a typical motor adaptation paradigm). This enables us to study inter-trial correlations, both on a trial-by-trial basis and across long blocks of trials. Saccade errors are found to be corrected on a trial-by-trial basis in a direction-specific manner (the next saccade made in the same direction will reflect a correction for errors made on the current saccade). Additionally, there is evidence for a second, modulating process that exhibits long memory. That is, performance information, as measured via inter-trial correlations, is strongly retained across a large number of saccades (about 100 trials). Together, this evidence indicates that the dynamics of motor learning exhibit complexities that must be carefully considered, as they cannot be fully described with current state-space (ARMA) modeling efforts. PMID:21966462

  4. Unscented predictive variable structure filter for satellite attitude estimation with model errors when using low precision sensors

    NASA Astrophysics Data System (ADS)

    Cao, Lu; Li, Hengnian

    2016-10-01

    For the satellite attitude estimation problem, the serious model errors always exist and hider the estimation performance of the Attitude Determination and Control System (ACDS), especially for a small satellite with low precision sensors. To deal with this problem, a new algorithm for the attitude estimation, referred to as the unscented predictive variable structure filter (UPVSF) is presented. This strategy is proposed based on the variable structure control concept and unscented transform (UT) sampling method. It can be implemented in real time with an ability to estimate the model errors on-line, in order to improve the state estimation precision. In addition, the model errors in this filter are not restricted only to the Gaussian noises; therefore, it has the advantages to deal with the various kinds of model errors or noises. It is anticipated that the UT sampling strategy can further enhance the robustness and accuracy of the novel UPVSF. Numerical simulations show that the proposed UPVSF is more effective and robustness in dealing with the model errors and low precision sensors compared with the traditional unscented Kalman filter (UKF).

  5. Extended range (10-30 days) heavy rain forecasting study based on a nonlinear cross-prediction error model

    NASA Astrophysics Data System (ADS)

    Xia, Zhiye; Chen, Hongbin; Xu, Lisheng; Wang, Yongqian

    2015-12-01

    Extended range (10-30 d) heavy rain forecasting is difficult but performs an important function in disaster prevention and mitigation. In this paper, a nonlinear cross prediction error (NCPE) algorithm that combines nonlinear dynamics and statistical methods is proposed. The method is based on phase space reconstruction of chaotic single-variable time series of precipitable water and is tested in 100 global cases of heavy rain. First, nonlinear relative dynamic error for local attractor pairs is calculated at different stages of the heavy rain process, after which the local change characteristics of the attractors are analyzed. Second, the eigen-peak is defined as a prediction indicator based on an error threshold of about 1.5, and is then used to analyze the forecasting validity period. The results reveal that the prediction indicator features regarded as eigenpeaks for heavy rain extreme weather are all reflected consistently, without failure, based on the NCPE model; the prediction validity periods for 1-2 d, 3-9 d and 10-30 d are 4, 22 and 74 cases, respectively, without false alarm or omission. The NCPE model developed allows accurate forecasting of heavy rain over an extended range of 10-30 d and has the potential to be used to explore the mechanisms involved in the development of heavy rain according to a segmentation scale. This novel method provides new insights into extended range forecasting and atmospheric predictability, and also allows the creation of multi-variable chaotic extreme weather prediction models based on high spatiotemporal resolution data.

  6. A neural reward prediction error revealed by a meta-analysis of ERPs using great grand averages.

    PubMed

    Sambrook, Thomas D; Goslin, Jeremy

    2015-01-01

    Economic approaches to decision making assume that people attach values to prospective goods and act to maximize their obtained value. Neuroeconomics strives to observe these values directly in the brain. A widely used valuation term in formal learning and decision-making models is the reward prediction error: the value of an outcome relative to its expected value. An influential theory (Holroyd & Coles, 2002) claims that an electrophysiological component, feedback related negativity (FRN), codes a reward prediction error in the human brain. Such a component should be sensitive to both the prior likelihood of reward and its magnitude on receipt. A number of studies have found the FRN to be insensitive to reward magnitude, thus questioning the Holroyd and Coles account. However, because of marked inconsistencies in how the FRN is measured, a meaningful synthesis of this evidence is highly problematic. We conducted a meta-analysis of the FRN's response to both reward magnitude and likelihood using a novel method in which published effect sizes were disregarded in favor of direct measurement of the published waveforms themselves, with these waveforms then averaged to produce "great grand averages." Under this standardized measure, the meta-analysis revealed strong effects of magnitude and likelihood on the FRN, consistent with it encoding a reward prediction error. In addition, it revealed strong main effects of reward magnitude and likelihood across much of the waveform, indicating sensitivity to unsigned prediction errors or "salience." The great grand average technique is proposed as a general method for meta-analysis of event-related potential (ERP). PMID:25495239

  7. A neural reward prediction error revealed by a meta-analysis of ERPs using great grand averages.

    PubMed

    Sambrook, Thomas D; Goslin, Jeremy

    2015-01-01

    Economic approaches to decision making assume that people attach values to prospective goods and act to maximize their obtained value. Neuroeconomics strives to observe these values directly in the brain. A widely used valuation term in formal learning and decision-making models is the reward prediction error: the value of an outcome relative to its expected value. An influential theory (Holroyd & Coles, 2002) claims that an electrophysiological component, feedback related negativity (FRN), codes a reward prediction error in the human brain. Such a component should be sensitive to both the prior likelihood of reward and its magnitude on receipt. A number of studies have found the FRN to be insensitive to reward magnitude, thus questioning the Holroyd and Coles account. However, because of marked inconsistencies in how the FRN is measured, a meaningful synthesis of this evidence is highly problematic. We conducted a meta-analysis of the FRN's response to both reward magnitude and likelihood using a novel method in which published effect sizes were disregarded in favor of direct measurement of the published waveforms themselves, with these waveforms then averaged to produce "great grand averages." Under this standardized measure, the meta-analysis revealed strong effects of magnitude and likelihood on the FRN, consistent with it encoding a reward prediction error. In addition, it revealed strong main effects of reward magnitude and likelihood across much of the waveform, indicating sensitivity to unsigned prediction errors or "salience." The great grand average technique is proposed as a general method for meta-analysis of event-related potential (ERP).

  8. Real-time prediction of atmospheric Lagrangian coherent structures based on forecast data: An application and error analysis

    NASA Astrophysics Data System (ADS)

    BozorgMagham, Amir E.; Ross, Shane D.; Schmale, David G.

    2013-09-01

    The language of Lagrangian coherent structures (LCSs) provides a new means for studying transport and mixing of passive particles advected by an atmospheric flow field. Recent observations suggest that LCSs govern the large-scale atmospheric motion of airborne microorganisms, paving the way for more efficient models and management strategies for the spread of infectious diseases affecting plants, domestic animals, and humans. In addition, having reliable predictions of the timing of hyperbolic LCSs may contribute to improved aerobiological sampling of microorganisms with unmanned aerial vehicles and LCS-based early warning systems. Chaotic atmospheric dynamics lead to unavoidable forecasting errors in the wind velocity field, which compounds errors in LCS forecasting. In this study, we reveal the cumulative effects of errors of (short-term) wind field forecasts on the finite-time Lyapunov exponent (FTLE) fields and the associated LCSs when realistic forecast plans impose certain limits on the forecasting parameters. Objectives of this paper are to (a) quantify the accuracy of prediction of FTLE-LCS features and (b) determine the sensitivity of such predictions to forecasting parameters. Results indicate that forecasts of attracting LCSs exhibit less divergence from the archive-based LCSs than the repelling features. This result is important since attracting LCSs are the backbone of long-lived features in moving fluids. We also show under what circumstances one can trust the forecast results if one merely wants to know if an LCS passed over a region and does not need to precisely know the passage time.

  9. Individual Differences in Working Memory Capacity Predict Action Monitoring and the Error-Related Negativity

    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…

  10. Genetic marker of norepinephrine synthesis predicts individual differences in post-error slowing: a pilot study.

    PubMed

    Colzato, Lorenza S; de Rover, Mischa; van den Wildenberg, Wery P M; Nieuwenhuis, Sander

    2013-11-01

    When our brain detects the commission of an error, we slow down immediately thereafter: a phenomenon called post-error slowing (PES). Some researchers have speculated that slowing after unexpected errors or negative feedback is related to the activity of the neuromodulatory locus coeruleus-norepinephrine system. In the present pilot study, we tested whether individual differences in the size of PES are related to differences in genetic predisposition related to norepinephrine synthesis. In a sample of 100 healthy adults, we studied the dependency of an individual's size of PES on the DBH5'-ins/del polymorphism-a variation in the DBH gene associated with the production of the enzyme dopamine β-hydroxylase, which catalyzes the conversion of dopamine to norepinephrine. DBH5'-ins/del heterozygotes, who have intermediate levels of plasma DβH activity, showed increased PES in a Simon task compared to del/del homozygotes and ins/ins homozygotes, who have low and high levels of plasma DβH activity, respectively. This outcome pattern presents preliminary evidence that the size of PES varies with DβH activity and, presumably, NE release according to an inverted U-shape: intermediate levels of DβH activity and NE release are associated with larger post-error adjustments. PMID:23962674

  11. Prediction of absolute risk of fragility fracture at 10 years in a Spanish population: validation of the WHO FRAX ™ tool in Spain

    PubMed Central

    2011-01-01

    Background Age-related bone loss is asymptomatic, and the morbidity of osteoporosis is secondary to the fractures that occur. Common sites of fracture include the spine, hip, forearm and proximal humerus. Fractures at the hip incur the greatest morbidity and mortality and give rise to the highest direct costs for health services. Their incidence increases exponentially with age. Independently changes in population demography, the age - and sex- specific incidence of osteoporotic fractures appears to be increasing in developing and developed countries. This could mean more than double the expected burden of osteoporotic fractures in the next 50 years. Methods/Design To assess the predictive power of the WHO FRAX™ tool to identify the subjects with the highest absolute risk of fragility fracture at 10 years in a Spanish population, a predictive validation study of the tool will be carried out. For this purpose, the participants recruited by 1999 will be assessed. These were referred to scan-DXA Department from primary healthcare centres, non hospital and hospital consultations. Study population: Patients attended in the national health services integrated into a FRIDEX cohort with at least one Dual-energy X-ray absorptiometry (DXA) measurement and one extensive questionnaire related to fracture risk factors. Measurements: At baseline bone mineral density measurement using DXA, clinical fracture risk factors questionnaire, dietary calcium intake assessment, history of previous fractures, and related drugs. Follow up by telephone interview to know fragility fractures in the 10 years with verification in electronic medical records and also to know the number of falls in the last year. The absolute risk of fracture will be estimated using the FRAX™ tool from the official web site. Discussion Since more than 10 years ago numerous publications have recognised the importance of other risk factors for new osteoporotic fractures in addition to low BMD. The extension of a

  12. What Dominates the Error in the CaO Diatomic Bond Energy Predicted by Various Approximate Exchange-Correlation Functionals?

    PubMed

    Yu, Haoyu; Truhlar, Donald G

    2014-06-10

    In order to understand what governs the accuracy of approximate exchange-correlation functionals for intrinsically multiconfigurational systems containing metal atoms, the properties of the ground electronic state of CaO have been studied in detail. We first applied the T1, TAE(T), B1, and M diagnostics to CaO and confirmed that CaO is an intrinsically multiconfigurational system. Then, we compared the bond dissociation energies (BDEs) of CaO as calculated by 49 exchange-correlation functionals, three exchange-only functionals, and the HF method. To analyze the error in the BDEs for the various functionals, we decomposed each calculated BDE into four components, in particular the ionization potential, the electron affinity, the atomic excitation energy of the metal cation to prepare the valence state, and the interaction energy between prepared states. We found that the dominant error occurs in the calculated atomic excitation energy of the cation. Third, we compared dipole moments of CaO as calculated by the 53 methods, and we analyzed the dipole moments in terms of partial atomic charges to understand the contribution of ionic bonding and how it is affected by errors in the calculated ionization potential of the metal atom. We then analyzed the dipole moment in terms of the charge distribution among orbitals, and we found that the orbital charge distribution does not correlate well with the difference between the calculated ionization potential and electron affinity. Fourth, we examined the potential curves and internuclear distance dependence of the orbital energies of the lowest-energy CaO singlet and triplet states to analyze the near-degeneracy aspect of the correlation energy. The most important conclusion is that the error tends to be dominated by the error in the relative energies of s and d orbitals in Ca(+), and the most popular density functionals predict this excitation energy poorly. Thus, even if they were to predict the BDE reasonably well, it would

  13. Sensorimotor feedback based on task-relevant error robustly predicts temporal recruitment and multidirectional tuning of muscle synergies

    PubMed Central

    Safavynia, Seyed A.

    2013-01-01

    We hypothesized that motor outputs are hierarchically organized such that descending temporal commands based on desired task-level goals flexibly recruit muscle synergies that specify the spatial patterns of muscle coordination that allow the task to be achieved. According to this hypothesis, it should be possible to predict the patterns of muscle synergy recruitment based on task-level goals. We demonstrated that the temporal recruitment of muscle synergies during standing balance control was robustly predicted across multiple perturbation directions based on delayed sensorimotor feedback of center of mass (CoM) kinematics (displacement, velocity, and acceleration). The modulation of a muscle synergy's recruitment amplitude across perturbation directions was predicted by the projection of CoM kinematic variables along the preferred tuning direction(s), generating cosine tuning functions. Moreover, these findings were robust in biphasic perturbations that initially imposed a perturbation in the sagittal plane and then, before sagittal balance was recovered, perturbed the body in multiple directions. Therefore, biphasic perturbations caused the initial state of the CoM to differ from the desired state, and muscle synergy recruitment was predicted based on the error between the actual and desired upright state of the CoM. These results demonstrate that that temporal motor commands to muscle synergies reflect task-relevant error as opposed to sensory inflow. The proposed hierarchical framework may represent a common principle of motor control across motor tasks and levels of the nervous system, allowing motor intentions to be transformed into motor actions. PMID:23100133

  14. Peripheral blood absolute lymphocyte/monocyte ratio during rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone treatment cycles predicts clinical outcomes in diffuse large B-cell lymphoma.

    PubMed

    Porrata, Luis F; Ristow, Kay M; Habermann, Thomas M; Witzig, Thomas E; Colgan, Joseph P; Inwards, David J; Ansell, Stephen M; Micallef, Ivana N; Johnston, Patrick B; Nowakowski, Grzegorz; Thompson, Carrie A; Markovic, Svetomir N

    2014-12-01

    A limitation of the prognostic factor peripheral blood absolute lymphocyte/monocyte ratio (ALC/AMC) at diagnosis in diffuse large B-cell lymphoma (DLBCL) is its inability to sequentially assess the host/tumor microenvironment interaction and clinical outcomes during treatment. Therefore, we studied the ALC/AMC ratio at each rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone (R-CHOP) cycle as a predictor for survival. We studied 107 consecutive patients with DLBCL diagnosed, treated only with R-CHOP and followed at the Mayo Clinic. Unsupervised hierarchical clustering identified four clusters based on the patterns of ALC/AMC ratio recovery during cycles. The most inferior survival was seen in the cluster with ALC/AMC ratio < 1.1 in all cycles. By multivariate analysis, ALC/AMC ratio < 1.1 during all cycles was an independent predictor for inferior overall survival and progression-free survival. The ALC/AMC ratio during R-CHOP cycles predicts survival and provides a platform to develop therapeutic modalities to manipulate the ALC/AMC ratio during R-CHOP cycles to improve DLBCL clinical outcomes.

  15. Spatial measurement error and correction by spatial SIMEX in linear regression models when using predicted air pollution exposures.

    PubMed

    Alexeeff, Stacey E; Carroll, Raymond J; Coull, Brent

    2016-04-01

    Spatial modeling of air pollution exposures is widespread in air pollution epidemiology research as a way to improve exposure assessment. However, there are key sources of exposure model uncertainty when air pollution is modeled, including estimation error and model misspecification. We examine the use of predicted air pollution levels in linear health effect models under a measurement error framework. For the prediction of air pollution exposures, we consider a universal Kriging framework, which may include land-use regression terms in the mean function and a spatial covariance structure for the residuals. We derive the bias induced by estimation error and by model misspecification in the exposure model, and we find that a misspecified exposure model can induce asymptotic bias in the effect estimate of air pollution on health. We propose a new spatial simulation extrapolation (SIMEX) procedure, and we demonstrate that the procedure has good performance in correcting this asymptotic bias. We illustrate spatial SIMEX in a study of air pollution and birthweight in Massachusetts.

  16. Characterizing the diurnal patterns of errors in the prediction of evapotranspiration by several land-surface models: An NACP analysis

    NASA Astrophysics Data System (ADS)

    Matheny, Ashley M.; Bohrer, Gil; Stoy, Paul C.; Baker, Ian T.; Black, Andy T.; Desai, Ankur R.; Dietze, Michael C.; Gough, Chris M.; Ivanov, Valeriy Y.; Jassal, Rachhpal S.; Novick, Kimberly A.; Schäfer, Karina V. R.; Verbeeck, Hans

    2014-07-01

    Land-surface models use different formulations of stomatal conductance and plant hydraulics, and it is unclear which type of model best matches the observed surface-atmosphere water flux. We use the North American Carbon Program data set of latent heat flux (LE) measurements from 25 sites and predictions from 9 models to evaluate models' ability to resolve subdaily dynamics of transpiration. Despite overall good forecast at the seasonal scale, the models have difficulty resolving the dynamics of intradaily hysteresis. The majority of models tend to underestimate LE in the prenoon hours and overestimate in the evening. We hypothesize that this is a result of unresolved afternoon stomatal closure due to hydrodynamic stresses. Although no model or stomata parameterization was consistently best or worst in terms of ability to predict LE, errors in model-simulated LE were consistently largest and most variable when soil moisture was moderate and vapor pressure deficit was moderate to limiting. Nearly all models demonstrate a tendency to underestimate the degree of maximum hysteresis which, across all sites studied, is most pronounced during moisture-limited conditions. These diurnal error patterns are consistent with models' diminished ability to accurately simulate the natural hysteresis of transpiration. We propose that the lack of representation of plant hydrodynamics is, in part, responsible for these error patterns.

  17. Can the apparent adaptation of dopamine neurons' mismatch sensitivities be reconciled with their computation of reward prediction errors?

    PubMed

    Tan, Can Ozan; Anderson, Eric; Dranias, Mark; Bullock, Daniel

    2008-06-13

    According to modern reinforcement learning theories, midbrain dopamine (DA) neurons are part of an adaptive system within which learned expectations filter reward-related signals to enable computation of reward prediction errors (RPEs). Recent electrophysiological data on DA neuron responses to probabilistic reward schedules inspired the idea that DA neurons might be adapting their mismatch sensitivities to reflect variances of expected rewards. Taken literally as a mathematical hypothesis, this idea contradicts reinforcement learning theory, and most computational models of basal ganglia learning. Here, we report a qualitative mathematical derivation of the implications of a generic class of circuit models for learning to compute RPEs. This analysis and concordant circuit simulations, both of which predict DA neuron responses on probabilistic schedules, support a reinterpretation of the electrophysiological data that is fully compatible with the examined class of RPE models. This reinterpretation implies a novel and readily testable prediction.

  18. Mitigation of Atmospheric Delay in SAR Absolute Ranging Using Global Numerical Weather Prediction Data: Corner Reflector Experiments at 3 Different Test Sites

    NASA Astrophysics Data System (ADS)

    Cong, Xiaoying; Balss, Ulrich; Eineder, Michael

    2015-04-01

    The atmospheric delay due to vertical stratification, the so-called stratified atmospheric delay, has a great impact on both interferometric and absolute range measurements. In our current researches [1][2][3], centimeter-range accuracy has been proven based on Corner Reflector (CR) based measurements by applying atmospheric delay correction using the Zenith Path Delay (ZPD) corrections derived from nearby Global Positioning System (GPS) stations. For a global usage, an effective method has been introduced to estimate the stratified delay based on global 4-dimensional Numerical Weather Prediction (NWP) products: the direct integration method [4][5]. Two products, ERA-Interim and operational data, provided by European Centre for Medium-Range Weather Forecast (ECMWF) are used to integrate the stratified delay. In order to access the integration accuracy, a validation approach is investigated based on ZPD derived from six permanent GPS stations located in different meteorological conditions. Range accuracy at centimeter level is demonstrated using both ECMWF products. Further experiments have been carried out in order to determine the best interpolation method by analyzing the temporal and spatial correlation of atmospheric delay using both ECMWF and GPS ZPD. Finally, the integrated atmospheric delays in slant direction (Slant Path Delay, SPD) have been applied instead of the GPS ZPD for CR experiments at three different test sites with more than 200 TerraSAR-X High Resolution SpotLight (HRSL) images. The delay accuracy is around 1-3 cm depending on the location of test site due to the local water vapor variation and the acquisition time/date. [1] Eineder M., Minet C., Steigenberger P., et al. Imaging geodesy - Toward centimeter-level ranging accuracy with TerraSAR-X. Geoscience and Remote Sensing, IEEE Transactions on, 2011, 49(2): 661-671. [2] Balss U., Gisinger C., Cong X. Y., et al. Precise Measurements on the Absolute Localization Accuracy of TerraSAR-X on the

  19. An initial state perturbation experiment with the GISS model. [random error effects on numerical weather prediction models

    NASA Technical Reports Server (NTRS)

    Spar, J.; Notario, J. J.; Quirk, W. J.

    1978-01-01

    Monthly mean global forecasts for January 1975 have been computed with the Goddard Institute for Space Studies model from four slightly different sets of initial conditions - a 'control' state and three random perturbations thereof - to simulate the effects of initial state uncertainty on forecast quality. Differences among the forecasts are examined in terms of energetics, synoptic patterns and forecast statistics. The 'noise level' of the model predictions is depicted on global maps of standard deviations of sea level pressures, 500 mb heights and 850 mb temperatures for the set of four forecasts. Initial small-scale random errors do not appear to result in any major degradation of the large-scale monthly mean forecast beyond that generated by the model itself, nor do they appear to represent the major source of large-scale forecast error.

  20. Measuring memory-prediction errors and their consequences in youth at risk for schizophrenia.

    PubMed

    Keefe, Richard S E; Kraus, Michael S

    2009-05-01

    The largely consistent columnar circuitry observed throughout the cortex may serve to continuously predict bottom-up activation based on invariant memories. This "memory-prediction" function is essential to efficient and accurate perception. Many of the defined cognitive deficits associated with schizophrenia suggest a breakdown of memory-prediction function. As deficits in memory-prediction function are proposed to lie more proximal to the biological causes of schizophrenia than deficits in standard cognitive constructs, tests that more directly probe memory-prediction function may be especially sensitive predictors of conversion in individuals at high-risk for schizophrenia. In this article, we review the conceptual basis for this hypothesis, and outline how it may be tested with specific cognitive paradigms. The accurate identification of cognitive processes that precede the onset of psychosis will not only be useful for clinicians to predict which young people are at greatest risk for schizophrenia, but will also help determine the neurobiology of psychosis onset, thus leading to new and effective treatments for preventing schizophrenia and other psychoses. PMID:19521641

  1. A simple solution for model comparison in bold imaging: the special case of reward prediction error and reward outcomes.

    PubMed

    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.

  2. A comparison of bootstrap methods and an adjusted bootstrap approach for estimating the prediction error in microarray classification.

    PubMed

    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.

  3. Errors between two- and three-dimensional thermal model predictions of hyperthermia treatments.

    PubMed

    Chen, Z P; Miller, W H; Roemer, R B; Cetas, T C

    1990-01-01

    A simulation program to study the three-dimensional temperature distributions produced by hyperthermia in anatomically realistic inhomogenous tissue models has been developed using the bioheat transfer equation. The anatomical data for the inhomogeneous tissues of the human body are entered on a digitizing tablet from serial computed tomography (CT) scans. Power deposition patterns from various heating modalities must be calculated independently. The program has been used to comparatively evaluate two- and three-dimensional simulations in a series of parametric calculations based on a simple inhomogeneous tissue model for uniform power deposition. The conclusions are that two-dimensional simulations always lead to significant errors at the ends of tumors (up to tens of degrees). However, they can give valid results for the central region of large tumors, but only with tumor blood perfusions greater than approximately 1 kg/m3/s. These conclusions from the geometrically simple model are substantiated by the results obtained using the full three-dimensional model for actual patient anatomical simulations. In summary, three-dimensional simulations will be necessary for accurate patient treatment planning. The effect of the thermal conductivity, used in the models, on the temperature field has also been studied. The results show that using any thermal conductivity value in the range of 0.4 to 0.6 W/m/degrees C sufficiently characterizes most soft tissues, especially in the presence of high blood perfusion. However, bone (thermal conductivity of 1.16 W/m/degrees C) and fat (thermal conductivity of 0.2 W/m/degrees C) do not fit this generalization and significant errors result if soft tissue values are used.

  4. Stochastic Residual-Error Analysis For Estimating Hydrologic Model Predictive Uncertainty

    EPA Science Inventory

    A hybrid time series-nonparametric sampling approach, referred to herein as semiparametric, is presented for the estimation of model predictive uncertainty. The methodology is a two-step procedure whereby a distributed hydrologic model is first calibrated, then followed by brute ...

  5. Teaching Absolute Value Meaningfully

    ERIC Educational Resources Information Center

    Wade, Angela

    2012-01-01

    What is the meaning of absolute value? And why do teachers teach students how to solve absolute value equations? Absolute value is a concept introduced in first-year algebra and then reinforced in later courses. Various authors have suggested instructional methods for teaching absolute value to high school students (Wei 2005; Stallings-Roberts…

  6. Using Multivariate Regression Model with Least Absolute Shrinkage and Selection Operator (LASSO) to Predict the Incidence of Xerostomia after Intensity-Modulated Radiotherapy for Head and Neck Cancer

    PubMed Central

    Ting, Hui-Min; Chang, Liyun; Huang, Yu-Jie; Wu, Jia-Ming; Wang, Hung-Yu; Horng, Mong-Fong; Chang, Chun-Ming; Lan, Jen-Hong; Huang, Ya-Yu; Fang, Fu-Min; Leung, Stephen Wan

    2014-01-01

    Purpose The aim of this study was to develop a multivariate logistic regression model with least absolute shrinkage and selection operator (LASSO) to make valid predictions about the incidence of moderate-to-severe patient-rated xerostomia among head and neck cancer (HNC) patients treated with IMRT. Methods and Materials Quality of life questionnaire datasets from 206 patients with HNC were analyzed. The European Organization for Research and Treatment of Cancer QLQ-H&N35 and QLQ-C30 questionnaires were used as the endpoint evaluation. The primary endpoint (grade 3+ xerostomia) was defined as moderate-to-severe xerostomia at 3 (XER3m) and 12 months (XER12m) after the completion of IMRT. Normal tissue complication probability (NTCP) models were developed. The optimal and suboptimal numbers of prognostic factors for a multivariate logistic regression model were determined using the LASSO with bootstrapping technique. Statistical analysis was performed using the scaled Brier score, Nagelkerke R2, chi-squared test, Omnibus, Hosmer-Lemeshow test, and the AUC. Results Eight prognostic factors were selected by LASSO for the 3-month time point: Dmean-c, Dmean-i, age, financial status, T stage, AJCC stage, smoking, and education. Nine prognostic factors were selected for the 12-month time point: Dmean-i, education, Dmean-c, smoking, T stage, baseline xerostomia, alcohol abuse, family history, and node classification. In the selection of the suboptimal number of prognostic factors by LASSO, three suboptimal prognostic factors were fine-tuned by Hosmer-Lemeshow test and AUC, i.e., Dmean-c, Dmean-i, and age for the 3-month time point. Five suboptimal prognostic factors were also selected for the 12-month time point, i.e., Dmean-i, education, Dmean-c, smoking, and T stage. The overall performance for both time points of the NTCP model in terms of scaled Brier score, Omnibus, and Nagelkerke R2 was satisfactory and corresponded well with the expected values. Conclusions

  7. Cognitive flexibility in adolescence: Neural and behavioral mechanisms of reward prediction error processing in adaptive decision making during development

    PubMed Central

    Hauser, Tobias U.; Iannaccone, Reto; Walitza, Susanne; Brandeis, Daniel; Brem, Silvia

    2015-01-01

    Adolescence is associated with quickly changing environmental demands which require excellent adaptive skills and high cognitive flexibility. Feedback-guided adaptive learning and cognitive flexibility are driven by reward prediction error (RPE) signals, which indicate the accuracy of expectations and can be estimated using computational models. Despite the importance of cognitive flexibility during adolescence, only little is known about how RPE processing in cognitive flexibility deviates between adolescence and adulthood. In this study, we investigated the developmental aspects of cognitive flexibility by means of computational models and functional magnetic resonance imaging (fMRI). We compared the neural and behavioral correlates of cognitive flexibility in healthy adolescents (12–16 years) to adults performing a probabilistic reversal learning task. Using a modified risk-sensitive reinforcement learning model, we found that adolescents learned faster from negative RPEs than adults. The fMRI analysis revealed that within the RPE network, the adolescents had a significantly altered RPE-response in the anterior insula. This effect seemed to be mainly driven by increased responses to negative prediction errors. In summary, our findings indicate that decision making in adolescence goes beyond merely increased reward-seeking behavior and provides a developmental perspective to the behavioral and neural mechanisms underlying cognitive flexibility in the context of reinforcement learning. PMID:25234119

  8. Clock time is absolute and universal

    NASA Astrophysics Data System (ADS)

    Shen, Xinhang

    2015-09-01

    A critical error is found in the Special Theory of Relativity (STR): mixing up the concepts of the STR abstract time of a reference frame and the displayed time of a physical clock, which leads to use the properties of the abstract time to predict time dilation on physical clocks and all other physical processes. Actually, a clock can never directly measure the abstract time, but can only record the result of a physical process during a period of the abstract time such as the number of cycles of oscillation which is the multiplication of the abstract time and the frequency of oscillation. After Lorentz Transformation, the abstract time of a reference frame expands by a factor gamma, but the frequency of a clock decreases by the same factor gamma, and the resulting multiplication i.e. the displayed time of a moving clock remains unchanged. That is, the displayed time of any physical clock is an invariant of Lorentz Transformation. The Lorentz invariance of the displayed times of clocks can further prove within the framework of STR our earth based standard physical time is absolute, universal and independent of inertial reference frames as confirmed by both the physical fact of the universal synchronization of clocks on the GPS satellites and clocks on the earth, and the theoretical existence of the absolute and universal Galilean time in STR which has proved that time dilation and space contraction are pure illusions of STR. The existence of the absolute and universal time in STR has directly denied that the reference frame dependent abstract time of STR is the physical time, and therefore, STR is wrong and all its predictions can never happen in the physical world.

  9. The disparity mutagenesis model predicts rescue of living things from catastrophic errors

    PubMed Central

    Furusawa, Mitsuru

    2014-01-01

    In animals including humans, mutation rates per generation exceed a perceived threshold, and excess mutations increase genetic load. Despite this, animals have survived without extinction. This is a perplexing problem for animal and human genetics, arising at the end of the last century, and to date still does not have a fully satisfactory explanation. Shortly after we proposed the disparity theory of evolution in 1992, the disparity mutagenesis model was proposed, which forms the basis for an explanation for an acceleration of evolution and species survival. This model predicts a significant increase of the mutation threshold values if the fidelity difference in replication between the lagging and leading strands is high enough. When applied to biological evolution, the model predicts that living things, including humans, might overcome the lethal effect of accumulated deleterious mutations and be able to survive. Artificially derived mutator strains of microorganisms, in which an enhanced lagging-strand-biased mutagenesis was introduced, showed unexpectedly high adaptability to severe environments. The implications of the striking behaviors shown by these disparity mutators will be discussed in relation to how living things with high mutation rates can avoid the self-defeating risk of excess mutations. PMID:25538731

  10. Frequency-domain analysis of absolute gravimeters

    NASA Astrophysics Data System (ADS)

    Svitlov, S.

    2012-12-01

    An absolute gravimeter is analysed as a linear time-invariant system in the frequency domain. Frequency responses of absolute gravimeters are derived analytically based on the propagation of the complex exponential signal through their linear measurement functions. Depending on the model of motion and the number of time-distance coordinates, an absolute gravimeter is considered as a second-order (three-level scheme) or third-order (multiple-level scheme) low-pass filter. It is shown that the behaviour of an atom absolute gravimeter in the frequency domain corresponds to that of the three-level corner-cube absolute gravimeter. Theoretical results are applied for evaluation of random and systematic measurement errors and optimization of an experiment. The developed theory agrees with known results of an absolute gravimeter analysis in the time and frequency domains and can be used for measurement uncertainty analyses, building of vibration-isolation systems and synthesis of digital filtering algorithms.

  11. A New Height Error Revision Method of Predicting Long-Term Wind Speed with MCP Algorithm

    NASA Astrophysics Data System (ADS)

    Liu, Yujue; Hu, Fei

    2013-04-01

    Wind energy technology is one of the fastest in growing rate in new and renewable energy technologies. It is very important to select stronger windy sites in a country for the purpose of producing more electricity. Measure-Correlate-Predict (MCP) algorithms are used to predict the wind resource at target site for wind power development. MCP method model bases on a relationship between wind data (speed and direction) measured at the target site and concurrent wind data at reference site nearby. The model is then used with long-term data from the reference site to predict the long-term wind speed and direction distributions at the target site. MCP method is in order to be able to determine the annual energy capture of a wind farm located at the target site. Over the last 15 years well over a half dozen of MCP methods in the literature. The MCP algorithms differ in terms of overall approach, model definition, use of direction sectors, and length of the data. Such as 1)a linear regression model; 2)a model using distributions of ratios of wind speeds at two sites; 3)a vector regression method; 4)a method based on the ratio of standard deviations of two data sets, etc. Unfortunately, none of these MCP algorithms can predict wind speed from two sites at different altitudes. If the target site is much higher or lower than the reference site, the result accuracy will be much poorer. Inner Mongolia grassland is known as one of the regions that rich in wind resource in China. The data we use is from three wind measurements, consisting of nearly one year of six layers in XiLinGuoLe of Inner Mongolia . Firstly, we use the maximum likelihood method to estimate k, shape parameter and c, scale parameter of the Weibull function for different time periods. And then we find out that c has a power law function of height, and that k varies as the form of a quadratic function of height and obtains the max value in the height of 10 to100 meters. Finally, we add the height distribution

  12. A Physiologically Based Pharmacokinetic Model to Predict the Pharmacokinetics of Highly Protein-Bound Drugs and Impact of Errors in Plasma Protein Binding

    PubMed Central

    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

  13. A physiologically based pharmacokinetic model to predict the pharmacokinetics of highly protein-bound drugs and the impact of errors in plasma protein binding.

    PubMed

    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.

  14. Measuring the Effect of Inter-Study Variability on Estimating Prediction Error

    PubMed Central

    Ma, Shuyi; Sung, Jaeyun; Magis, Andrew T.; Wang, Yuliang; Geman, Donald; Price, Nathan D.

    2014-01-01

    Background The biomarker discovery field is replete with molecular signatures that have not translated into the clinic despite ostensibly promising performance in predicting disease phenotypes. One widely cited reason is lack of classification consistency, largely due to failure to maintain performance from study to study. This failure is widely attributed to variability in data collected for the same phenotype among disparate studies, due to technical factors unrelated to phenotypes (e.g., laboratory settings resulting in “batch-effects”) and non-phenotype-associated biological variation in the underlying populations. These sources of variability persist in new data collection technologies. Methods Here we quantify the impact of these combined “study-effects” on a disease signature’s predictive performance by comparing two types of validation methods: ordinary randomized cross-validation (RCV), which extracts random subsets of samples for testing, and inter-study validation (ISV), which excludes an entire study for testing. Whereas RCV hardwires an assumption of training and testing on identically distributed data, this key property is lost in ISV, yielding systematic decreases in performance estimates relative to RCV. Measuring the RCV-ISV difference as a function of number of studies quantifies influence of study-effects on performance. Results As a case study, we gathered publicly available gene expression data from 1,470 microarray samples of 6 lung phenotypes from 26 independent experimental studies and 769 RNA-seq samples of 2 lung phenotypes from 4 independent studies. We find that the RCV-ISV performance discrepancy is greater in phenotypes with few studies, and that the ISV performance converges toward RCV performance as data from additional studies are incorporated into classification. Conclusions We show that by examining how fast ISV performance approaches RCV as the number of studies is increased, one can estimate when

  15. Absolute transition probabilities of phosphorus.

    NASA Technical Reports Server (NTRS)

    Miller, M. H.; Roig, R. A.; Bengtson, R. D.

    1971-01-01

    Use of a gas-driven shock tube to measure the absolute strengths of 21 P I lines and 126 P II lines (from 3300 to 6900 A). Accuracy for prominent, isolated neutral and ionic lines is estimated to be 28 to 40% and 18 to 30%, respectively. The data and the corresponding theoretical predictions are examined for conformity with the sum rules.-

  16. Cloud Condensation Nuclei Prediction Error from Application of Kohler Theory: Importance for the Aerosol Indirect Effect

    NASA Technical Reports Server (NTRS)

    Sotiropoulou, Rafaella-Eleni P.; Nenes, Athanasios; Adams, Peter J.; Seinfeld, John H.

    2007-01-01

    In situ observations of aerosol and cloud condensation nuclei (CCN) and the GISS GCM Model II' with an online aerosol simulation and explicit aerosol-cloud interactions are used to quantify the uncertainty in radiative forcing and autoconversion rate from application of Kohler theory. Simulations suggest that application of Koehler theory introduces a 10-20% uncertainty in global average indirect forcing and 2-11% uncertainty in autoconversion. Regionally, the uncertainty in indirect forcing ranges between 10-20%, and 5-50% for autoconversion. These results are insensitive to the range of updraft velocity and water vapor uptake coefficient considered. This study suggests that Koehler theory (as implemented in climate models) is not a significant source of uncertainty for aerosol indirect forcing but can be substantial for assessments of aerosol effects on the hydrological cycle in climatically sensitive regions of the globe. This implies that improvements in the representation of GCM subgrid processes and aerosol size distribution will mostly benefit indirect forcing assessments. Predictions of autoconversion, by nature, will be subject to considerable uncertainty; its reduction may require explicit representation of size-resolved aerosol composition and mixing state.

  17. From prediction error to incentive salience: mesolimbic computation of reward motivation

    PubMed Central

    Berridge, Kent C.

    2011-01-01

    Reward contains separable psychological components of learning, incentive motivation and pleasure. Most computational models have focused only on the learning component of reward, but the motivational component is equally important in reward circuitry, and even more directly controls behavior. Modeling the motivational component requires recognition of additional control factors besides learning. Here I will discuss how mesocorticolimbic mechanisms generate the motivation component of incentive salience. Incentive salience takes Pavlovian learning and memory as one input and as an equally important input takes neurobiological state factors (e.g., drug states, appetite states, satiety states) that can vary independently of learning. Neurobiological state changes can produce unlearned fluctuations or even reversals in the ability of a previously-learned reward cue to trigger motivation. Such fluctuations in cue-triggered motivation can dramatically depart from all previously learned values about the associated reward outcome. Thus a consequence of the difference between incentive salience and learning can be to decouple cue-triggered motivation of the moment from previously learned values of how good the associated reward has been in the past. Another consequence can be to produce irrationally strong motivation urges that are not justified by any memories of previous reward values (and without distorting associative predictions of future reward value). Such irrationally strong motivation may be especially problematic in addiction. To comprehend these phenomena, future models of mesocorticolimbic reward function should address the neurobiological state factors that participate to control generation of incentive salience. PMID:22487042

  18. Eosinophil count - absolute

    MedlinePlus

    Eosinophils; Absolute eosinophil count ... the white blood cell count to give the absolute eosinophil count. ... than 500 cells per microliter (cells/mcL). Normal value ranges may vary slightly among different laboratories. Talk ...

  19. Encoding of both positive and negative reward prediction errors by neurons of the primate lateral prefrontal cortex and caudate nucleus.

    PubMed

    Asaad, Wael F; Eskandar, Emad N

    2011-12-01

    Learning can be motivated by unanticipated success or unexpected failure. The former encourages us to repeat an action or activity, whereas the latter leads us to find an alternative strategy. Understanding the neural representation of these unexpected events is therefore critical to elucidate learning-related circuits. We examined the activity of neurons in the lateral prefrontal cortex (PFC) and caudate nucleus of monkeys as they performed a trial-and-error learning task. Unexpected outcomes were widely represented in both structures, and neurons driven by unexpectedly negative outcomes were as frequent as those activated by unexpectedly positive outcomes. Moreover, both positive and negative reward prediction errors (RPEs) were represented primarily by increases in firing rate, unlike the manner in which dopamine neurons have been observed to reflect these values. Interestingly, positive RPEs tended to appear with shorter latency than negative RPEs, perhaps reflecting the mechanism of their generation. Last, in the PFC but not the caudate, trial-by-trial variations in outcome-related activity were linked to the animals' subsequent behavioral decisions. More broadly, the robustness of RPE signaling by these neurons suggests that actor-critic models of reinforcement learning in which the PFC and particularly the caudate are considered primarily to be "actors" rather than "critics," should be reconsidered to include a prominent evaluative role for these structures. PMID:22159094

  20. Putative extremely high rate of proteome innovation in lancelets might be explained by high rate of gene prediction errors.

    PubMed

    Bányai, László; Patthy, László

    2016-08-01

    A recent analysis of the genomes of Chinese and Florida lancelets has concluded that the rate of creation of novel protein domain combinations is orders of magnitude greater in lancelets than in other metazoa and it was suggested that continuous activity of transposable elements in lancelets is responsible for this increased rate of protein innovation. Since morphologically Chinese and Florida lancelets are highly conserved, this finding would contradict the observation that high rates of protein innovation are usually associated with major evolutionary innovations. Here we show that the conclusion that the rate of proteome innovation is exceptionally high in lancelets may be unjustified: the differences observed in domain architectures of orthologous proteins of different amphioxus species probably reflect high rates of gene prediction errors rather than true innovation.

  1. Putative extremely high rate of proteome innovation in lancelets might be explained by high rate of gene prediction errors

    PubMed Central

    Bányai, László; Patthy, László

    2016-01-01

    A recent analysis of the genomes of Chinese and Florida lancelets has concluded that the rate of creation of novel protein domain combinations is orders of magnitude greater in lancelets than in other metazoa and it was suggested that continuous activity of transposable elements in lancelets is responsible for this increased rate of protein innovation. Since morphologically Chinese and Florida lancelets are highly conserved, this finding would contradict the observation that high rates of protein innovation are usually associated with major evolutionary innovations. Here we show that the conclusion that the rate of proteome innovation is exceptionally high in lancelets may be unjustified: the differences observed in domain architectures of orthologous proteins of different amphioxus species probably reflect high rates of gene prediction errors rather than true innovation. PMID:27476717

  2. Putative extremely high rate of proteome innovation in lancelets might be explained by high rate of gene prediction errors.

    PubMed

    Bányai, László; Patthy, László

    2016-01-01

    A recent analysis of the genomes of Chinese and Florida lancelets has concluded that the rate of creation of novel protein domain combinations is orders of magnitude greater in lancelets than in other metazoa and it was suggested that continuous activity of transposable elements in lancelets is responsible for this increased rate of protein innovation. Since morphologically Chinese and Florida lancelets are highly conserved, this finding would contradict the observation that high rates of protein innovation are usually associated with major evolutionary innovations. Here we show that the conclusion that the rate of proteome innovation is exceptionally high in lancelets may be unjustified: the differences observed in domain architectures of orthologous proteins of different amphioxus species probably reflect high rates of gene prediction errors rather than true innovation. PMID:27476717

  3. Forensic comparison and matching of fingerprints: using quantitative image measures for estimating error rates through understanding and predicting difficulty.

    PubMed

    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

  4. Forensic Comparison and Matching of Fingerprints: Using Quantitative Image Measures for Estimating Error Rates through Understanding and Predicting Difficulty

    PubMed Central

    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

  5. Distinguishing the effects of model structural error and parameter uncertainty on predictions of pesticide leaching under climate change

    NASA Astrophysics Data System (ADS)

    Steffens, K.; Larsbo, M.; Moeys, J.; Jarvis, N.; Lewan, E.

    2012-04-01

    Studying climate change impacts on pesticide leaching is laced with various sources of uncertainty, which must be assessed in as detailed way as possible in order to understand the reliability of predictions of pesticide leaching under current and future climate conditions. One dilemma in this respect is the difficulty in separating the effects of model structural error from parameter uncertainty. An example of the former is that most of the commonly-used pesticide transport models only consider temperature-dependent degradation, whereas temperature also influences transport in soils through its effect on sorption and diffusion. Especially for climate impact assessments of pesticide leaching, the processes and parameters that depend on soil temperature and moisture should be carefully considered. Two functions, one describing temperature-dependent sorption and one for temperature-dependent diffusion, were therefore introduced as options into the process-oriented 1D pesticide fate and transport model MACRO5.2, which resulted in four structurally different versions of the MACRO-model. The aims of the study were to assess (i) the uncertainty related to model structure in relation to parameter uncertainty and (ii) the importance of these sources of uncertainty in long-term predictions of leaching in the perspective of climate change. A case study for leaching of the mobile herbicide Bentazone was performed in a two-step procedure. First, acceptable parameter sets were identified by evaluating model performance using the Nash-Sutcliff criteria against comprehensive data from a one-year field experiment on a clay soil in Lanna (Southern Sweden). Eight sensitive and uncertain parameters were sampled from uniform distributions in a Monte-Carlo approach, separately for each of the four model versions. In a second step, each model-version with its particular ensemble of different acceptable parameter combinations was used to predict leaching for a present (1970-1999) and a

  6. Effect of model error on precipitation forecasts in the high-resolution limited area ensemble prediction system of the Korea Meteorological Administration

    NASA Astrophysics Data System (ADS)

    Kim, SeHyun; Kim, Hyun Mee

    2015-04-01

    In numerical weather prediction using convective-scale model resolution, forecast uncertainties are caused by initial condition error, boundary condition error, and model error. Because convective-scale forecasts are influenced by subgrid scale processes which cannot be resolved easily, the model error becomes more important than the initial and boundary condition errors. To consider the model error, multi-model and multi-physics methods use several models and physics schemes and the stochastic physics method uses random numbers to create a noise term in the model equations (e.g. Stochastic Perturbed Parameterization Tendency (SPPT), Stochastic Kinetic Energy Backscatter (SKEB), Stochastic Convective Vorticity (SCV), and Random Parameters (RP)). In this study, the RP method was used to consider the model error in the high-resolution limited area ensemble prediction system (EPS) of the Korea Meteorological Administration (KMA). The EPS has 12 ensemble members with 3 km horizontal resolution which generate 48 h forecasts. The initial and boundary conditions were provided by the global EPS of the KMA. The RP method was applied to microphysics and boundary layer schemes, and the ensemble forecasts using RP were compared with those without RP during July 2013. Both Root Mean Square Error (RMSE) and spread of wind at 10 m verified by surface Automatic Weather System (AWS) observations decreased when using RP. However, for 1 hour accumulated precipitation, the spread increased with RP and Equitable Threat Score (ETS) showed different results for each rainfall event.

  7. Absolute Radiometric Calibration of EUNIS-06

    NASA Technical Reports Server (NTRS)

    Thomas, R. J.; Rabin, D. M.; Kent, B. J.; Paustian, W.

    2007-01-01

    The Extreme-Ultraviolet Normal-Incidence Spectrometer (EUNIS) is a soundingrocket payload that obtains imaged high-resolution spectra of individual solar features, providing information about the Sun's corona and upper transition region. Shortly after its successful initial flight last year, a complete end-to-end calibration was carried out to determine the instrument's absolute radiometric response over its Longwave bandpass of 300 - 370A. The measurements were done at the Rutherford-Appleton Laboratory (RAL) in England, using the same vacuum facility and EUV radiation source used in the pre-flight calibrations of both SOHO/CDS and Hinode/EIS, as well as in three post-flight calibrations of our SERTS sounding rocket payload, the precursor to EUNIS. The unique radiation source provided by the Physikalisch-Technische Bundesanstalt (PTB) had been calibrated to an absolute accuracy of 7% (l-sigma) at 12 wavelengths covering our bandpass directly against the Berlin electron storage ring BESSY, which is itself a primary radiometric source standard. Scans of the EUNIS aperture were made to determine the instrument's absolute spectral sensitivity to +- 25%, considering all sources of error, and demonstrate that EUNIS-06 was the most sensitive solar E W spectrometer yet flown. The results will be matched against prior calibrations which relied on combining measurements of individual optical components, and on comparisons with theoretically predicted 'insensitive' line ratios. Coordinated observations were made during the EUNIS-06 flight by SOHO/CDS and EIT that will allow re-calibrations of those instruments as well. In addition, future EUNIS flights will provide similar calibration updates for TRACE, Hinode/EIS, and STEREO/SECCHI/EUVI.

  8. No unified reward prediction error in local field potentials from the human nucleus accumbens: evidence from epilepsy patients.

    PubMed

    Stenner, Max-Philipp; Rutledge, Robb B; Zaehle, Tino; Schmitt, Friedhelm C; Kopitzki, Klaus; Kowski, Alexander B; Voges, Jürgen; Heinze, Hans-Jochen; Dolan, Raymond J

    2015-08-01

    Functional magnetic resonance imaging (fMRI), cyclic voltammetry, and single-unit electrophysiology studies suggest that signals measured in the nucleus accumbens (Nacc) during value-based decision making represent reward prediction errors (RPEs), the difference between actual and predicted rewards. Here, we studied the precise temporal and spectral pattern of reward-related signals in the human Nacc. We recorded local field potentials (LFPs) from the Nacc of six epilepsy patients during an economic decision-making task. On each trial, patients decided whether to accept or reject a gamble with equal probabilities of a monetary gain or loss. The behavior of four patients was consistent with choices being guided by value expectations. Expected value signals before outcome onset were observed in three of those patients, at varying latencies and with nonoverlapping spectral patterns. Signals after outcome onset were correlated with RPE regressors in all subjects. However, further analysis revealed that these signals were better explained as outcome valence rather than RPE signals, with gamble gains and losses differing in the power of beta oscillations and in evoked response amplitudes. Taken together, our results do not support the idea that postsynaptic potentials in the Nacc represent a RPE that unifies outcome magnitude and prior value expectation. We discuss the generalizability of our findings to healthy individuals and the relation of our results to measurements of RPE signals obtained from the Nacc with other methods. PMID:26019312

  9. No unified reward prediction error in local field potentials from the human nucleus accumbens: evidence from epilepsy patients.

    PubMed

    Stenner, Max-Philipp; Rutledge, Robb B; Zaehle, Tino; Schmitt, Friedhelm C; Kopitzki, Klaus; Kowski, Alexander B; Voges, Jürgen; Heinze, Hans-Jochen; Dolan, Raymond J

    2015-08-01

    Functional magnetic resonance imaging (fMRI), cyclic voltammetry, and single-unit electrophysiology studies suggest that signals measured in the nucleus accumbens (Nacc) during value-based decision making represent reward prediction errors (RPEs), the difference between actual and predicted rewards. Here, we studied the precise temporal and spectral pattern of reward-related signals in the human Nacc. We recorded local field potentials (LFPs) from the Nacc of six epilepsy patients during an economic decision-making task. On each trial, patients decided whether to accept or reject a gamble with equal probabilities of a monetary gain or loss. The behavior of four patients was consistent with choices being guided by value expectations. Expected value signals before outcome onset were observed in three of those patients, at varying latencies and with nonoverlapping spectral patterns. Signals after outcome onset were correlated with RPE regressors in all subjects. However, further analysis revealed that these signals were better explained as outcome valence rather than RPE signals, with gamble gains and losses differing in the power of beta oscillations and in evoked response amplitudes. Taken together, our results do not support the idea that postsynaptic potentials in the Nacc represent a RPE that unifies outcome magnitude and prior value expectation. We discuss the generalizability of our findings to healthy individuals and the relation of our results to measurements of RPE signals obtained from the Nacc with other methods.

  10. No unified reward prediction error in local field potentials from the human nucleus accumbens: evidence from epilepsy patients

    PubMed Central

    Rutledge, Robb B.; Zaehle, Tino; Schmitt, Friedhelm C.; Kopitzki, Klaus; Kowski, Alexander B.; Voges, Jürgen; Heinze, Hans-Jochen; Dolan, Raymond J.

    2015-01-01

    Functional magnetic resonance imaging (fMRI), cyclic voltammetry, and single-unit electrophysiology studies suggest that signals measured in the nucleus accumbens (Nacc) during value-based decision making represent reward prediction errors (RPEs), the difference between actual and predicted rewards. Here, we studied the precise temporal and spectral pattern of reward-related signals in the human Nacc. We recorded local field potentials (LFPs) from the Nacc of six epilepsy patients during an economic decision-making task. On each trial, patients decided whether to accept or reject a gamble with equal probabilities of a monetary gain or loss. The behavior of four patients was consistent with choices being guided by value expectations. Expected value signals before outcome onset were observed in three of those patients, at varying latencies and with nonoverlapping spectral patterns. Signals after outcome onset were correlated with RPE regressors in all subjects. However, further analysis revealed that these signals were better explained as outcome valence rather than RPE signals, with gamble gains and losses differing in the power of beta oscillations and in evoked response amplitudes. Taken together, our results do not support the idea that postsynaptic potentials in the Nacc represent a RPE that unifies outcome magnitude and prior value expectation. We discuss the generalizability of our findings to healthy individuals and the relation of our results to measurements of RPE signals obtained from the Nacc with other methods. PMID:26019312

  11. Evaluation of the predicted error of the soil moisture retrieval from C-band SAR by comparison against modelled soil moisture estimates over Australia.

    PubMed

    Doubková, Marcela; Van Dijk, Albert I J M; Sabel, Daniel; Wagner, Wolfgang; Blöschl, Günter

    2012-05-15

    The Sentinel-1 will carry onboard a C-band radar instrument that will map the European continent once every four days and the global land surface at least once every twelve days with finest 5 × 20 m spatial resolution. The high temporal sampling rate and operational configuration make Sentinel-1 of interest for operational soil moisture monitoring. Currently, updated soil moisture data are made available at 1 km spatial resolution as a demonstration service using Global Mode (GM) measurements from the Advanced Synthetic Aperture Radar (ASAR) onboard ENVISAT. The service demonstrates the potential of the C-band observations to monitor variations in soil moisture. Importantly, a retrieval error estimate is also available; these are needed to assimilate observations into models. The retrieval error is estimated by propagating sensor errors through the retrieval model. In this work, the existing ASAR GM retrieval error product is evaluated using independent top soil moisture estimates produced by the grid-based landscape hydrological model (AWRA-L) developed within the Australian Water Resources Assessment system (AWRA). The ASAR GM retrieval error estimate, an assumed prior AWRA-L error estimate and the variance in the respective datasets were used to spatially predict the root mean square error (RMSE) and the Pearson's correlation coefficient R between the two datasets. These were compared with the RMSE calculated directly from the two datasets. The predicted and computed RMSE showed a very high level of agreement in spatial patterns as well as good quantitative agreement; the RMSE was predicted within accuracy of 4% of saturated soil moisture over 89% of the Australian land mass. Predicted and calculated R maps corresponded within accuracy of 10% over 61% of the continent. The strong correspondence between the predicted and calculated RMSE and R builds confidence in the retrieval error model and derived ASAR GM error estimates. The ASAR GM and Sentinel-1 have

  12. Evaluation of the predicted error of the soil moisture retrieval from C-band SAR by comparison against modelled soil moisture estimates over Australia

    PubMed Central

    Doubková, Marcela; Van Dijk, Albert I.J.M.; Sabel, Daniel; Wagner, Wolfgang; Blöschl, Günter

    2012-01-01

    The Sentinel-1 will carry onboard a C-band radar instrument that will map the European continent once every four days and the global land surface at least once every twelve days with finest 5 × 20 m spatial resolution. The high temporal sampling rate and operational configuration make Sentinel-1 of interest for operational soil moisture monitoring. Currently, updated soil moisture data are made available at 1 km spatial resolution as a demonstration service using Global Mode (GM) measurements from the Advanced Synthetic Aperture Radar (ASAR) onboard ENVISAT. The service demonstrates the potential of the C-band observations to monitor variations in soil moisture. Importantly, a retrieval error estimate is also available; these are needed to assimilate observations into models. The retrieval error is estimated by propagating sensor errors through the retrieval model. In this work, the existing ASAR GM retrieval error product is evaluated using independent top soil moisture estimates produced by the grid-based landscape hydrological model (AWRA-L) developed within the Australian Water Resources Assessment system (AWRA). The ASAR GM retrieval error estimate, an assumed prior AWRA-L error estimate and the variance in the respective datasets were used to spatially predict the root mean square error (RMSE) and the Pearson's correlation coefficient R between the two datasets. These were compared with the RMSE calculated directly from the two datasets. The predicted and computed RMSE showed a very high level of agreement in spatial patterns as well as good quantitative agreement; the RMSE was predicted within accuracy of 4% of saturated soil moisture over 89% of the Australian land mass. Predicted and calculated R maps corresponded within accuracy of 10% over 61% of the continent. The strong correspondence between the predicted and calculated RMSE and R builds confidence in the retrieval error model and derived ASAR GM error estimates. The ASAR GM and Sentinel-1 have

  13. Why Don't We Learn to Accurately Forecast Feelings? How Misremembering Our Predictions Blinds Us to Past Forecasting Errors

    ERIC Educational Resources Information Center

    Meyvis, Tom; Ratner, Rebecca K.; Levav, Jonathan

    2010-01-01

    Why do affective forecasting errors persist in the face of repeated disconfirming evidence? Five studies demonstrate that people misremember their forecasts as consistent with their experience and thus fail to perceive the extent of their forecasting error. As a result, people do not learn from past forecasting errors and fail to adjust subsequent…

  14. Comparison of the initial errors most likely to cause a spring predictability barrier for two types of El Niño events

    NASA Astrophysics Data System (ADS)

    Tian, Ben; Duan, Wansuo

    2016-08-01

    In this paper, the spring predictability barrier (SPB) problem for two types of El Niño events is investigated. This is enabled by tracing the evolution of a conditional nonlinear optimal perturbation (CNOP) that acts as the initial error with the biggest negative effect on the El Niño predictions. We show that the CNOP-type errors for central Pacific-El Niño (CP-El Niño) events can be classified into two types: the first are CP-type-1 errors possessing a sea surface temperature anomaly (SSTA) pattern with negative anomalies in the equatorial central western Pacific, positive anomalies in the equatorial eastern Pacific, and accompanied by a thermocline depth anomaly pattern with positive anomalies along the equator. The second are, CP-type-2 errors presenting an SSTA pattern in the central eastern equatorial Pacific, with a dipole structure of negative anomalies in the east and positive anomalies in the west, and a thermocline depth anomaly pattern with a slight deepening along the equator. CP-type-1 errors grow in a manner similar to an eastern Pacific-El Niño (EP-El Niño) event and grow significantly during boreal spring, leading to a significant SPB for the CP-El Niño. CP-type-2 errors initially present as a process similar to a La Niña-like decay, prior to transitioning into a growth phase of an EP-El Niño-like event; but they fail to cause a SPB. For the EP-El Niño events, the CNOP-type errors are also classified into two types: EP-type-1 errors and 2 errors. The former is similar to a CP-type-1 error, while the latter presents with an almost opposite pattern. Both EP-type-1 and 2 errors yield a significant SPB for EP-El Niño events. For both CP- and EP-El Niño, their CNOP-type errors that cause a prominent SPB are concentrated in the central and eastern tropical Pacific. This may indicate that the prediction uncertainties of both types of El Niño events are sensitive to the initial errors in this region. The region may represent a common

  15. Differential Dopamine Release Dynamics in the Nucleus Accumbens Core and Shell Reveal Complementary Signals for Error Prediction and Incentive Motivation

    PubMed Central

    Cacciapaglia, Fabio; Wightman, R. Mark; Carelli, Regina M.

    2015-01-01

    Mesolimbic dopamine (DA) is phasically released during appetitive behaviors, though there is substantive disagreement about the specific purpose of these DA signals. For example, prediction error (PE) models suggest a role of learning, while incentive salience (IS) models argue that the DA signal imbues stimuli with value and thereby stimulates motivated behavior. However, within the nucleus accumbens (NAc) patterns of DA release can strikingly differ between subregions, and as such, it is possible that these patterns differentially contribute to aspects of PE and IS. To assess this, we measured DA release in subregions of the NAc during a behavioral task that spatiotemporally separated sequential goal-directed stimuli. Electrochemical methods were used to measure subsecond NAc dopamine release in the core and shell during a well learned instrumental chain schedule in which rats were trained to press one lever (seeking; SL) to gain access to a second lever (taking; TL) linked with food delivery, and again during extinction. In the core, phasic DA release was greatest following initial SL presentation, but minimal for the subsequent TL and reward events. In contrast, phasic shell DA showed robust release at all task events. Signaling decreased between the beginning and end of sessions in the shell, but not core. During extinction, peak DA release in the core showed a graded decrease for the SL and pauses in release during omitted expected rewards, whereas shell DA release decreased predominantly during the TL. These release dynamics suggest parallel DA signals capable of supporting distinct theories of appetitive behavior. SIGNIFICANCE STATEMENT Dopamine signaling in the brain is important for a variety of cognitive functions, such as learning and motivation. Typically, it is assumed that a single dopamine signal is sufficient to support these cognitive functions, though competing theories disagree on how dopamine contributes to reward-based behaviors. Here, we have

  16. Absolute nuclear material assay

    DOEpatents

    Prasad, Manoj K.; Snyderman, Neal J.; Rowland, Mark S.

    2012-05-15

    A method of absolute nuclear material assay of an unknown source comprising counting neutrons from the unknown source and providing an absolute nuclear material assay utilizing a model to optimally compare to the measured count distributions. In one embodiment, the step of providing an absolute nuclear material assay comprises utilizing a random sampling of analytically computed fission chain distributions to generate a continuous time-evolving sequence of event-counts by spreading the fission chain distribution in time.

  17. Absolute nuclear material assay

    DOEpatents

    Prasad, Manoj K.; Snyderman, Neal J.; Rowland, Mark S.

    2010-07-13

    A method of absolute nuclear material assay of an unknown source comprising counting neutrons from the unknown source and providing an absolute nuclear material assay utilizing a model to optimally compare to the measured count distributions. In one embodiment, the step of providing an absolute nuclear material assay comprises utilizing a random sampling of analytically computed fission chain distributions to generate a continuous time-evolving sequence of event-counts by spreading the fission chain distribution in time.

  18. Modeling dopaminergic and other processes involved in learning from reward prediction error: contributions from an individual differences perspective

    PubMed Central

    Pickering, Alan D.; Pesola, Francesca

    2014-01-01

    Phasic firing changes of midbrain dopamine neurons have been widely characterized as reflecting a reward prediction error (RPE). Major personality traits (e.g., extraversion) have been linked to inter-individual variations in dopaminergic neurotransmission. Consistent with these two claims, recent research (Smillie et al., 2011; Cooper et al., 2014) found that extraverts exhibited larger RPEs than introverts, as reflected in feedback related negativity (FRN) effects in EEG recordings. Using an established, biologically-localized RPE computational model, we successfully simulated dopaminergic cell firing changes which are thought to modulate the FRN. We introduced simulated individual differences into the model: parameters were systematically varied, with stable values for each simulated individual. We explored whether a model parameter might be responsible for the observed covariance between extraversion and the FRN changes in real data, and argued that a parameter is a plausible source of such covariance if parameter variance, across simulated individuals, correlated almost perfectly with the size of the simulated dopaminergic FRN modulation, and created as much variance as possible in this simulated output. Several model parameters met these criteria, while others did not. In particular, variations in the strength of connections carrying excitatory reward drive inputs to midbrain dopaminergic cells were considered plausible candidates, along with variations in a parameter which scales the effects of dopamine cell firing bursts on synaptic modification in ventral striatum. We suggest possible neurotransmitter mechanisms underpinning these model parameters. Finally, the limitations and possible extensions of our general approach are discussed. PMID:25324752

  19. Alignment as a consequence of expectation adaptation: syntactic priming is affected by the prime's prediction error given both prior and recent experience.

    PubMed

    Jaeger, T Florian; Snider, Neal E

    2013-04-01

    Speakers show a remarkable tendency to align their productions with their interlocutors'. Focusing on sentence production, we investigate the cognitive systems underlying such alignment (syntactic priming). Our guiding hypothesis is that syntactic priming is a consequence of a language processing system that is organized to achieve efficient communication in an ever-changing (subjectively non-stationary) environment. We build on recent work suggesting that comprehenders adapt to the statistics of the current environment. If such adaptation is rational or near-rational, the extent to which speakers adapt their expectations for a syntactic structure after processing a prime sentence should be sensitive to the prediction error experienced while processing the prime. This prediction is shared by certain error-based implicit learning accounts, but not by most other accounts of syntactic priming. In three studies, we test this prediction against data from conversational speech, speech during picture description, and written production during sentence completion. All three studies find stronger syntactic priming for primes associated with a larger prediction error (primes with higher syntactic surprisal). We find that the relevant prediction error is sensitive to both prior and recent experience within the experiment. Together with other findings, this supports accounts that attribute syntactic priming to expectation adaptation.

  20. Variable selection for modeling the absolute magnitude at maximum of Type Ia supernovae

    NASA Astrophysics Data System (ADS)

    Uemura, Makoto; Kawabata, Koji S.; Ikeda, Shiro; Maeda, Keiichi

    2015-06-01

    We discuss what is an appropriate set of explanatory variables in order to predict the absolute magnitude at the maximum of Type Ia supernovae. In order to have a good prediction, the error for future data, which is called the "generalization error," should be small. We use cross-validation in order to control the generalization error and a LASSO-type estimator in order to choose the set of variables. This approach can be used even in the case that the number of samples is smaller than the number of candidate variables. We studied the Berkeley supernova database with our approach. Candidates for the explanatory variables include normalized spectral data, variables about lines, and previously proposed flux ratios, as well as the color and light-curve widths. As a result, we confirmed the past understanding about Type Ia supernovae: (i) The absolute magnitude at maximum depends on the color and light-curve width. (ii) The light-curve width depends on the strength of Si II. Recent studies have suggested adding more variables in order to explain the absolute magnitude. However, our analysis does not support adding any other variables in order to have a better generalization error.

  1. How to regress and predict in a Bland-Altman plot? Review and contribution based on tolerance intervals and correlated-errors-in-variables models.

    PubMed

    Francq, Bernard G; Govaerts, Bernadette

    2016-06-30

    Two main methodologies for assessing equivalence in method-comparison studies are presented separately in the literature. The first one is the well-known and widely applied Bland-Altman approach with its agreement intervals, where two methods are considered interchangeable if their differences are not clinically significant. The second approach is based on errors-in-variables regression in a classical (X,Y) plot and focuses on confidence intervals, whereby two methods are considered equivalent when providing similar measures notwithstanding the random measurement errors. This paper reconciles these two methodologies and shows their similarities and differences using both real data and simulations. A new consistent correlated-errors-in-variables regression is introduced as the errors are shown to be correlated in the Bland-Altman plot. Indeed, the coverage probabilities collapse and the biases soar when this correlation is ignored. Novel tolerance intervals are compared with agreement intervals with or without replicated data, and novel predictive intervals are introduced to predict a single measure in an (X,Y) plot or in a Bland-Atman plot with excellent coverage probabilities. We conclude that the (correlated)-errors-in-variables regressions should not be avoided in method comparison studies, although the Bland-Altman approach is usually applied to avert their complexity. We argue that tolerance or predictive intervals are better alternatives than agreement intervals, and we provide guidelines for practitioners regarding method comparison studies. Copyright © 2016 John Wiley & Sons, Ltd.

  2. Basic Diagnosis and Prediction of Persistent Contrail Occurrence using High-resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part I: Effects of Random Error

    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.

  3. An investigation into multi-dimensional prediction models to estimate the pose error of a quadcopter in a CSP plant setting

    NASA Astrophysics Data System (ADS)

    Lock, Jacobus C.; Smit, Willie J.; Treurnicht, Johann

    2016-05-01

    The Solar Thermal Energy Research Group (STERG) is investigating ways to make heliostats cheaper to reduce the total cost of a concentrating solar power (CSP) plant. One avenue of research is to use unmanned aerial vehicles (UAVs) to automate and assist with the heliostat calibration process. To do this, the pose estimation error of each UAV must be determined and integrated into a calibration procedure. A computer vision (CV) system is used to measure the pose of a quadcopter UAV. However, this CV system contains considerable measurement errors. Since this is a high-dimensional problem, a sophisticated prediction model must be used to estimate the measurement error of the CV system for any given pose measurement vector. This paper attempts to train and validate such a model with the aim of using it to determine the pose error of a quadcopter in a CSP plant setting.

  4. Prediction of stream volatilization coefficients

    USGS Publications Warehouse

    Rathbun, Ronald E.

    1990-01-01

    Equations are developed for predicting the liquid-film and gas-film reference-substance parameters for quantifying volatilization of organic solutes from streams. Molecular weight and molecular-diffusion coefficients of the solute are used as correlating parameters. Equations for predicting molecular-diffusion coefficients of organic solutes in water and air are developed, with molecular weight and molal volume as parameters. Mean absolute errors of prediction for diffusion coefficients in water are 9.97% for the molecular-weight equation, 6.45% for the molal-volume equation. The mean absolute error for the diffusion coefficient in air is 5.79% for the molal-volume equation. Molecular weight is not a satisfactory correlating parameter for diffusion in air because two equations are necessary to describe the values in the data set. The best predictive equation for the liquid-film reference-substance parameter has a mean absolute error of 5.74%, with molal volume as the correlating parameter. The best equation for the gas-film parameter has a mean absolute error of 7.80%, with molecular weight as the correlating parameter.

  5. Processing of action- but not stimulus-related prediction errors differs between active and observational feedback learning.

    PubMed

    Kobza, Stefan; Bellebaum, Christian

    2015-01-01

    Learning of stimulus-response-outcome associations is driven by outcome prediction errors (PEs). Previous studies have shown larger PE-dependent activity in the striatum for learning from own as compared to observed actions and the following outcomes despite comparable learning rates. We hypothesised that this finding relates primarily to a stronger integration of action and outcome information in active learners. Using functional magnetic resonance imaging, we investigated brain activations related to action-dependent PEs, reflecting the deviation between action values and obtained outcomes, and action-independent PEs, reflecting the deviation between subjective values of response-preceding cues and obtained outcomes. To this end, 16 active and 15 observational learners engaged in a probabilistic learning card-guessing paradigm. On each trial, active learners saw one out of five cues and pressed either a left or right response button to receive feedback (monetary win or loss). Each observational learner observed exactly those cues, responses and outcomes of one active learner. Learning performance was assessed in active test trials without feedback and did not differ between groups. For both types of PEs, activations were found in the globus pallidus, putamen, cerebellum, and insula in active learners. However, only for action-dependent PEs, activations in these structures and the anterior cingulate were increased in active relative to observational learners. Thus, PE-related activity in the reward system is not generally enhanced in active relative to observational learning but only for action-dependent PEs. For the cerebellum, additional activations were found across groups for cue-related uncertainty, thereby emphasising the cerebellum's role in stimulus-outcome learning.

  6. How the credit assignment problems in motor control could be solved after the cerebellum predicts increases in error

    PubMed Central

    Verduzco-Flores, Sergio O.; O'Reilly, Randall C.

    2015-01-01

    We present a cerebellar architecture with two main characteristics. The first one is that complex spikes respond to increases in sensory errors. The second one is that cerebellar modules associate particular contexts where errors have increased in the past with corrective commands that stop the increase in error. We analyze our architecture formally and computationally for the case of reaching in a 3D environment. In the case of motor control, we show that there are synergies of this architecture with the Equilibrium-Point hypothesis, leading to novel ways to solve the motor error and distal learning problems. In particular, the presence of desired equilibrium lengths for muscles provides a way to know when the error is increasing, and which corrections to apply. In the context of Threshold Control Theory and Perceptual Control Theory we show how to extend our model so it implements anticipative corrections in cascade control systems that span from muscle contractions to cognitive operations. PMID:25852535

  7. Information systems and human error in the lab.

    PubMed

    Bissell, Michael G

    2004-01-01

    Health system costs in clinical laboratories are incurred daily due to human error. Indeed, a major impetus for automating clinical laboratories has always been the opportunity it presents to simultaneously reduce cost and improve quality of operations by decreasing human error. But merely automating these processes is not enough. To the extent that introduction of these systems results in operators having less practice in dealing with unexpected events or becoming deskilled in problemsolving, however new kinds of error will likely appear. Clinical laboratories could potentially benefit by integrating findings on human error from modern behavioral science into their operations. Fully understanding human error requires a deep understanding of human information processing and cognition. Predicting and preventing negative consequences requires application of this understanding to laboratory operations. Although the occurrence of a particular error at a particular instant cannot be absolutely prevented, human error rates can be reduced. The following principles are key: an understanding of the process of learning in relation to error; understanding the origin of errors since this knowledge can be used to reduce their occurrence; optimal systems should be forgiving to the operator by absorbing errors, at least for a time; although much is known by industrial psychologists about how to write operating procedures and instructions in ways that reduce the probability of error, this expertise is hardly ever put to use in the laboratory; and a feedback mechanism must be designed into the system that enables the operator to recognize in real time that an error has occurred.

  8. Standardized Software for Wind Load Forecast Error Analyses and Predictions Based on Wavelet-ARIMA Models - Applications at Multiple Geographically Distributed Wind Farms

    SciTech Connect

    Hou, Zhangshuan; Makarov, Yuri V.; Samaan, Nader A.; Etingov, Pavel V.

    2013-03-19

    Given the multi-scale variability and uncertainty of wind generation and forecast errors, it is a natural choice to use time-frequency representation (TFR) as a view of the corresponding time series represented over both time and frequency. Here we use wavelet transform (WT) to expand the signal in terms of wavelet functions which are localized in both time and frequency. Each WT component is more stationary and has consistent auto-correlation pattern. We combined wavelet analyses with time series forecast approaches such as ARIMA, and tested the approach at three different wind farms located far away from each other. The prediction capability is satisfactory -- the day-ahead prediction of errors match the original error values very well, including the patterns. The observations are well located within the predictive intervals. Integrating our wavelet-ARIMA (‘stochastic’) model with the weather forecast model (‘deterministic’) will improve our ability significantly to predict wind power generation and reduce predictive uncertainty.

  9. EEG Theta Dynamics within Frontal and Parietal Cortices for Error Processing during Reaching Movements in a Prism Adaptation Study Altering Visuo-Motor Predictive Planning.

    PubMed

    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

  10. EEG Theta Dynamics within Frontal and Parietal Cortices for Error Processing during Reaching Movements in a Prism Adaptation Study Altering Visuo-Motor Predictive Planning

    PubMed Central

    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

  11. Phasic dopamine as a prediction error of intrinsic and extrinsic reinforcements driving both action acquisition and reward maximization: a simulated robotic study.

    PubMed

    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.

  12. Computing reward-prediction error: an integrated account of cortical timing and basal-ganglia pathways for appetitive and aversive learning.

    PubMed

    Morita, Kenji; Kawaguchi, Yasuo

    2015-08-01

    There are two prevailing notions regarding the involvement of the corticobasal ganglia system in value-based learning: (i) the direct and indirect pathways of the basal ganglia are crucial for appetitive and aversive learning, respectively, and (ii) the activity of midbrain dopamine neurons represents reward-prediction error. Although (ii) constitutes a critical assumption of (i), it remains elusive how (ii) holds given (i), with the basal-ganglia influence on the dopamine neurons. Here we present a computational neural-circuit model that potentially resolves this issue. Based on the latest analyses of the heterogeneous corticostriatal neurons and connections, our model posits that the direct and indirect pathways, respectively, represent the values of upcoming and previous actions, and up-regulate and down-regulate the dopamine neurons via the basal-ganglia output nuclei. This explains how the difference between the upcoming and previous values, which constitutes the core of reward-prediction error, is calculated. Simultaneously, it predicts that blockade of the direct/indirect pathway causes a negative/positive shift of reward-prediction error and thereby impairs learning from positive/negative error, i.e. appetitive/aversive learning. Through simulation of reward-reversal learning and punishment-avoidance learning, we show that our model could indeed account for the experimentally observed features that are suggested to support notion (i) and could also provide predictions on neural activity. We also present a behavioral prediction of our model, through simulation of inter-temporal choice, on how the balance between the two pathways relates to the subject's time preference. These results indicate that our model, incorporating the heterogeneity of the cortical influence on the basal ganglia, is expected to provide a closed-circuit mechanistic understanding of appetitive/aversive learning.

  13. Computing reward-prediction error: an integrated account of cortical timing and basal-ganglia pathways for appetitive and aversive learning.

    PubMed

    Morita, Kenji; Kawaguchi, Yasuo

    2015-08-01

    There are two prevailing notions regarding the involvement of the corticobasal ganglia system in value-based learning: (i) the direct and indirect pathways of the basal ganglia are crucial for appetitive and aversive learning, respectively, and (ii) the activity of midbrain dopamine neurons represents reward-prediction error. Although (ii) constitutes a critical assumption of (i), it remains elusive how (ii) holds given (i), with the basal-ganglia influence on the dopamine neurons. Here we present a computational neural-circuit model that potentially resolves this issue. Based on the latest analyses of the heterogeneous corticostriatal neurons and connections, our model posits that the direct and indirect pathways, respectively, represent the values of upcoming and previous actions, and up-regulate and down-regulate the dopamine neurons via the basal-ganglia output nuclei. This explains how the difference between the upcoming and previous values, which constitutes the core of reward-prediction error, is calculated. Simultaneously, it predicts that blockade of the direct/indirect pathway causes a negative/positive shift of reward-prediction error and thereby impairs learning from positive/negative error, i.e. appetitive/aversive learning. Through simulation of reward-reversal learning and punishment-avoidance learning, we show that our model could indeed account for the experimentally observed features that are suggested to support notion (i) and could also provide predictions on neural activity. We also present a behavioral prediction of our model, through simulation of inter-temporal choice, on how the balance between the two pathways relates to the subject's time preference. These results indicate that our model, incorporating the heterogeneity of the cortical influence on the basal ganglia, is expected to provide a closed-circuit mechanistic understanding of appetitive/aversive learning. PMID:26095906

  14. Enabling Predictive Simulation and UQ of Complex Multiphysics PDE Systems by the Development of Goal-Oriented Variational Sensitivity Analysis and a-Posteriori Error Estimation Methods

    SciTech Connect

    Estep, Donald

    2015-11-30

    This project addressed the challenge of predictive computational analysis of strongly coupled, highly nonlinear multiphysics systems characterized by multiple physical phenomena that span a large range of length- and time-scales. Specifically, the project was focused on computational estimation of numerical error and sensitivity analysis of computational solutions with respect to variations in parameters and data. In addition, the project investigated the use of accurate computational estimates to guide efficient adaptive discretization. The project developed, analyzed and evaluated new variational adjoint-based techniques for integration, model, and data error estimation/control and sensitivity analysis, in evolutionary multiphysics multiscale simulations.

  15. Absolute biological needs.

    PubMed

    McLeod, Stephen

    2014-07-01

    Absolute needs (as against instrumental needs) are independent of the ends, goals and purposes of personal agents. Against the view that the only needs are instrumental needs, David Wiggins and Garrett Thomson have defended absolute needs on the grounds that the verb 'need' has instrumental and absolute senses. While remaining neutral about it, this article does not adopt that approach. Instead, it suggests that there are absolute biological needs. The absolute nature of these needs is defended by appeal to: their objectivity (as against mind-dependence); the universality of the phenomenon of needing across the plant and animal kingdoms; the impossibility that biological needs depend wholly upon the exercise of the abilities characteristic of personal agency; the contention that the possession of biological needs is prior to the possession of the abilities characteristic of personal agency. Finally, three philosophical usages of 'normative' are distinguished. On two of these, to describe a phenomenon or claim as 'normative' is to describe it as value-dependent. A description of a phenomenon or claim as 'normative' in the third sense does not entail such value-dependency, though it leaves open the possibility that value depends upon the phenomenon or upon the truth of the claim. It is argued that while survival needs (or claims about them) may well be normative in this third sense, they are normative in neither of the first two. Thus, the idea of absolute need is not inherently normative in either of the first two senses. PMID:23586876

  16. Absolute biological needs.

    PubMed

    McLeod, Stephen

    2014-07-01

    Absolute needs (as against instrumental needs) are independent of the ends, goals and purposes of personal agents. Against the view that the only needs are instrumental needs, David Wiggins and Garrett Thomson have defended absolute needs on the grounds that the verb 'need' has instrumental and absolute senses. While remaining neutral about it, this article does not adopt that approach. Instead, it suggests that there are absolute biological needs. The absolute nature of these needs is defended by appeal to: their objectivity (as against mind-dependence); the universality of the phenomenon of needing across the plant and animal kingdoms; the impossibility that biological needs depend wholly upon the exercise of the abilities characteristic of personal agency; the contention that the possession of biological needs is prior to the possession of the abilities characteristic of personal agency. Finally, three philosophical usages of 'normative' are distinguished. On two of these, to describe a phenomenon or claim as 'normative' is to describe it as value-dependent. A description of a phenomenon or claim as 'normative' in the third sense does not entail such value-dependency, though it leaves open the possibility that value depends upon the phenomenon or upon the truth of the claim. It is argued that while survival needs (or claims about them) may well be normative in this third sense, they are normative in neither of the first two. Thus, the idea of absolute need is not inherently normative in either of the first two senses.

  17. Temporal Uncertainty and Temporal Estimation Errors Affect Insular Activity and the Frontostriatal Indirect Pathway during Action Update: A Predictive Coding Study

    PubMed Central

    Limongi, Roberto; Pérez, Francisco J.; Modroño, Cristián; González-Mora, José L.

    2016-01-01

    Action update, substituting a prepotent behavior with a new action, allows the organism to counteract surprising environmental demands. However, action update fails when the organism is uncertain about when to release the substituting behavior, when it faces temporal uncertainty. Predictive coding states that accurate perception demands minimization of precise prediction errors. Activity of the right anterior insula (rAI) is associated with temporal uncertainty. Therefore, we hypothesize that temporal uncertainty during action update would cause the AI to decrease the sensitivity to ascending prediction errors. Moreover, action update requires response inhibition which recruits the frontostriatal indirect pathway associated with motor control. Therefore, we also hypothesize that temporal estimation errors modulate frontostriatal connections. To test these hypotheses, we collected fMRI data when participants performed an action-update paradigm within the context of temporal estimation. We fit dynamic causal models to the imaging data. Competing models comprised the inferior occipital gyrus (IOG), right supramarginal gyrus (rSMG), rAI, right presupplementary motor area (rPreSMA), and the right striatum (rSTR). The winning model showed that temporal uncertainty drove activity into the rAI and decreased insular sensitivity to ascending prediction errors, as shown by weak connectivity strength of rSMG→rAI connections. Moreover, temporal estimation errors weakened rPreSMA→rSTR connections and also modulated rAI→rSTR connections, causing the disruption of action update. Results provide information about the neurophysiological implementation of the so-called horse-race model of action control. We suggest that, contrary to what might be believed, unsuccessful action update could be a homeostatic process that represents a Bayes optimal encoding of uncertainty. PMID:27445737

  18. Self-Reported and Observed Punitive Parenting Prospectively Predicts Increased Error-Related Brain Activity in Six-Year-Old Children.

    PubMed

    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

  19. Self-Reported and Observed Punitive Parenting Prospectively Predicts Increased Error-Related Brain Activity in Six-Year-Old Children.

    PubMed

    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

  20. Issues in Absolute Spectral Radiometric Calibration: Intercomparison of Eight Sources

    NASA Technical Reports Server (NTRS)

    Goetz, Alexander F. H.; Kindel, Bruce; Pilewskie, Peter

    1998-01-01

    The application of atmospheric models to AVIRIS and other spectral imaging data to derive surface reflectance requires that the sensor output be calibrated to absolute radiance. Uncertainties in absolute calibration are to be expected, and claims of 92% accuracy have been published. Measurements of accurate surface albedos and cloud absorption to be used in radiative balance calculations depend critically on knowing the absolute spectral-radiometric response of the sensor. The Earth Observing System project is implementing a rigorous program of absolute radiometric calibration for all optical sensors. Since a number of imaging instruments that provide output in terms of absolute radiance are calibrated at different sites, it is important to determine the errors that can be expected among calibration sites. Another question exists about the errors in the absolute knowledge of the exoatmospheric spectral solar irradiance.

  1. Estimating the absolute wealth of households

    PubMed Central

    Gerkey, Drew; Hadley, Craig

    2015-01-01

    Abstract Objective To estimate the absolute wealth of households using data from demographic and health surveys. Methods We developed a new metric, the absolute wealth estimate, based on the rank of each surveyed household according to its material assets and the assumed shape of the distribution of wealth among surveyed households. Using data from 156 demographic and health surveys in 66 countries, we calculated absolute wealth estimates for households. We validated the method by comparing the proportion of households defined as poor using our estimates with published World Bank poverty headcounts. We also compared the accuracy of absolute versus relative wealth estimates for the prediction of anthropometric measures. Findings The median absolute wealth estimates of 1 403 186 households were 2056 international dollars per capita (interquartile range: 723–6103). The proportion of poor households based on absolute wealth estimates were strongly correlated with World Bank estimates of populations living on less than 2.00 United States dollars per capita per day (R2 = 0.84). Absolute wealth estimates were better predictors of anthropometric measures than relative wealth indexes. Conclusion Absolute wealth estimates provide new opportunities for comparative research to assess the effects of economic resources on health and human capital, as well as the long-term health consequences of economic change and inequality. PMID:26170506

  2. Moderation of the Relationship Between Reward Expectancy and Prediction Error-Related Ventral Striatal Reactivity by Anhedonia in Unmedicated Major Depressive Disorder: Findings From the EMBARC Study

    PubMed Central

    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

  3. Evaluating the performance of the LPC (Linear Predictive Coding) 2.4 kbps (kilobits per second) processor with bit errors using a sentence verification task

    NASA Astrophysics Data System (ADS)

    Schmidt-Nielsen, Astrid; Kallman, Howard J.

    1987-11-01

    The comprehension of narrowband digital speech with bit errors was tested by using a sentence verification task. The use of predicates that were either strongly or weakly related to the subjects (e.g., A toad has warts./ A toad has eyes.) varied the difficulty of the verification task. The test conditions included unprocessed and processed speech using a 2.4 kb/s (kilobits per second) linear predictive coding (LPC) voice processing algorithm with random bit error rates of 0 percent, 2 percent, and 5 percent. In general, response accuracy decreased and reaction time increased with LPC processing and with increasing bit error rates. Weakly related true sentences and strongly related false sentences were more difficult than their counterparts. Interactions between sentence type and speech processing conditions are discussed.

  4. The absolute path command

    2012-05-11

    The ap command traveres all symlinks in a given file, directory, or executable name to identify the final absolute path. It can print just the final path, each intermediate link along with the symlink chan, and the permissions and ownership of each directory component in the final path. It has functionality similar to "which", except that it shows the final path instead of the first path. It is also similar to "pwd", but it canmore » provide the absolute path to a relative directory from the current working directory.« less

  5. The absolute path command

    SciTech Connect

    Moody, A.

    2012-05-11

    The ap command traveres all symlinks in a given file, directory, or executable name to identify the final absolute path. It can print just the final path, each intermediate link along with the symlink chan, and the permissions and ownership of each directory component in the final path. It has functionality similar to "which", except that it shows the final path instead of the first path. It is also similar to "pwd", but it can provide the absolute path to a relative directory from the current working directory.

  6. [Absolute risk fracture prediction by risk factors validation and survey of osteoporosis in a Brussels cohort followed during 10 years (FRISBEE study)].

    PubMed

    Body, J J; Moreau, M; Bergmann, P; Paesmans, M; Dekelver, C; Lemaire, M L

    2008-09-01

    Osteoporosis is a major public health problem. For the time being, the diagnosis of osteoporosis relies on densitometry (T-score < -2.5 by DXA), although the risk of fracture depends also on other factors than the bone mass. Osteoporosis diagnosis (DXA) must be distinguished from the individual risk assessment of fracture. Different risk factors complementary to bone mass have been already validated in different populations. These include an old age, a history of fracture after the age of 50, a familial history of hip fracture (father or mother), a low BMI (< 20), corticoid treatment (> 3 months), tabagism and excessive alcohol consumption. A WHO taskforce has combined these different factors in order to integrate them in a 10-years predictive risk model of fracture (FRAX**). This model should still be validated in different populations, especially in populations not included in its development, which is the case for Belgium. We are evaluating these different risk factors for fracture in a Brussels population of 5000 women (60-80 years) who will be followed each year during 10 years. We also assess the predictive value of other risk factors for fracture not included in the WHO model (tendency to fall, use of sleeping pills, early non substituted menopause, sedentarity, ...). In an interim analysis of the first 452 women included and with data yet available at the time of this writing, we could find a significant (P < 0.05) relationship between diagnosis of osteoporosis at DXA and the number of risk factors, age > 70 years, a personal history of fracture after 50 years and a BMI < 20. PMID:18949979

  7. Relationship between optimal precursory disturbances and optimally growing initial errors associated with ENSO events: Implications to target observations for ENSO prediction

    NASA Astrophysics Data System (ADS)

    Hu, Junya; Duan, Wansuo

    2016-05-01

    By superimposing initial sea temperature disturbances in neutral years, we determine the precursory disturbances that are most likely to evolve into El Niño and La Niña events using an Earth System Model. These precursory disturbances for El Niño and La Niña events are deemed optimal precursory disturbances because they are more likely to trigger strong ENSO events. Specifically, the optimal precursory disturbance for El Niño exhibits negative sea surface temperature anomalies (SSTAs) in the central-eastern equatorial Pacific. Additionally, the subsurface temperature component exhibits negative anomalies in the upper layers of the eastern equatorial Pacific and positive anomalies in the lower layers of the western equatorial Pacific. The optimal precursory disturbance for La Niña is almost opposite to that of El Niño. The optimal precursory disturbances show that both El Niño and La Niña originate from precursory signals in the subsurface layers of the western equatorial Pacific and in the surface layers of the eastern equatorial Pacific. We find that the optimal precursory disturbances for El Niño and La Niña are particularly similar to the optimally growing initial errors associated with El Niño prediction that have been presented in previous studies. The optimally growing initial errors show that the optimal precursor source areas represent the sensitive areas for target observations associated with ENSO prediction. Combining the optimal precursory disturbances and the optimally growing initial errors for ENSO, we infer that additional observations in these sensitive areas can reduce initial errors and be used to detect precursory signals, thereby improving ENSO predictions.

  8. ResQ: An Approach to Unified Estimation of B-Factor and Residue-Specific Error in Protein Structure Prediction.

    PubMed

    Yang, Jianyi; Wang, Yan; Zhang, Yang

    2016-02-22

    Computer-based structure prediction becomes a major tool to provide large-scale structure models for annotating biological function of proteins. Information of residue-level accuracy and thermal mobility (or B-factor), which is critical to decide how biologists utilize the predicted models, is however missed in most structure prediction pipelines. We developed ResQ for unified residue-level model quality and B-factor estimations by combining local structure assembly variations with sequence-based and structure-based profiling. ResQ was tested on 635 non-redundant proteins with structure models generated by I-TASSER, where the average difference between estimated and observed distance errors is 1.4Å for the confidently modeled proteins. ResQ was further tested on structure decoys from CASP9-11 experiments, where the error of local structure quality prediction is consistently lower than or comparable to other state-of-the-art predictors. Finally, ResQ B-factor profile was used to assist molecular replacement, which resulted in successful solutions on several proteins that could not be solved from constant B-factor settings. PMID:26437129

  9. The dynamics of error growth in a quasigeostrophic channel model

    NASA Technical Reports Server (NTRS)

    Straus, David M.

    1988-01-01

    The objective of the paper is to determine the extent to which baroclinic instability contributes to the growth of errors in simple, yet realistic models of atmospheric flow. The model used here is a two-level quasi-geostrophic channel model. Results of two predictability experiments are reported. In one experiment, the initial condition perturbation was confined to the highest wavenumbers and had an energy of 1 percent of the climatological energy of the model for these scales. In the other experiment, perturbations were put only in the planetary wave and had the same strength relative to climatology as in the first experiment, leading to much larger absolute errors.

  10. Combined Use of Absolute and Differential Seismic Arrival Time Data to Improve Absolute Event Location

    NASA Astrophysics Data System (ADS)

    Myers, S.; Johannesson, G.

    2012-12-01

    Arrival time measurements based on waveform cross correlation are becoming more common as advanced signal processing methods are applied to seismic data archives and real-time data streams. Waveform correlation can precisely measure the time difference between the arrival of two phases, and differential time data can be used to constrain relative location of events. Absolute locations are needed for many applications, which generally requires the use of absolute time data. Current methods for measuring absolute time data are approximately two orders of magnitude less precise than differential time measurements. To exploit the strengths of both absolute and differential time data, we extend our multiple-event location method Bayesloc, which previously used absolute time data only, to include the use of differential time measurements that are based on waveform cross correlation. Fundamentally, Bayesloc is a formulation of the joint probability over all parameters comprising the multiple event location system. The Markov-Chain Monte Carlo method is used to sample from the joint probability distribution given arrival data sets. The differential time component of Bayesloc includes scaling a stochastic estimate of differential time measurement precision based the waveform correlation coefficient for each datum. For a regional-distance synthetic data set with absolute and differential time measurement error of 0.25 seconds and 0.01 second, respectively, epicenter location accuracy is improved from and average of 1.05 km when solely absolute time data are used to 0.28 km when absolute and differential time data are used jointly (73% improvement). The improvement in absolute location accuracy is the result of conditionally limiting absolute location probability regions based on the precise relative position with respect to neighboring events. Bayesloc estimates of data precision are found to be accurate for the synthetic test, with absolute and differential time measurement

  11. Integrated Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC) and Isobaric Tags for Relative and Absolute Quantitation (iTRAQ) Quantitative Proteomic Analysis Identifies Galectin-1 as a Potential Biomarker for Predicting Sorafenib Resistance in Liver Cancer.

    PubMed

    Yeh, Chao-Chi; Hsu, Chih-Hung; Shao, Yu-Yun; Ho, Wen-Ching; Tsai, Mong-Hsun; Feng, Wen-Chi; Chow, Lu-Ping

    2015-06-01

    Sorafenib has become the standard therapy for patients with advanced hepatocellular carcinoma (HCC). Unfortunately, most patients eventually develop acquired resistance. Therefore, it is important to identify potential biomarkers that could predict the efficacy of sorafenib. To identify target proteins associated with the development of sorafenib resistance, we applied stable isotope labelling with amino acids in cell culture (SILAC)-based quantitative proteomic approach to analyze differences in protein expression levels between parental HuH-7 and sorafenib-acquired resistance HuH-7 (HuH-7(R)) cells in vitro, combined with an isobaric tags for relative and absolute quantitation (iTRAQ) quantitative analysis of HuH-7 and HuH-7(R) tumors in vivo. In total, 2,450 quantified proteins were identified in common in SILAC and iTRAQ experiments, with 81 showing increased expression (>2.0-fold) with sorafenib resistance and 75 showing decreased expression (<0.5-fold). In silico analyses of these differentially expressed proteins predicted that 10 proteins were related to cancer with involvements in cell adhesion, migration, and invasion. Knockdown of one of these candidate proteins, galectin-1, decreased cell proliferation and metastasis in HuH-7(R) cells and restored sensitivity to sorafenib. We verified galectin-1 as a predictive marker of sorafenib resistance and a downstream target of the AKT/mTOR/HIF-1α signaling pathway. In addition, increased galectin-1 expression in HCC patients' serum was associated with poor tumor control and low response rate. We also found that a high serum galectin-1 level was an independent factor associated with poor progression-free survival and overall survival. In conclusion, these results suggest that galectin-1 is a possible biomarker for predicting the response of HCC patients to treatment with sorafenib. As such, it may assist in the stratification of HCC and help direct personalized therapy.

  12. Integrated Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC) and Isobaric Tags for Relative and Absolute Quantitation (iTRAQ) Quantitative Proteomic Analysis Identifies Galectin-1 as a Potential Biomarker for Predicting Sorafenib Resistance in Liver Cancer*

    PubMed Central

    Yeh, Chao-Chi; Hsu, Chih-Hung; Shao, Yu-Yun; Ho, Wen-Ching; Tsai, Mong-Hsun; Feng, Wen-Chi; Chow, Lu-Ping

    2015-01-01

    Sorafenib has become the standard therapy for patients with advanced hepatocellular carcinoma (HCC). Unfortunately, most patients eventually develop acquired resistance. Therefore, it is important to identify potential biomarkers that could predict the efficacy of sorafenib. To identify target proteins associated with the development of sorafenib resistance, we applied stable isotope labelling with amino acids in cell culture (SILAC)-based quantitative proteomic approach to analyze differences in protein expression levels between parental HuH-7 and sorafenib-acquired resistance HuH-7 (HuH-7R) cells in vitro, combined with an isobaric tags for relative and absolute quantitation (iTRAQ) quantitative analysis of HuH-7 and HuH-7R tumors in vivo. In total, 2,450 quantified proteins were identified in common in SILAC and iTRAQ experiments, with 81 showing increased expression (>2.0-fold) with sorafenib resistance and 75 showing decreased expression (<0.5-fold). In silico analyses of these differentially expressed proteins predicted that 10 proteins were related to cancer with involvements in cell adhesion, migration, and invasion. Knockdown of one of these candidate proteins, galectin-1, decreased cell proliferation and metastasis in HuH-7R cells and restored sensitivity to sorafenib. We verified galectin-1 as a predictive marker of sorafenib resistance and a downstream target of the AKT/mTOR/HIF-1α signaling pathway. In addition, increased galectin-1 expression in HCC patients' serum was associated with poor tumor control and low response rate. We also found that a high serum galectin-1 level was an independent factor associated with poor progression-free survival and overall survival. In conclusion, these results suggest that galectin-1 is a possible biomarker for predicting the response of HCC patients to treatment with sorafenib. As such, it may assist in the stratification of HCC and help direct personalized therapy. PMID:25850433

  13. Accepting error to make less error.

    PubMed

    Einhorn, H J

    1986-01-01

    In this article I argue that the clinical and statistical approaches rest on different assumptions about the nature of random error and the appropriate level of accuracy to be expected in prediction. To examine this, a case is made for each approach. The clinical approach is characterized as being deterministic, causal, and less concerned with prediction than with diagnosis and treatment. The statistical approach accepts error as inevitable and in so doing makes less error in prediction. This is illustrated using examples from probability learning and equal weighting in linear models. Thereafter, a decision analysis of the two approaches is proposed. Of particular importance are the errors that characterize each approach: myths, magic, and illusions of control in the clinical; lost opportunities and illusions of the lack of control in the statistical. Each approach represents a gamble with corresponding risks and benefits.

  14. Determining what caused the error in the prediction of the December 1st, 2013 snow storm using the Weather Research and Forecasting Model

    NASA Astrophysics Data System (ADS)

    Prajapati, Nikunjkumar; Trout, Joseph

    2014-03-01

    The severity of snow events in the northeast United States depends on the position of the pressure systems and the fronts. Although numerical models have improved greatly as computer power has increased, occasionally the forecasts of the pressure systems and fronts can have large margins of error. For example, the snow storm which passed over the north east coast on the week of December 1, 2013, which proved to be much more severe than predicted. In this research, The Weather Research and Forecasting Model(WRF-Model) is used to model the December 1, 2013 storm. Multiple simulations using nested, high resolution grids are compared. Research in computational atmospheric physics.

  15. Development and application of an empirical probability distribution for the prediction error of re-entry body maximum dynamic pressure

    NASA Technical Reports Server (NTRS)

    Lanzi, R. James; Vincent, Brett T.

    1993-01-01

    The relationship between actual and predicted re-entry maximum dynamic pressure is characterized using a probability density function and a cumulative distribution function derived from sounding rocket flight data. This paper explores the properties of this distribution and demonstrates applications of this data with observed sounding rocket re-entry body damage characteristics to assess probabilities of sustaining various levels of heating damage. The results from this paper effectively bridge the gap existing in sounding rocket reentry analysis between the known damage level/flight environment relationships and the predicted flight environment.

  16. Absolute Plate Velocities from Seismic Anisotropy

    NASA Astrophysics Data System (ADS)

    Kreemer, Corné; Zheng, Lin; Gordon, Richard

    2015-04-01

    The orientation of seismic anisotropy inferred beneath plate interiors may provide a means to estimate the motions of the plate relative to the sub-asthenospheric mantle. Here we analyze two global sets of shear-wave splitting data, that of Kreemer [2009] and an updated and expanded data set, to estimate plate motions and to better understand the dispersion of the data, correlations in the errors, and their relation to plate speed. We also explore the effect of using geologically current plate velocities (i.e., the MORVEL set of angular velocities [DeMets et al. 2010]) compared with geodetically current plate velocities (i.e., the GSRM v1.2 angular velocities [Kreemer et al. 2014]). We demonstrate that the errors in plate motion azimuths inferred from shear-wave splitting beneath any one tectonic plate are correlated with the errors of other azimuths from the same plate. To account for these correlations, we adopt a two-tier analysis: First, find the pole of rotation and confidence limits for each plate individually. Second, solve for the best fit to these poles while constraining relative plate angular velocities to consistency with the MORVEL relative plate angular velocities. The SKS-MORVEL absolute plate angular velocities (based on the Kreemer [2009] data set) are determined from the poles from eight plates weighted proportionally to the root-mean-square velocity of each plate. SKS-MORVEL indicates that eight plates (Amur, Antarctica, Caribbean, Eurasia, Lwandle, Somalia, Sundaland, and Yangtze) have angular velocities that differ insignificantly from zero. The net rotation of the lithosphere is 0.25±0.11° Ma-1 (95% confidence limits) right-handed about 57.1°S, 68.6°E. The within-plate dispersion of seismic anisotropy for oceanic lithosphere (σ=19.2° ) differs insignificantly from that for continental lithosphere (σ=21.6° ). The between-plate dispersion, however, is significantly smaller for oceanic lithosphere (σ=7.4° ) than for continental

  17. Optogenetic stimulation in a computational model of the basal ganglia biases action selection and reward prediction error.

    PubMed

    Berthet, Pierre; Lansner, Anders

    2014-01-01

    Optogenetic stimulation of specific types of medium spiny neurons (MSNs) in the striatum has been shown to bias the selection of mice in a two choices task. This shift is dependent on the localisation and on the intensity of the stimulation but also on the recent reward history. We have implemented a way to simulate this increased activity produced by the optical flash in our computational model of the basal ganglia (BG). This abstract model features the direct and indirect pathways commonly described in biology, and a reward prediction pathway (RP). The framework is similar to Actor-Critic methods and to the ventral/dorsal distinction in the striatum. We thus investigated the impact on the selection caused by an added stimulation in each of the three pathways. We were able to reproduce in our model the bias in action selection observed in mice. Our results also showed that biasing the reward prediction is sufficient to create a modification in the action selection. However, we had to increase the percentage of trials with stimulation relative to that in experiments in order to impact the selection. We found that increasing only the reward prediction had a different effect if the stimulation in RP was action dependent (only for a specific action) or not. We further looked at the evolution of the change in the weights depending on the stage of learning within a block. A bias in RP impacts the plasticity differently depending on that stage but also on the outcome. It remains to experimentally test how the dopaminergic neurons are affected by specific stimulations of neurons in the striatum and to relate data to predictions of our model. PMID:24614169

  18. Optogenetic Stimulation in a Computational Model of the Basal Ganglia Biases Action Selection and Reward Prediction Error

    PubMed Central

    Berthet, Pierre; Lansner, Anders

    2014-01-01

    Optogenetic stimulation of specific types of medium spiny neurons (MSNs) in the striatum has been shown to bias the selection of mice in a two choices task. This shift is dependent on the localisation and on the intensity of the stimulation but also on the recent reward history. We have implemented a way to simulate this increased activity produced by the optical flash in our computational model of the basal ganglia (BG). This abstract model features the direct and indirect pathways commonly described in biology, and a reward prediction pathway (RP). The framework is similar to Actor-Critic methods and to the ventral/dorsal distinction in the striatum. We thus investigated the impact on the selection caused by an added stimulation in each of the three pathways. We were able to reproduce in our model the bias in action selection observed in mice. Our results also showed that biasing the reward prediction is sufficient to create a modification in the action selection. However, we had to increase the percentage of trials with stimulation relative to that in experiments in order to impact the selection. We found that increasing only the reward prediction had a different effect if the stimulation in RP was action dependent (only for a specific action) or not. We further looked at the evolution of the change in the weights depending on the stage of learning within a block. A bias in RP impacts the plasticity differently depending on that stage but also on the outcome. It remains to experimentally test how the dopaminergic neurons are affected by specific stimulations of neurons in the striatum and to relate data to predictions of our model. PMID:24614169

  19. ABSOLUTE POLARIMETRY AT RHIC.

    SciTech Connect

    OKADA; BRAVAR, A.; BUNCE, G.; GILL, R.; HUANG, H.; MAKDISI, Y.; NASS, A.; WOOD, J.; ZELENSKI, Z.; ET AL.

    2007-09-10

    Precise and absolute beam polarization measurements are critical for the RHIC spin physics program. Because all experimental spin-dependent results are normalized by beam polarization, the normalization uncertainty contributes directly to final physics uncertainties. We aimed to perform the beam polarization measurement to an accuracy Of {Delta}P{sub beam}/P{sub beam} < 5%. The absolute polarimeter consists of Polarized Atomic Hydrogen Gas Jet Target and left-right pairs of silicon strip detectors and was installed in the RHIC-ring in 2004. This system features proton-proton elastic scattering in the Coulomb nuclear interference (CNI) region. Precise measurements of the analyzing power A{sub N} of this process has allowed us to achieve {Delta}P{sub beam}/P{sub beam} = 4.2% in 2005 for the first long spin-physics run. In this report, we describe the entire set up and performance of the system. The procedure of beam polarization measurement and analysis results from 2004-2005 are described. Physics topics of AN in the CNI region (four-momentum transfer squared 0.001 < -t < 0.032 (GeV/c){sup 2}) are also discussed. We point out the current issues and expected optimum accuracy in 2006 and the future.

  20. Speech Errors across the Lifespan

    ERIC Educational Resources Information Center

    Vousden, Janet I.; Maylor, Elizabeth A.

    2006-01-01

    Dell, Burger, and Svec (1997) proposed that the proportion of speech errors classified as anticipations (e.g., "moot and mouth") can be predicted solely from the overall error rate, such that the greater the error rate, the lower the anticipatory proportion (AP) of errors. We report a study examining whether this effect applies to changes in error…

  1. Prospects for the Moon as an SI-Traceable Absolute Spectroradiometric Standard for Satellite Remote Sensing

    NASA Astrophysics Data System (ADS)

    Cramer, C. E.; Stone, T. C.; Lykke, K.; Woodward, J. T.

    2015-12-01

    The Earth's Moon has many physical properties that make it suitable for use as a reference light source for radiometric calibration of remote sensing satellite instruments. Lunar calibration has been successfully applied to many imagers in orbit, including both MODIS instruments and NPP-VIIRS, using the USGS ROLO model to predict the reference exoatmospheric lunar irradiance. Sensor response trending was developed for SeaWIFS with a relative accuracy better than 0.1 % per year with lunar calibration techniques. However, the Moon rarely is used as an absolute reference for on-orbit calibration, primarily due to uncertainties in the ROLO model absolute scale of 5%-10%. But this limitation lies only with the models - the Moon itself is radiometrically stable, and development of a high-accuracy absolute lunar reference is inherently feasible. A program has been undertaken by NIST to collect absolute measurements of the lunar spectral irradiance with absolute accuracy <1 % (k=2), traceable to SI radiometric units. Initial Moon observations were acquired from the Whipple Observatory on Mt. Hopkins, Arizona, elevation 2367 meters, with continuous spectral coverage from 380 nm to 1040 nm at ~3 nm resolution. The lunar spectrometer acquired calibration measurements several times each observing night by pointing to a calibrated integrating sphere source. The lunar spectral irradiance at the top of the atmosphere was derived from a time series of ground-based measurements by a Langley analysis that incorporated measured atmospheric conditions and ROLO model predictions for the change in irradiance resulting from the changing Sun-Moon-Observer geometry throughout each night. Two nights were selected for further study. An extensive error analysis, which includes instrument calibration and atmospheric correction terms, shows a combined standard uncertainty under 1 % over most of the spectral range. Comparison of these two nights' spectral irradiance measurements with predictions

  2. Absolute Equilibrium Entropy

    NASA Technical Reports Server (NTRS)

    Shebalin, John V.

    1997-01-01

    The entropy associated with absolute equilibrium ensemble theories of ideal, homogeneous, fluid and magneto-fluid turbulence is discussed and the three-dimensional fluid case is examined in detail. A sigma-function is defined, whose minimum value with respect to global parameters is the entropy. A comparison is made between the use of global functions sigma and phase functions H (associated with the development of various H-theorems of ideal turbulence). It is shown that the two approaches are complimentary though conceptually different: H-theorems show that an isolated system tends to equilibrium while sigma-functions allow the demonstration that entropy never decreases when two previously isolated systems are combined. This provides a more complete picture of entropy in the statistical mechanics of ideal fluids.

  3. Absolute calibration in vivo measurement systems

    SciTech Connect

    Kruchten, D.A.; Hickman, D.P.

    1991-02-01

    Lawrence Livermore National Laboratory (LLNL) is currently investigating a new method for obtaining absolute calibration factors for radiation measurement systems used to measure internally deposited radionuclides in vivo. Absolute calibration of in vivo measurement systems will eliminate the need to generate a series of human surrogate structures (i.e., phantoms) for calibrating in vivo measurement systems. The absolute calibration of in vivo measurement systems utilizes magnetic resonance imaging (MRI) to define physiological structure, size, and composition. The MRI image provides a digitized representation of the physiological structure, which allows for any mathematical distribution of radionuclides within the body. Using Monte Carlo transport codes, the emission spectrum from the body is predicted. The in vivo measurement equipment is calibrated using the Monte Carlo code and adjusting for the intrinsic properties of the detection system. The calibration factors are verified using measurements of existing phantoms and previously obtained measurements of human volunteers. 8 refs.

  4. Stimulus probability effects in absolute identification.

    PubMed

    Kent, Christopher; Lamberts, Koen

    2016-05-01

    This study investigated the effect of stimulus presentation probability on accuracy and response times in an absolute identification task. Three schedules of presentation were used to investigate the interaction between presentation probability and stimulus position within the set. Data from individual participants indicated strong effects of presentation probability on both proportion correct and response times. The effects were moderated by the ubiquitous stimulus position effect. The accuracy and response time data were predicted by an exemplar-based model of perceptual cognition (Kent & Lamberts, 2005). The bow in discriminability was also attenuated when presentation probability for middle items was relatively high, an effect that will constrain future model development. The study provides evidence for item-specific learning in absolute identification. Implications for other theories of absolute identification are discussed. (PsycINFO Database Record

  5. Refractive Errors

    MedlinePlus

    ... and lens of your eye helps you focus. Refractive errors are vision problems that happen when the shape ... cornea, or aging of the lens. Four common refractive errors are Myopia, or nearsightedness - clear vision close up ...

  6. Major Source of Error in QSPR Prediction of Intrinsic Thermodynamic Solubility of Drugs: Solid vs Nonsolid State Contributions?

    PubMed

    Abramov, Yuriy A

    2015-06-01

    The main purpose of this study is to define the major limiting factor in the accuracy of the quantitative structure-property relationship (QSPR) models of the thermodynamic intrinsic aqueous solubility of the drug-like compounds. For doing this, the thermodynamic intrinsic aqueous solubility property was suggested to be indirectly "measured" from the contributions of solid state, ΔGfus, and nonsolid state, ΔGmix, properties, which are estimated by the corresponding QSPR models. The QSPR models of ΔGfus and ΔGmix properties were built based on a set of drug-like compounds with available accurate measurements of fusion and thermodynamic solubility properties. For consistency ΔGfus and ΔGmix models were developed using similar algorithms and descriptor sets, and validated against the similar test compounds. Analysis of the relative performances of these two QSPR models clearly demonstrates that it is the solid state contribution which is the limiting factor in the accuracy and predictive power of the QSPR models of the thermodynamic intrinsic solubility. The performed analysis outlines a necessity of development of new descriptor sets for an accurate description of the long-range order (periodicity) phenomenon in the crystalline state. The proposed approach to the analysis of limitations and suggestions for improvement of QSPR-type models may be generalized to other applications in the pharmaceutical industry.

  7. Using Air Temperature to Quantitatively Predict the MODIS Fractional Snow Cover Retrieval Errors over the Continental US (CONUS)

    NASA Technical Reports Server (NTRS)

    Dong, Jiarui; Ek, Mike; Hall, Dorothy K.; Peters-Lidard, Christa; Cosgrove, Brian; Miller, Jeff; Riggs, George A.; Xia, Youlong

    2013-01-01

    In the middle to high latitude and alpine regions, the seasonal snow pack can dominate the surface energy and water budgets due to its high albedo, low thermal conductivity, high emissivity, considerable spatial and temporal variability, and ability to store and then later release a winters cumulative snowfall (Cohen, 1994; Hall, 1998). With this in mind, the snow drought across the U.S. has raised questions about impacts on water supply, ski resorts and agriculture. Knowledge of various snow pack properties is crucial for short-term weather forecasts, climate change prediction, and hydrologic forecasting for producing reliable daily to seasonal forecasts. One potential source of this information is the multi-institution North American Land Data Assimilation System (NLDAS) project (Mitchell et al., 2004). Real-time NLDAS products are used for drought monitoring to support the National Integrated Drought Information System (NIDIS) and as initial conditions for a future NCEP drought forecast system. Additionally, efforts are currently underway to assimilate remotely-sensed estimates of land-surface states such as snowpack information into NLDAS. It is believed that this assimilation will not only produce improved snowpack states that better represent snow evolving conditions, but will directly improve the monitoring of drought.

  8. Hemispheric Asymmetries in Striatal Reward Responses Relate to Approach-Avoidance Learning and Encoding of Positive-Negative Prediction Errors in Dopaminergic Midbrain Regions.

    PubMed

    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.

  9. Hemispheric Asymmetries in Striatal Reward Responses Relate to Approach-Avoidance Learning and Encoding of Positive-Negative Prediction Errors in Dopaminergic Midbrain Regions.

    PubMed

    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. PMID:26511241

  10. Possible sources of forecast errors generated by the global/regional assimilation and prediction system for landfalling tropical cyclones. Part I: Initial uncertainties

    NASA Astrophysics Data System (ADS)

    Zhou, Feifan; Yamaguchi, Munehiko; Qin, Xiaohao

    2016-07-01

    This paper investigates the possible sources of errors associated with tropical cyclone (TC) tracks forecasted using the Global/Regional Assimilation and Prediction System (GRAPES). The GRAPES forecasts were made for 16 landfalling TCs in the western North Pacific basin during the 2008 and 2009 seasons, with a forecast length of 72 hours, and using the default initial conditions ("initials", hereafter), which are from the NCEP-FNL dataset, as well as ECMWF initials. The forecasts are compared with ECMWF forecasts. The results show that in most TCs, the GRAPES forecasts are improved when using the ECMWF initials compared with the default initials. Compared with the ECMWF initials, the default initials produce lower intensity TCs and a lower intensity subtropical high, but a higher intensity South Asia high and monsoon trough, as well as a higher temperature but lower specific humidity at the TC center. Replacement of the geopotential height and wind fields with the ECMWF initials in and around the TC center at the initial time was found to be the most efficient way to improve the forecasts. In addition, TCs that showed the greatest improvement in forecast accuracy usually had the largest initial uncertainties in TC intensity and were usually in the intensifying phase. The results demonstrate the importance of the initial intensity for TC track forecasts made using GRAPES, and indicate the model is better in describing the intensifying phase than the decaying phase of TCs. Finally, the limit of the improvement indicates that the model error associated with GRAPES forecasts may be the main cause of poor forecasts of landfalling TCs. Thus, further examinations of the model errors are required.

  11. The initial errors that induce a significant "spring predictability barrier" for El Niño events and their implications for target observation: results from an earth system model

    NASA Astrophysics Data System (ADS)

    Duan, Wansuo; Hu, Junya

    2016-06-01

    The National Center for Atmospheric Research Community Earth System Model is used to study the "spring predictability barrier" (SPB) problem for El Niño events from the perspective of initial error growth. By conducting perfect model predictability experiments, we obtain two types of initial sea temperature errors, which often exhibit obvious season-dependent evolution and cause a significant SPB when predicting the onset of El Niño events bestriding spring. One type of initial errors possesses a sea surface temperature anomaly (SSTA) pattern with negative anomalies in the central-eastern equatorial Pacific, plus a basin-wide dipolar subsurface temperature anomaly pattern with negative anomalies in the upper layers of the eastern equatorial Pacific and positive anomalies in the lower layers of the western equatorial Pacific. The other type consists of an SSTA component with positive anomalies over the southeastern equatorial Pacific, plus a large-scale zonal dipole pattern of the subsurface temperature anomaly with positive anomalies in the upper layers of the eastern equatorial Pacific and negative anomalies in the lower layers of the central-western equatorial Pacific. Both exhibit a La Niña-like evolving mode and cause an under-prediction for Niño-3 SSTA of El Niño events. For the former initial error type, the resultant prediction errors grow in a manner similar to the behavior of the growth phase of La Niña; while for the latter initial error type, they experience a process that is similar to El Niño decay and transition to a La Niña growth phase. Both two types of initial errors cause negative prediction errors of Niño-3 SSTA for El Niño events. The prediction errors for Niño-3 SSTA are mainly due to the contribution of initial sea temperature errors in the large-error-related regions in the upper layers of the eastern tropical Pacific and/or in the lower layers of the western tropical Pacific. These regions may represent ``sensitive areas'' for El

  12. Reconsideration of measurement of error in human motor learning.

    PubMed

    Crabtree, D A; Antrim, L R

    1988-10-01

    Human motor learning is often measured by error scores. The convention of using mean absolute error, mean constant error, and variable error shows lack of desirable parsimony and interpretability. This paper provides the background of error measurement and states criticisms of conventional methodology. A parsimonious model of error analysis is provided, along with operationalized interpretations and implications for motor learning. Teaching, interpreting, and using error scores in research may be simplified and facilitated with the model.

  13. Constraint on Absolute Accuracy of Metacomprehension Assessments: The Anchoring and Adjustment Model vs. the Standards Model

    ERIC Educational Resources Information Center

    Kwon, Heekyung

    2011-01-01

    The objective of this study is to provide a systematic account of three typical phenomena surrounding absolute accuracy of metacomprehension assessments: (1) the absolute accuracy of predictions is typically quite low; (2) there exist individual differences in absolute accuracy of predictions as a function of reading skill; and (3) postdictions…

  14. Absolute neutrino mass measurements

    SciTech Connect

    Wolf, Joachim

    2011-10-06

    The neutrino mass plays an important role in particle physics, astrophysics and cosmology. In recent years the detection of neutrino flavour oscillations proved that neutrinos carry mass. However, oscillation experiments are only sensitive to the mass-squared difference of the mass eigenvalues. In contrast to cosmological observations and neutrino-less double beta decay (0v2{beta}) searches, single {beta}-decay experiments provide a direct, model-independent way to determine the absolute neutrino mass by measuring the energy spectrum of decay electrons at the endpoint region with high accuracy.Currently the best kinematic upper limits on the neutrino mass of 2.2eV have been set by two experiments in Mainz and Troitsk, using tritium as beta emitter. The next generation tritium {beta}-experiment KATRIN is currently under construction in Karlsruhe/Germany by an international collaboration. KATRIN intends to improve the sensitivity by one order of magnitude to 0.2eV. The investigation of a second isotope ({sup 137}Rh) is being pursued by the international MARE collaboration using micro-calorimeters to measure the beta spectrum. The technology needed to reach 0.2eV sensitivity is still in the R and D phase. This paper reviews the present status of neutrino-mass measurements with cosmological data, 0v2{beta} decay and single {beta}-decay.

  15. Absolute method of measuring magnetic susceptibility

    USGS Publications Warehouse

    Thorpe, A.; Senftle, F.E.

    1959-01-01

    An absolute method of standardization and measurement of the magnetic susceptibility of small samples is presented which can be applied to most techniques based on the Faraday method. The fact that the susceptibility is a function of the area under the curve of sample displacement versus distance of the magnet from the sample, offers a simple method of measuring the susceptibility without recourse to a standard sample. Typical results on a few substances are compared with reported values, and an error of less than 2% can be achieved. ?? 1959 The American Institute of Physics.

  16. Absolute radiometric calibration of the CCRS SAR

    NASA Astrophysics Data System (ADS)

    Ulander, Lars M. H.; Hawkins, Robert K.; Livingstone, Charles E.; Lukowski, Tom I.

    1991-11-01

    Determining the radar scattering coefficients from SAR (synthetic aperture radar) image data requires absolute radiometric calibration of the SAR system. The authors describe an internal calibration methodology for the airborne Canada Centre for Remote Sensing (CCRS) SAR system, based on radar theory, a detailed model of the radar system, and measurements of system parameters. The methodology is verified by analyzing external calibration data acquired over a 6-month period in 1988 by the C-band radar using HH polarization. The results indicate that the overall error is +/- 0.8 dB (1-sigma) for incidence angles +/- 20 deg from antenna boresight. The dominant error contributions are due to the antenna radome and uncertainties in the elevation angle relative to the antenna boresight.

  17. Absolute Identification by Relative Judgment

    ERIC Educational Resources Information Center

    Stewart, Neil; Brown, Gordon D. A.; Chater, Nick

    2005-01-01

    In unidimensional absolute identification tasks, participants identify stimuli that vary along a single dimension. Performance is surprisingly poor compared with discrimination of the same stimuli. Existing models assume that identification is achieved using long-term representations of absolute magnitudes. The authors propose an alternative…

  18. Be Resolute about Absolute Value

    ERIC Educational Resources Information Center

    Kidd, Margaret L.

    2007-01-01

    This article explores how conceptualization of absolute value can start long before it is introduced. The manner in which absolute value is introduced to students in middle school has far-reaching consequences for their future mathematical understanding. It begins to lay the foundation for students' understanding of algebra, which can change…

  19. The Carina Project: Absolute and Relative Calibrations

    NASA Astrophysics Data System (ADS)

    Corsi, C. E.; Bono, G.; Walker, A. R.; Brocato, E.; Buonanno, R.; Caputo, F.; Castellani, M.; Castellani, V.; Dall'Ora, M.; Marconi, M.; Monelli, M.; Nonino, M.; Pulone, L.; Ripepi, V.; Smith, H. A.

    We discuss the reduction strategy adopted to perform the relative and the absolute calibration of the Wide Field Imager (WFI) available at the 2.2m ESO/MPI telescope and of the Mosaic Camera (MC) available at the 4m CTIO Blanco telescope. To properly constrain the occurrence of deceptive systematic errors in the relative calibration we observed with each chip the same set of stars. Current photometry seems to suggest that the WFI shows a positional effect when moving from the top to the bottom of individual chips. Preliminary results based on an independent data set collected with the MC suggest that this camera is only marginally affected by the same problem. To perform the absolute calibration we observed with each chip the same set of standard stars. The sample covers a wide color range and the accuracy both in the B and in the V-band appears to be of the order of a few hundredths of magnitude. Finally, we briefly outline the observing strategy to improve both relative and absolute calibrations of mosaic CCD cameras.

  20. Experimental results for absolute cylindrical wavefront testing

    NASA Astrophysics Data System (ADS)

    Reardon, Patrick J.; Alatawi, Ayshah

    2014-09-01

    Applications for Cylindrical and near-cylindrical surfaces are ever-increasing. However, fabrication of high quality cylindrical surfaces is limited by the difficulty of accurate and affordable metrology. Absolute testing of such surfaces represents a challenge to the optical testing community as cylindrical reference wavefronts are difficult to produce. In this paper, preliminary results for a new method of absolute testing of cylindrical wavefronts are presented. The method is based on the merging of the random ball test method with the fiber optic reference test. The random ball test assumes a large number of interferograms of a good quality sphere with errors that are statistically distributed such that the average of the errors goes to zero. The fiber optic reference test utilizes a specially processed optical fiber to provide a clean high quality reference wave from an incident line focus from the cylindrical wave under test. By taking measurements at different rotation and translations of the fiber, an analogous procedure can be employed to determine the quality of the converging cylindrical wavefront with high accuracy. This paper presents and discusses the results of recent tests of this method using a null optic formed by a COTS cylindrical lens and a free-form polished corrector element.

  1. Error Analysis

    NASA Astrophysics Data System (ADS)

    Scherer, Philipp O. J.

    Input data as well as the results of elementary operations have to be represented by machine numbers, the subset of real numbers which is used by the arithmetic unit of today's computers. Generally this generates rounding errors. This kind of numerical error can be avoided in principle by using arbitrary precision arithmetics or symbolic algebra programs. But this is unpractical in many cases due to the increase in computing time and memory requirements. Results from more complex operations like square roots or trigonometric functions can have even larger errors since series expansions have to be truncated and iterations accumulate the errors of the individual steps. In addition, the precision of input data from an experiment is limited. In this chapter we study the influence of numerical errors on the uncertainties of the calculated results and the stability of simple algorithms.

  2. Proofreading for word errors.

    PubMed

    Pilotti, Maura; Chodorow, Martin; Agpawa, Ian; Krajniak, Marta; Mahamane, Salif

    2012-04-01

    Proofreading (i.e., reading text for the purpose of detecting and correcting typographical errors) is viewed as a component of the activity of revising text and thus is a necessary (albeit not sufficient) procedural step for enhancing the quality of a written product. The purpose of the present research was to test competing accounts of word-error detection which predict factors that may influence reading and proofreading differently. Word errors, which change a word into another word (e.g., from --> form), were selected for examination because they are unlikely to be detected by automatic spell-checking functions. Consequently, their detection still rests mostly in the hands of the human proofreader. Findings highlighted the weaknesses of existing accounts of proofreading and identified factors, such as length and frequency of the error in the English language relative to frequency of the correct word, which might play a key role in detection of word errors.

  3. Absolute magnitudes and kinematics of barium stars.

    NASA Astrophysics Data System (ADS)

    Gomez, A. E.; Luri, X.; Grenier, S.; Prevot, L.; Mennessier, M. O.; Figueras, F.; Torra, J.

    1997-03-01

    The absolute magnitude of barium stars has been obtained from kinematical data using a new algorithm based on the maximum-likelihood principle. The method allows to separate a sample into groups characterized by different mean absolute magnitudes, kinematics and z-scale heights. It also takes into account, simultaneously, the censorship in the sample and the errors on the observables. The method has been applied to a sample of 318 barium stars. Four groups have been detected. Three of them show a kinematical behaviour corresponding to disk population stars. The fourth group contains stars with halo kinematics. The luminosities of the disk population groups spread a large range. The intrinsically brightest one (M_v_=-1.5mag, σ_M_=0.5mag) seems to be an inhomogeneous group containing barium binaries as well as AGB single stars. The most numerous group (about 150 stars) has a mean absolute magnitude corresponding to stars in the red giant branch (M_v_=0.9mag, σ_M_=0.8mag). The third group contains barium dwarfs, the obtained mean absolute magnitude is characteristic of stars on the main sequence or on the subgiant branch (M_v_=3.3mag, σ_M_=0.5mag). The obtained mean luminosities as well as the kinematical results are compatible with an evolutionary link between barium dwarfs and classical barium giants. The highly luminous group is not linked with these last two groups. More high-resolution spectroscopic data will be necessary in order to better discriminate between barium and non-barium stars.

  4. Effects of affective arousal on choice behavior, reward prediction errors, and feedback-related negativities in human reward-based decision making.

    PubMed

    Liu, Hong-Hsiang; Hsieh, Ming H; Hsu, Yung-Fong; Lai, Wen-Sung

    2015-01-01

    Emotional experience has a pervasive impact on choice behavior, yet the underlying mechanism remains unclear. Introducing facial-expression primes into a probabilistic learning task, we investigated how affective arousal regulates reward-related choice based on behavioral, model fitting, and feedback-related negativity (FRN) data. Sixty-six paid subjects were randomly assigned to the Neutral-Neutral (NN), Angry-Neutral (AN), and Happy-Neutral (HN) groups. A total of 960 trials were conducted. Subjects in each group were randomly exposed to half trials of the pre-determined emotional faces and another half of the neutral faces before choosing between two cards drawn from two decks with different assigned reward probabilities. Trial-by-trial data were fit with a standard reinforcement learning model using the Bayesian estimation approach. The temporal dynamics of brain activity were simultaneously recorded and analyzed using event-related potentials. Our analyses revealed that subjects in the NN group gained more reward values than those in the other two groups; they also exhibited comparatively differential estimated model-parameter values for reward prediction errors. Computing the difference wave of FRNs in reward vs. non-reward trials, we found that, compared to the NN group, subjects in the AN and HN groups had larger "General" FRNs (i.e., FRNs in no-reward trials minus FRNs in reward trials) and "Expected" FRNs (i.e., FRNs in expected reward-omission trials minus FRNs in expected reward-delivery trials), indicating an interruption in predicting reward. Further, both AN and HN groups appeared to be more sensitive to negative outcomes than the NN group. Collectively, our study suggests that affective arousal negatively regulates reward-related choice, probably through overweighting with negative feedback.

  5. Effects of affective arousal on choice behavior, reward prediction errors, and feedback-related negativities in human reward-based decision making

    PubMed Central

    Liu, Hong-Hsiang; Hsieh, Ming H.; Hsu, Yung-Fong; Lai, Wen-Sung

    2015-01-01

    Emotional experience has a pervasive impact on choice behavior, yet the underlying mechanism remains unclear. Introducing facial-expression primes into a probabilistic learning task, we investigated how affective arousal regulates reward-related choice based on behavioral, model fitting, and feedback-related negativity (FRN) data. Sixty-six paid subjects were randomly assigned to the Neutral-Neutral (NN), Angry-Neutral (AN), and Happy-Neutral (HN) groups. A total of 960 trials were conducted. Subjects in each group were randomly exposed to half trials of the pre-determined emotional faces and another half of the neutral faces before choosing between two cards drawn from two decks with different assigned reward probabilities. Trial-by-trial data were fit with a standard reinforcement learning model using the Bayesian estimation approach. The temporal dynamics of brain activity were simultaneously recorded and analyzed using event-related potentials. Our analyses revealed that subjects in the NN group gained more reward values than those in the other two groups; they also exhibited comparatively differential estimated model-parameter values for reward prediction errors. Computing the difference wave of FRNs in reward vs. non-reward trials, we found that, compared to the NN group, subjects in the AN and HN groups had larger “General” FRNs (i.e., FRNs in no-reward trials minus FRNs in reward trials) and “Expected” FRNs (i.e., FRNs in expected reward-omission trials minus FRNs in expected reward-delivery trials), indicating an interruption in predicting reward. Further, both AN and HN groups appeared to be more sensitive to negative outcomes than the NN group. Collectively, our study suggests that affective arousal negatively regulates reward-related choice, probably through overweighting with negative feedback. PMID:26042057

  6. Individual differences in reward prediction error: contrasting relations between feedback-related negativity and trait measures of reward sensitivity, impulsivity and extraversion

    PubMed Central

    Cooper, Andrew J.; Duke, Éilish; Pickering, Alan D.; Smillie, Luke D.

    2014-01-01

    Medial-frontal negativity occurring ∼200–300 ms post-stimulus in response to motivationally salient stimuli, usually referred to as feedback-related negativity (FRN), appears to be at least partly modulated by dopaminergic-based reward prediction error (RPE) signaling. Previous research (e.g., Smillie et al., 2011) has shown that higher scores on a putatively dopaminergic-based personality trait, extraversion, were associated with a more pronounced difference wave contrasting unpredicted non-reward and unpredicted reward trials on an associative learning task. In the current study, we sought to extend this research by comparing how trait measures of reward sensitivity, impulsivity and extraversion related to the FRN using the same associative learning task. A sample of healthy adults (N = 38) completed a battery of personality questionnaires, before completing the associative learning task while EEG was recorded. As expected, FRN was most negative following unpredicted non-reward. A difference wave contrasting unpredicted non-reward and unpredicted reward trials was calculated. Extraversion, but not measures of impulsivity, had a significant association with this difference wave. Further, the difference wave was significantly related to a measure of anticipatory pleasure, but not consummatory pleasure. These findings provide support for the existing evidence suggesting that variation in dopaminergic functioning in brain “reward” pathways may partially underpin associations between the FRN and trait measures of extraversion and anticipatory pleasure. PMID:24808845

  7. Medication Errors

    MedlinePlus

    ... to reduce the risk of medication errors to industry and others at FDA. Additionally, DMEPA prospectively reviews ... List of Abbreviations Regulations and Guidances Guidance for Industry: Safety Considerations for Product Design to Minimize Medication ...

  8. Medication Errors

    MedlinePlus

    Medicines cure infectious diseases, prevent problems from chronic diseases, and ease pain. But medicines can also cause harmful reactions if not used ... You can help prevent errors by Knowing your medicines. Keep a list of the names of your ...

  9. Absolute radiometric calibration of advanced remote sensing systems

    NASA Technical Reports Server (NTRS)

    Slater, P. N.

    1982-01-01

    The distinction between the uses of relative and absolute spectroradiometric calibration of remote sensing systems is discussed. The advantages of detector-based absolute calibration are described, and the categories of relative and absolute system calibrations are listed. The limitations and problems associated with three common methods used for the absolute calibration of remote sensing systems are addressed. Two methods are proposed for the in-flight absolute calibration of advanced multispectral linear array systems. One makes use of a sun-illuminated panel in front of the sensor, the radiance of which is monitored by a spectrally flat pyroelectric radiometer. The other uses a large, uniform, high-radiance reference ground surface. The ground and atmospheric measurements required as input to a radiative transfer program to predict the radiance level at the entrance pupil of the orbital sensor are discussed, and the ground instrumentation is described.

  10. 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.

  11. Singular perturbation of absolute stability.

    NASA Technical Reports Server (NTRS)

    Siljak, D. D.

    1972-01-01

    It was previously shown (author, 1969) that the regions of absolute stability in the parameter space can be determined when the parameters appear on the right-hand side of the system equations, i.e., the regular case. Here, the effect on absolute stability of a small parameter attached to higher derivatives in the equations (the singular case) is studied. The Lur'e-Postnikov class of nonlinear systems is considered.

  12. The Error in Total Error Reduction

    PubMed Central

    Witnauer, James E.; Urcelay, Gonzalo P.; Miller, Ralph R.

    2013-01-01

    Most models of human and animal learning assume that learning is proportional to the discrepancy between a delivered outcome and the outcome predicted by all cues present during that trial (i.e., total error across a stimulus compound). This total error reduction (TER) view has been implemented in connectionist and artificial neural network models to describe the conditions under which weights between units change. Electrophysiological work has revealed that the activity of dopamine neurons is correlated with the total error signal in models of reward learning. Similar neural mechanisms presumably support fear conditioning, human contingency learning, and other types of learning. Using a computational modelling approach, we compared several TER models of associative learning to an alternative model that rejects the TER assumption in favor of local error reduction (LER), which assumes that learning about each cue is proportional to the discrepancy between the delivered outcome and the outcome predicted by that specific cue on that trial. The LER model provided a better fit to the reviewed data than the TER models. Given the superiority of the LER model with the present data sets, acceptance of TER should be tempered. PMID:23891930

  13. Absolute Radiometer for Reproducing the Solar Irradiance Unit

    NASA Astrophysics Data System (ADS)

    Sapritskii, V. I.; Pavlovich, M. N.

    1989-01-01

    A high-precision absolute radiometer with a thermally stabilized cavity as receiving element has been designed for use in solar irradiance measurements. The State Special Standard of the Solar Irradiance Unit has been built on the basis of the developed absolute radiometer. The Standard also includes the sun tracking system and the system for automatic thermal stabilization and information processing, comprising a built-in microcalculator which calculates the irradiance according to the input program. During metrological certification of the Standard, main error sources have been analysed and the non-excluded systematic and accidental errors of the irradiance-unit realization have been determined. The total error of the Standard does not exceed 0.3%. Beginning in 1984 the Standard has been taking part in a comparison with the Å 212 pyrheliometer and other Soviet and foreign standards. In 1986 it took part in the international comparison of absolute radiometers and standard pyrheliometers of socialist countries. The results of the comparisons proved the high metrological quality of this Standard based on an absolute radiometer.

  14. Absolute geostrophic currents in global tropical oceans

    NASA Astrophysics Data System (ADS)

    Yang, Lina; Yuan, Dongliang

    2016-11-01

    A set of absolute geostrophic current (AGC) data for the period January 2004 to December 2012 are calculated using the P-vector method based on monthly gridded Argo profiles in the world tropical oceans. The AGCs agree well with altimeter geostrophic currents, Ocean Surface Current Analysis-Real time currents, and moored current-meter measurements at 10-m depth, based on which the classical Sverdrup circulation theory is evaluated. Calculations have shown that errors of wind stress calculation, AGC transport, and depth ranges of vertical integration cannot explain non-Sverdrup transport, which is mainly in the subtropical western ocean basins and equatorial currents near the Equator in each ocean basin (except the North Indian Ocean, where the circulation is dominated by monsoons). The identified non-Sverdrup transport is thereby robust and attributed to the joint effect of baroclinicity and relief of the bottom (JEBAR) and mesoscale eddy nonlinearity.

  15. Absolute flux scale for radioastronomy

    SciTech Connect

    Ivanov, V.P.; Stankevich, K.S.

    1986-07-01

    The authors propose and provide support for a new absolute flux scale for radio astronomy, which is not encumbered with the inadequacies of the previous scales. In constructing it the method of relative spectra was used (a powerful tool for choosing reference spectra). A review is given of previous flux scales. The authors compare the AIS scale with the scale they propose. Both scales are based on absolute measurements by the ''artificial moon'' method, and they are practically coincident in the range from 0.96 to 6 GHz. At frequencies above 6 GHz, 0.96 GHz, the AIS scale is overestimated because of incorrect extrapolation of the spectra of the primary and secondary standards. The major results which have emerged from this review of absolute scales in radio astronomy are summarized.

  16. Absolute Proper Motions of Southern Globular Clusters

    NASA Astrophysics Data System (ADS)

    Dinescu, D. I.; Girard, T. M.; van Altena, W. F.

    1996-05-01

    Our program involves the determination of absolute proper motions with respect to galaxies for a sample of globular clusters situated in the southern sky. The plates cover a 6(deg) x 6(deg) area and are taken with the 51-cm double astrograph at Cesco Observatory in El Leoncito, Argentina. We have developed special methods to deal with the modelling error of the plate transformation and we correct for magnitude equation using the cluster stars. This careful astrometric treatment leads to accuracies of from 0.5 to 1.0 mas/yr for the absolute proper motion of each cluster, depending primarily on the number of measurable cluster stars which in turn is related to the cluster's distance. Space velocities are then derived which, in association with metallicities, provide key information for the formation scenario of the Galaxy, i.e. accretion and/or dissipational collapse. Here we present results for NGC 1851, NGC 6752, NGC 6584, NGC 6362 and NGC 288.

  17. Error control in the GCF: An information-theoretic model for error analysis and coding

    NASA Technical Reports Server (NTRS)

    Adeyemi, O.

    1974-01-01

    The structure of data-transmission errors within the Ground Communications Facility is analyzed in order to provide error control (both forward error correction and feedback retransmission) for improved communication. Emphasis is placed on constructing a theoretical model of errors and obtaining from it all the relevant statistics for error control. No specific coding strategy is analyzed, but references to the significance of certain error pattern distributions, as predicted by the model, to error correction are made.

  18. A Simple Model Predicting Individual Weight Change in Humans

    PubMed Central

    Thomas, Diana M.; Martin, Corby K.; Heymsfield, Steven; Redman, Leanne M.; Schoeller, Dale A.; Levine, James A.

    2010-01-01

    Excessive weight in adults is a national concern with over 2/3 of the US population deemed overweight. Because being overweight has been correlated to numerous diseases such as heart disease and type 2 diabetes, there is a need to understand mechanisms and predict outcomes of weight change and weight maintenance. A simple mathematical model that accurately predicts individual weight change offers opportunities to understand how individuals lose and gain weight and can be used to foster patient adherence to diets in clinical settings. For this purpose, we developed a one dimensional differential equation model of weight change based on the energy balance equation is paired to an algebraic relationship between fat free mass and fat mass derived from a large nationally representative sample of recently released data collected by the Centers for Disease Control. We validate the model's ability to predict individual participants’ weight change by comparing model estimates of final weight data from two recent underfeeding studies and one overfeeding study. Mean absolute error and standard deviation between model predictions and observed measurements of final weights are less than 1.8 ± 1.3 kg for the underfeeding studies and 2.5 ± 1.6 kg for the overfeeding study. Comparison of the model predictions to other one dimensional models of weight change shows improvement in mean absolute error, standard deviation of mean absolute error, and group mean predictions. The maximum absolute individual error decreased by approximately 60% substantiating reliability in individual weight change predictions. The model provides a viable method for estimating individual weight change as a result of changes in intake and determining individual dietary adherence during weight change studies. PMID:24707319

  19. Computation of Standard Errors

    PubMed Central

    Dowd, Bryan E; Greene, William H; Norton, Edward C

    2014-01-01

    Objectives We discuss the problem of computing the standard errors of functions involving estimated parameters and provide the relevant computer code for three different computational approaches using two popular computer packages. Study Design We show how to compute the standard errors of several functions of interest: the predicted value of the dependent variable for a particular subject, and the effect of a change in an explanatory variable on the predicted value of the dependent variable for an individual subject and average effect for a sample of subjects. Empirical Application Using a publicly available dataset, we explain three different methods of computing standard errors: the delta method, Krinsky–Robb, and bootstrapping. We provide computer code for Stata 12 and LIMDEP 10/NLOGIT 5. Conclusions In most applications, choice of the computational method for standard errors of functions of estimated parameters is a matter of convenience. However, when computing standard errors of the sample average of functions that involve both estimated parameters and nonstochastic explanatory variables, it is important to consider the sources of variation in the function's values. PMID:24800304

  20. Error Analysis of non-TLD HDR Brachytherapy Dosimetric Techniques

    NASA Astrophysics Data System (ADS)

    Amoush, Ahmad

    The American Association of Physicists in Medicine Task Group Report43 (AAPM-TG43) and its updated version TG-43U1 rely on the LiF TLD detector to determine the experimental absolute dose rate for brachytherapy. The recommended uncertainty estimates associated with TLD experimental dosimetry include 5% for statistical errors (Type A) and 7% for systematic errors (Type B). TG-43U1 protocol does not include recommendation for other experimental dosimetric techniques to calculate the absolute dose for brachytherapy. This research used two independent experimental methods and Monte Carlo simulations to investigate and analyze uncertainties and errors associated with absolute dosimetry of HDR brachytherapy for a Tandem applicator. An A16 MicroChamber* and one dose MOSFET detectors† were selected to meet the TG-43U1 recommendations for experimental dosimetry. Statistical and systematic uncertainty analyses associated with each experimental technique were analyzed quantitatively using MCNPX 2.6‡ to evaluate source positional error, Tandem positional error, the source spectrum, phantom size effect, reproducibility, temperature and pressure effects, volume averaging, stem and wall effects, and Tandem effect. Absolute dose calculations for clinical use are based on Treatment Planning System (TPS) with no corrections for the above uncertainties. Absolute dose and uncertainties along the transverse plane were predicted for the A16 microchamber. The generated overall uncertainties are 22%, 17%, 15%, 15%, 16%, 17%, and 19% at 1cm, 2cm, 3cm, 4cm, and 5cm, respectively. Predicting the dose beyond 5cm is complicated due to low signal-to-noise ratio, cable effect, and stem effect for the A16 microchamber. Since dose beyond 5cm adds no clinical information, it has been ignored in this study. The absolute dose was predicted for the MOSFET detector from 1cm to 7cm along the transverse plane. The generated overall uncertainties are 23%, 11%, 8%, 7%, 7%, 9%, and 8% at 1cm, 2cm, 3cm

  1. Relativistic Absolutism in Moral Education.

    ERIC Educational Resources Information Center

    Vogt, W. Paul

    1982-01-01

    Discusses Emile Durkheim's "Moral Education: A Study in the Theory and Application of the Sociology of Education," which holds that morally healthy societies may vary in culture and organization but must possess absolute rules of moral behavior. Compares this moral theory with current theory and practice of American educators. (MJL)

  2. 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…

  3. Absolute calibration of remote sensing instruments

    NASA Astrophysics Data System (ADS)

    Biggar, S. F.; Bruegge, C. J.; Capron, B. A.; Castle, K. R.; Dinguirard, M. C.; Holm, R. G.; Lingg, L. J.; Mao, Y.; Palmer, J. M.; Phillips, A. L.

    1985-12-01

    Source-based and detector-based methods for the absolute radiometric calibration of a broadband field radiometer are described. Using such a radiometer, calibrated by both methods, the calibration of the integrating sphere used in the preflight calibration of the Thematic Mapper was redetermined. The results are presented. The in-flight calibration of space remote sensing instruments is discussed. A method which uses the results of ground-based reflectance and atmospheric measurements as input to a radiative transfer code to predict the radiance at the instrument is described. A calibrated, helicopter-mounted radiometer is used to determine the radiance levels at intermediate altitudes to check the code predictions. Results of such measurements for the calibration of the Thematic Mapper on Landsat 5 and an analysis that shows the value of such measurements are described.

  4. Absolute Stability Analysis of a Phase Plane Controlled Spacecraft

    NASA Technical Reports Server (NTRS)

    Jang, Jiann-Woei; Plummer, Michael; Bedrossian, Nazareth; Hall, Charles; Jackson, Mark; Spanos, Pol

    2010-01-01

    Many aerospace attitude control systems utilize phase plane control schemes that include nonlinear elements such as dead zone and ideal relay. To evaluate phase plane control robustness, stability margin prediction methods must be developed. Absolute stability is extended to predict stability margins and to define an abort condition. A constrained optimization approach is also used to design flex filters for roll control. The design goal is to optimize vehicle tracking performance while maintaining adequate stability margins. Absolute stability is shown to provide satisfactory stability constraints for the optimization.

  5. Absolute measurement of the extreme UV solar flux

    NASA Technical Reports Server (NTRS)

    Carlson, R. W.; Ogawa, H. S.; Judge, D. L.; Phillips, E.

    1984-01-01

    A windowless rare-gas ionization chamber has been developed to measure the absolute value of the solar extreme UV flux in the 50-575-A region. Successful results were obtained on a solar-pointing sounding rocket. The ionization chamber, operated in total absorption, is an inherently stable absolute detector of ionizing UV radiation and was designed to be independent of effects from secondary ionization and gas effusion. The net error of the measurement is + or - 7.3 percent, which is primarily due to residual outgassing in the instrument, other errors such as multiple ionization, photoelectron collection, and extrapolation to the zero atmospheric optical depth being small in comparison. For the day of the flight, Aug. 10, 1982, the solar irradiance (50-575 A), normalized to unit solar distance, was found to be 5.71 + or - 0.42 x 10 to the 10th photons per sq cm sec.

  6. Mathematical Model for Absolute Magnetic Measuring Systems in Industrial Applications

    NASA Astrophysics Data System (ADS)

    Fügenschuh, Armin; Fügenschuh, Marzena; Ludszuweit, Marina; Mojsic, Aleksandar; Sokół, Joanna

    2015-09-01

    Scales for measuring systems are either based on incremental or absolute measuring methods. Incremental scales need to initialize a measurement cycle at a reference point. From there, the position is computed by counting increments of a periodic graduation. Absolute methods do not need reference points, since the position can be read directly from the scale. The positions on the complete scales are encoded using two incremental tracks with different graduation. We present a new method for absolute measuring using only one track for position encoding up to micrometre range. Instead of the common perpendicular magnetic areas, we use a pattern of trapezoidal magnetic areas, to store more complex information. For positioning, we use the magnetic field where every position is characterized by a set of values measured by a hall sensor array. We implement a method for reconstruction of absolute positions from the set of unique measured values. We compare two patterns with respect to uniqueness, accuracy, stability and robustness of positioning. We discuss how stability and robustness are influenced by different errors during the measurement in real applications and how those errors can be compensated.

  7. Explaining Errors in Children's Questions

    ERIC Educational Resources Information Center

    Rowland, Caroline F.

    2007-01-01

    The ability to explain the occurrence of errors in children's speech is an essential component of successful theories of language acquisition. The present study tested some generativist and constructivist predictions about error on the questions produced by ten English-learning children between 2 and 5 years of age. The analyses demonstrated that,…

  8. Prospective errors determine motor learning

    PubMed Central

    Takiyama, Ken; Hirashima, Masaya; Nozaki, Daichi

    2015-01-01

    Diverse features of motor learning have been reported by numerous studies, but no single theoretical framework concurrently accounts for these features. Here, we propose a model for motor learning to explain these features in a unified way by extending a motor primitive framework. The model assumes that the recruitment pattern of motor primitives is determined by the predicted movement error of an upcoming movement (prospective error). To validate this idea, we perform a behavioural experiment to examine the model’s novel prediction: after experiencing an environment in which the movement error is more easily predictable, subsequent motor learning should become faster. The experimental results support our prediction, suggesting that the prospective error might be encoded in the motor primitives. Furthermore, we demonstrate that this model has a strong explanatory power to reproduce a wide variety of motor-learning-related phenomena that have been separately explained by different computational models. PMID:25635628

  9. Optomechanics for absolute rotation detection

    NASA Astrophysics Data System (ADS)

    Davuluri, Sankar

    2016-07-01

    In this article, we present an application of optomechanical cavity for the absolute rotation detection. The optomechanical cavity is arranged in a Michelson interferometer in such a way that the classical centrifugal force due to rotation changes the length of the optomechanical cavity. The change in the cavity length induces a shift in the frequency of the cavity mode. The phase shift corresponding to the frequency shift in the cavity mode is measured at the interferometer output to estimate the angular velocity of absolute rotation. We derived an analytic expression to estimate the minimum detectable rotation rate in our scheme for a given optomechanical cavity. Temperature dependence of the rotation detection sensitivity is studied.

  10. Moral absolutism and ectopic pregnancy.

    PubMed

    Kaczor, C

    2001-02-01

    If one accepts a version of absolutism that excludes the intentional killing of any innocent human person from conception to natural death, ectopic pregnancy poses vexing difficulties. Given that the embryonic life almost certainly will die anyway, how can one retain one's moral principle and yet adequately respond to a situation that gravely threatens the life of the mother and her future fertility? The four options of treatment most often discussed in the literature are non-intervention, salpingectomy (removal of tube with embryo), salpingostomy (removal of embryo alone), and use of methotrexate (MXT). In this essay, I review these four options and introduce a fifth (the milking technique). In order to assess these options in terms of the absolutism mentioned, it will also be necessary to discuss various accounts of the intention/foresight distinction. I conclude that salpingectomy, salpingostomy, and the milking technique are compatible with absolutist presuppositions, but not the use of methotrexate.

  11. Moral absolutism and ectopic pregnancy.

    PubMed

    Kaczor, C

    2001-02-01

    If one accepts a version of absolutism that excludes the intentional killing of any innocent human person from conception to natural death, ectopic pregnancy poses vexing difficulties. Given that the embryonic life almost certainly will die anyway, how can one retain one's moral principle and yet adequately respond to a situation that gravely threatens the life of the mother and her future fertility? The four options of treatment most often discussed in the literature are non-intervention, salpingectomy (removal of tube with embryo), salpingostomy (removal of embryo alone), and use of methotrexate (MXT). In this essay, I review these four options and introduce a fifth (the milking technique). In order to assess these options in terms of the absolutism mentioned, it will also be necessary to discuss various accounts of the intention/foresight distinction. I conclude that salpingectomy, salpingostomy, and the milking technique are compatible with absolutist presuppositions, but not the use of methotrexate. PMID:11262641

  12. The Absolute Spectrum Polarimeter (ASP)

    NASA Technical Reports Server (NTRS)

    Kogut, A. J.

    2010-01-01

    The Absolute Spectrum Polarimeter (ASP) is an Explorer-class mission to map the absolute intensity and linear polarization of the cosmic microwave background and diffuse astrophysical foregrounds over the full sky from 30 GHz to 5 THz. The principal science goal is the detection and characterization of linear polarization from an inflationary epoch in the early universe, with tensor-to-scalar ratio r much greater than 1O(raised to the power of { -3}) and Compton distortion y < 10 (raised to the power of{-6}). We describe the ASP instrument and mission architecture needed to detect the signature of an inflationary epoch in the early universe using only 4 semiconductor bolometers.

  13. Accuracy of devices for self-monitoring of blood glucose: A stochastic error model.

    PubMed

    Vettoretti, M; Facchinetti, A; Sparacino, G; Cobelli, C

    2015-01-01

    Self-monitoring of blood glucose (SMBG) devices are portable systems that allow measuring glucose concentration in a small drop of blood obtained via finger-prick. SMBG measurements are key in type 1 diabetes (T1D) management, e.g. for tuning insulin dosing. A reliable model of SMBG accuracy would be important in several applications, e.g. in in silico design and optimization of insulin therapy. In the literature, the most used model to describe SMBG error is the Gaussian distribution, which however is simplistic to properly account for the observed variability. Here, a methodology to derive a stochastic model of SMBG accuracy is presented. The method consists in dividing the glucose range into zones in which absolute/relative error presents constant standard deviation (SD) and, then, fitting by maximum-likelihood a skew-normal distribution model to absolute/relative error distribution in each zone. The method was tested on a database of SMBG measurements collected by the One Touch Ultra 2 (Lifescan Inc., Milpitas, CA). In particular, two zones were identified: zone 1 (BG≤75 mg/dl) with constant-SD absolute error and zone 2 (BG>75mg/dl) with constant-SD relative error. Mean and SD of the identified skew-normal distributions are, respectively, 2.03 and 6.51 in zone 1, 4.78% and 10.09% in zone 2. Visual predictive check validation showed that the derived two-zone model accurately reproduces SMBG measurement error distribution, performing significantly better than the single-zone Gaussian model used previously in the literature. This stochastic model allows a more realistic SMBG scenario for in silico design and optimization of T1D insulin therapy.

  14. Enabling Predictive Simulation and UQ of Complex Multiphysics PDE Systems by the Development of Goal-Oriented Variational Sensitivity Analysis and A Posteriori Error Estimation Methods

    SciTech Connect

    Ginting, Victor

    2014-03-15

    it was demonstrated that a posteriori analyses in general and in particular one that uses adjoint methods can accurately and efficiently compute numerical error estimates and sensitivity for critical Quantities of Interest (QoIs) that depend on a large number of parameters. Activities include: analysis and implementation of several time integration techniques for solving system of ODEs as typically obtained from spatial discretization of PDE systems; multirate integration methods for ordinary differential equations; formulation and analysis of an iterative multi-discretization Galerkin finite element method for multi-scale reaction-diffusion equations; investigation of an inexpensive postprocessing technique to estimate the error of finite element solution of the second-order quasi-linear elliptic problems measured in some global metrics; investigation of an application of the residual-based a posteriori error estimates to symmetric interior penalty discontinuous Galerkin method for solving a class of second order quasi-linear elliptic problems; a posteriori analysis of explicit time integrations for system of linear ordinary differential equations; derivation of accurate a posteriori goal oriented error estimates for a user-defined quantity of interest for two classes of first and second order IMEX schemes for advection-diffusion-reaction problems; Postprocessing finite element solution; and A Bayesian Framework for Uncertain Quantification of Porous Media Flows.

  15. Achieving Climate Change Absolute Accuracy in Orbit

    NASA Technical Reports Server (NTRS)

    Wielicki, Bruce A.; Young, D. F.; Mlynczak, M. G.; Thome, K. J; Leroy, S.; Corliss, J.; Anderson, J. G.; Ao, C. O.; Bantges, R.; Best, F.; Bowman, K.; Brindley, H.; Butler, J. J.; Collins, W.; Dykema, J. A.; Doelling, D. R.; Feldman, D. R.; Fox, N.; Huang, X.; Holz, R.; Huang, Y.; Jennings, D.; Jin, Z.; Johnson, D. G.; Jucks, K.; Kato, S.; Kratz, D. P.; Liu, X.; Lukashin, C.; Mannucci, A. J.; Phojanamongkolkij, N.; Roithmayr, C. M.; Sandford, S.; Taylor, P. C.; Xiong, X.

    2013-01-01

    The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission will provide a calibration laboratory in orbit for the purpose of accurately measuring and attributing climate change. CLARREO measurements establish new climate change benchmarks with high absolute radiometric accuracy and high statistical confidence across a wide range of essential climate variables. CLARREO's inherently high absolute accuracy will be verified and traceable on orbit to Système Internationale (SI) units. The benchmarks established by CLARREO will be critical for assessing changes in the Earth system and climate model predictive capabilities for decades into the future as society works to meet the challenge of optimizing strategies for mitigating and adapting to climate change. The CLARREO benchmarks are derived from measurements of the Earth's thermal infrared spectrum (5-50 micron), the spectrum of solar radiation reflected by the Earth and its atmosphere (320-2300 nm), and radio occultation refractivity from which accurate temperature profiles are derived. The mission has the ability to provide new spectral fingerprints of climate change, as well as to provide the first orbiting radiometer with accuracy sufficient to serve as the reference transfer standard for other space sensors, in essence serving as a "NIST [National Institute of Standards and Technology] in orbit." CLARREO will greatly improve the accuracy and relevance of a wide range of space-borne instruments for decadal climate change. Finally, CLARREO has developed new metrics and methods for determining the accuracy requirements of climate observations for a wide range of climate variables and uncertainty sources. These methods should be useful for improving our understanding of observing requirements for most climate change observations.

  16. Clinical review: Medication errors in critical care

    PubMed Central

    Moyen, Eric; Camiré, Eric; Stelfox, Henry Thomas

    2008-01-01

    Medication errors in critical care are frequent, serious, and predictable. Critically ill patients are prescribed twice as many medications as patients outside of the intensive care unit (ICU) and nearly all will suffer a potentially life-threatening error at some point during their stay. The aim of this article is to provide a basic review of medication errors in the ICU, identify risk factors for medication errors, and suggest strategies to prevent errors and manage their consequences. PMID:18373883

  17. Quantifying error distributions in crowding.

    PubMed

    Hanus, Deborah; Vul, Edward

    2013-03-22

    When multiple objects are in close proximity, observers have difficulty identifying them individually. Two classes of theories aim to account for this crowding phenomenon: spatial pooling and spatial substitution. Variations of these accounts predict different patterns of errors in crowded displays. Here we aim to characterize the kinds of errors that people make during crowding by comparing a number of error models across three experiments in which we manipulate flanker spacing, display eccentricity, and precueing duration. We find that both spatial intrusions and individual letter confusions play a considerable role in errors. Moreover, we find no evidence that a naïve pooling model that predicts errors based on a nonadditive combination of target and flankers explains errors better than an independent intrusion model (indeed, in our data, an independent intrusion model is slightly, but significantly, better). Finally, we find that manipulating trial difficulty in any way (spacing, eccentricity, or precueing) produces homogenous changes in error distributions. Together, these results provide quantitative baselines for predictive models of crowding errors, suggest that pooling and spatial substitution models are difficult to tease apart, and imply that manipulations of crowding all influence a common mechanism that impacts subject performance.

  18. Dynamic diagnostics of the error fields in tokamaks

    NASA Astrophysics Data System (ADS)

    Pustovitov, V. D.

    2007-07-01

    The error field diagnostics based on magnetic measurements outside the plasma is discussed. The analysed methods rely on measuring the plasma dynamic response to the finite-amplitude external magnetic perturbations, which are the error fields and the pre-programmed probing pulses. Such pulses can be created by the coils designed for static error field correction and for stabilization of the resistive wall modes, the technique developed and applied in several tokamaks, including DIII-D and JET. Here analysis is based on the theory predictions for the resonant field amplification (RFA). To achieve the desired level of the error field correction in tokamaks, the diagnostics must be sensitive to signals of several Gauss. Therefore, part of the measurements should be performed near the plasma stability boundary, where the RFA effect is stronger. While the proximity to the marginal stability is important, the absolute values of plasma parameters are not. This means that the necessary measurements can be done in the diagnostic discharges with parameters below the nominal operating regimes, with the stability boundary intentionally lowered. The estimates for ITER are presented. The discussed diagnostics can be tested in dedicated experiments in existing tokamaks. The diagnostics can be considered as an extension of the 'active MHD spectroscopy' used recently in the DIII-D tokamak and the EXTRAP T2R reversed field pinch.

  19. Magnetospheric Multiscale (MMS) Mission Commissioning Phase Orbit Determination Error Analysis

    NASA Technical Reports Server (NTRS)

    Chung, Lauren R.; Novak, Stefan; Long, Anne; Gramling, Cheryl

    2009-01-01

    The Magnetospheric MultiScale (MMS) mission commissioning phase starts in a 185 km altitude x 12 Earth radii (RE) injection orbit and lasts until the Phase 1 mission orbits and orientation to the Earth-Sun li ne are achieved. During a limited time period in the early part of co mmissioning, five maneuvers are performed to raise the perigee radius to 1.2 R E, with a maneuver every other apogee. The current baseline is for the Goddard Space Flight Center Flight Dynamics Facility to p rovide MMS orbit determination support during the early commissioning phase using all available two-way range and Doppler tracking from bo th the Deep Space Network and Space Network. This paper summarizes th e results from a linear covariance analysis to determine the type and amount of tracking data required to accurately estimate the spacecraf t state, plan each perigee raising maneuver, and support thruster cal ibration during this phase. The primary focus of this study is the na vigation accuracy required to plan the first and the final perigee ra ising maneuvers. Absolute and relative position and velocity error hi stories are generated for all cases and summarized in terms of the ma ximum root-sum-square consider and measurement noise error contributi ons over the definitive and predictive arcs and at discrete times inc luding the maneuver planning and execution times. Details of the meth odology, orbital characteristics, maneuver timeline, error models, and error sensitivities are provided.

  20. Individual Differences in Absolute and Relative Metacomprehension Accuracy

    ERIC Educational Resources Information Center

    Maki, Ruth H.; Shields, Micheal; Wheeler, Amanda Easton; Zacchilli, Tammy Lowery

    2005-01-01

    The authors investigated absolute and relative metacomprehension accuracy as a function of verbal ability in college students. Students read hard texts, revised texts, or a mixed set of texts. They then predicted their performance, took a multiple-choice test on the texts, and made posttest judgments about their performance. With hard texts,…

  1. The AFGL absolute gravity program

    NASA Technical Reports Server (NTRS)

    Hammond, J. A.; Iliff, R. L.

    1978-01-01

    A brief discussion of the AFGL's (Air Force Geophysics Laboratory) program in absolute gravity is presented. Support of outside work and in-house studies relating to gravity instrumentation are discussed. A description of the current transportable system is included and the latest results are presented. These results show good agreement with measurements at the AFGL site by an Italian system. The accuracy obtained by the transportable apparatus is better than 0.1 microns sq sec 10 microgal and agreement with previous measurements is within the combined uncertainties of the measurements.

  2. Familial Aggregation of Absolute Pitch

    PubMed Central

    Baharloo, Siamak; Service, Susan K.; Risch, Neil; Gitschier, Jane; Freimer, Nelson B.

    2000-01-01

    Absolute pitch (AP) is a behavioral trait that is defined as the ability to identify the pitch of tones in the absence of a reference pitch. AP is an ideal phenotype for investigation of gene and environment interactions in the development of complex human behaviors. Individuals who score exceptionally well on formalized auditory tests of pitch perception are designated as “AP-1.” As described in this report, auditory testing of siblings of AP-1 probands and of a control sample indicates that AP-1 aggregates in families. The implications of this finding for the mapping of loci for AP-1 predisposition are discussed. PMID:10924408

  3. Absolute radiometric calibration of the Thematic Mapper

    NASA Technical Reports Server (NTRS)

    Slater, P. N.; Biggar, S. F.; Holm, R. G.; Jackson, R. D.; Mao, Y.

    1986-01-01

    Calibration data for the solar reflective bands of the Landsat-5 TM obtained from five in-flight absolute radiometric calibrations from July 1984-November 1985 at White Sands, New Mexico are presented and analyzed. Ground reflectance and atmospheric data were utilized to predict the spectral radiance at the entrance pupil of the TM and the average number of digital counts in each TM band. The calibration of each of the TM solar reflective bands was calculated in terms of average digital counts/unit spectral radiance for each band. It is observed that for the 12 reflectance-based measurements the rms variation from the means as a percentage of the mean is + or - 1.9 percent; for the 11 measurements in the IR bands, it is + or - 3.4 percent; and the rms variation for all 23 measurements is + or - 2.8 percent.

  4. Atmospheric Predictability: Why Butterflies Are Not Important

    NASA Astrophysics Data System (ADS)

    Durran, D. R.; Gingrich, M.

    2014-12-01

    The spectral turbulence model of Lorenz, as modified for surface quasi-geostrophic dynamics by Rotunno and Snyder, is further modified to more smoothly approach nonlinear saturation. This model is used to investigate error growth starting from different distributions of the initial error. Consistent with an often overlooked finding by Lorenz, the loss of predictability generated by initial errors of small but fixed absolute magnitude is essentially independent of their spatial scale when the background saturation kinetic energy spectrum is proportional the -5/3 power of the wavenumber. Thus, because the background kinetic energy increases with scale, very small relative errors at long wavelengths have similar impacts on perturbation error growth as large relative errors at short wavelengths. To the extent that this model applies to practical meteorological forecasts, the influence of initial perturbations generated by butterflies would be swamped by unavoidable tiny relative errors in the large scales. The rough applicability of our modified spectral turbulence model to the atmosphere over scales ranging between 10 km and 1000 km is supported by the good estimate it provides for the ensemble error growth in state-of-the-art ensemble mesoscale-model simulations of two winter storms. The initial error spectrum for the ensemble perturbations in these cases has maximum power at the longest wavelengths. The dominance of large-scale errors in the ensemble suggests that mesoscale weather forecasts may often be limited by errors arising from the large-scales instead of being produced solely through an upscale cascade from the smallest scales. These results imply the predictability of small-scale features in the vicinity of topography may be shorter than currently supposed.

  5. The Absolute Radiometric Calibration of Space - Sensors.

    NASA Astrophysics Data System (ADS)

    Holm, Ronald Gene

    1987-09-01

    The need for absolute radiometric calibration of space-based sensors will continue to increase as new generations of space sensors are developed. A reflectance -based in-flight calibration procedure is used to determine the radiance reaching the entrance pupil of the sensor. This procedure uses ground-based measurements coupled with a radiative transfer code to characterize the effects the atmosphere has on the signal reaching the sensor. The computed radiance is compared to the digital count output of the sensor associated with the image of a test site. This provides an update to the preflight calibration of the system and a check on the on-board internal calibrator. This calibration procedure was used to perform a series of five calibrations of the Landsat-5 Thematic Mapper (TM). For the 12 measurements made in TM bands 1-3, the RMS variation from the mean as a percentage of the mean is (+OR-) 1.9%, and for measurements in the IR, TM bands 4,5, and 7, the value is (+OR-) 3.4%. The RMS variation for all 23 measurements is (+OR-) 2.8%. The absolute calibration techniques were put to another test with a series of three calibration of the SPOT-1 High Resolution Visible, (HRV), sensors. The ratio, HRV-2/HRV-1, of absolute calibration coefficients compared very well with ratios of histogrammed data obtained when the cameras simultaneously imaged the same ground site. Bands PA, B1 and B3 agreed to within 3%, while band B2 showed a 7% difference. The procedure for performing a satellite calibration was then used to demonstrate how a calibrated satellite sensor can be used to quantitatively evaluate surface reflectance over a wide range of surface features. Predicted reflectance factors were compared to values obtained from aircraft -based radiometer data. This procedure was applied on four dates with two different surface conditions per date. A strong correlation, R('2) = .996, was shown between reflectance values determined from satellite imagery and low-flying aircraft

  6. Absolute intensity and polarization of rotational Raman scattering from N2, O2, and CO2

    NASA Technical Reports Server (NTRS)

    Penney, C. M.; St.peters, R. L.; Lapp, M.

    1973-01-01

    An experimental examination of the absolute intensity, polarization, and relative line intensities of rotational Raman scattering (RRS) from N2, O2, and CO2 is reported. The absolute scattering intensity for N2 is characterized by its differential cross section for backscattering of incident light at 647.1 nm, which is calculated from basic measured values. The ratio of the corresponding cross section for O2 to that for N2 is 2.50 plus or minus 5 percent. The intensity recent for N2, O2, and CO2 are shown to compare favorably to values calculated from recent measurements of the depolarization of Rayleigh scattering plus RRS. Measured depolarizations of various RRS lines agree to within a few percent with the theoretical value of 3/4. Detailed error analyses are presented for intensity and depolarization measurements. Finally, extensive RRS spectra at nominal gas temperatures of 23 C, 75 C, and 125 C are presented and shown to compare favorably to theoretical predictions.

  7. Cosmology with negative absolute temperatures

    NASA Astrophysics Data System (ADS)

    Vieira, J. P. P.; Byrnes, Christian T.; Lewis, Antony

    2016-08-01

    Negative absolute temperatures (NAT) are an exotic thermodynamical consequence of quantum physics which has been known since the 1950's (having been achieved in the lab on a number of occasions). Recently, the work of Braun et al. [1] has rekindled interest in negative temperatures and hinted at a possibility of using NAT systems in the lab as dark energy analogues. This paper goes one step further, looking into the cosmological consequences of the existence of a NAT component in the Universe. NAT-dominated expanding Universes experience a borderline phantom expansion (w < ‑1) with no Big Rip, and their contracting counterparts are forced to bounce after the energy density becomes sufficiently large. Both scenarios might be used to solve horizon and flatness problems analogously to standard inflation and bouncing cosmologies. We discuss the difficulties in obtaining and ending a NAT-dominated epoch, and possible ways of obtaining density perturbations with an acceptable spectrum.

  8. Apparatus for absolute pressure measurement

    NASA Technical Reports Server (NTRS)

    Hecht, R. (Inventor)

    1969-01-01

    An absolute pressure sensor (e.g., the diaphragm of a capacitance manometer) was subjected to a superimposed potential to effectively reduce the mechanical stiffness of the sensor. This substantially increases the sensitivity of the sensor and is particularly useful in vacuum gauges. An oscillating component of the superimposed potential induced vibrations of the sensor. The phase of these vibrations with respect to that of the oscillating component was monitored, and served to initiate an automatic adjustment of the static component of the superimposed potential, so as to bring the sensor into resonance at the frequency of the oscillating component. This establishes a selected sensitivity for the sensor, since a definite relationship exists between resonant frequency and sensitivity.

  9. Cosmology with negative absolute temperatures

    NASA Astrophysics Data System (ADS)

    Vieira, J. P. P.; Byrnes, Christian T.; Lewis, Antony

    2016-08-01

    Negative absolute temperatures (NAT) are an exotic thermodynamical consequence of quantum physics which has been known since the 1950's (having been achieved in the lab on a number of occasions). Recently, the work of Braun et al. [1] has rekindled interest in negative temperatures and hinted at a possibility of using NAT systems in the lab as dark energy analogues. This paper goes one step further, looking into the cosmological consequences of the existence of a NAT component in the Universe. NAT-dominated expanding Universes experience a borderline phantom expansion (w < -1) with no Big Rip, and their contracting counterparts are forced to bounce after the energy density becomes sufficiently large. Both scenarios might be used to solve horizon and flatness problems analogously to standard inflation and bouncing cosmologies. We discuss the difficulties in obtaining and ending a NAT-dominated epoch, and possible ways of obtaining density perturbations with an acceptable spectrum.

  10. Relative error covariance analysis techniques and application

    NASA Technical Reports Server (NTRS)

    Wolff, Peter, J.; Williams, Bobby G.

    1988-01-01

    A technique for computing the error covariance of the difference between two estimators derived from different (possibly overlapping) data arcs is presented. The relative error covariance is useful for predicting the achievable consistency between Kalman-Bucy filtered estimates generated from two (not necessarily disjoint) data sets. The relative error covariance analysis technique is then applied to a Venus Orbiter simulation.

  11. [Diagnostic Errors in Medicine].

    PubMed

    Buser, Claudia; Bankova, Andriyana

    2015-12-01

    The recognition of diagnostic errors in everyday practice can help improve patient safety. The most common diagnostic errors are the cognitive errors, followed by system-related errors and no fault errors. The cognitive errors often result from mental shortcuts, known as heuristics. The rate of cognitive errors can be reduced by a better understanding of heuristics and the use of checklists. The autopsy as a retrospective quality assessment of clinical diagnosis has a crucial role in learning from diagnostic errors. Diagnostic errors occur more often in primary care in comparison to hospital settings. On the other hand, the inpatient errors are more severe than the outpatient errors.

  12. [Diagnostic Errors in Medicine].

    PubMed

    Buser, Claudia; Bankova, Andriyana

    2015-12-01

    The recognition of diagnostic errors in everyday practice can help improve patient safety. The most common diagnostic errors are the cognitive errors, followed by system-related errors and no fault errors. The cognitive errors often result from mental shortcuts, known as heuristics. The rate of cognitive errors can be reduced by a better understanding of heuristics and the use of checklists. The autopsy as a retrospective quality assessment of clinical diagnosis has a crucial role in learning from diagnostic errors. Diagnostic errors occur more often in primary care in comparison to hospital settings. On the other hand, the inpatient errors are more severe than the outpatient errors. PMID:26649954

  13. Drifting from slow to "D'oh!": working memory capacity and mind wandering predict extreme reaction times and executive control errors.

    PubMed

    McVay, Jennifer C; Kane, Michael J

    2012-05-01

    A combined experimental, individual-differences, and thought-sampling study tested the predictions of executive attention (e.g., Engle & Kane, 2004) and coordinative binding (e.g., Oberauer, Süβ, Wilhelm, & Sander, 2007) theories of working memory capacity (WMC). We assessed 288 subjects' WMC and their performance and mind-wandering rates during a sustained-attention task; subjects completed either a go/no-go version requiring executive control over habit or a vigilance version that did not. We further combined the data with those from McVay and Kane (2009) to (1) gauge the contributions of WMC and attentional lapses to the worst performance rule and the tail, or τ parameter, of reaction time (RT) distributions; (2) assess which parameters from a quantitative evidence-accumulation RT model were predicted by WMC and mind-wandering reports; and (3) consider intrasubject RT patterns--particularly, speeding--as potential objective markers of mind wandering. We found that WMC predicted action and thought control in only some conditions, that attentional lapses (indicated by task-unrelated-thought reports and drift-rate variability in evidence accumulation) contributed to τ, performance accuracy, and WMC's association with them and that mind-wandering experiences were not predicted by trial-to-trial RT changes, and so they cannot always be inferred from objective performance measures.

  14. Drifting from Slow to "D'oh!": Working Memory Capacity and Mind Wandering Predict Extreme Reaction Times and Executive Control Errors

    ERIC Educational Resources Information Center

    McVay, Jennifer C.; Kane, Michael J.

    2012-01-01

    A combined experimental, individual-differences, and thought-sampling study tested the predictions of executive attention (e.g., Engle & Kane, 2004) and coordinative binding (e.g., Oberauer, Suss, Wilhelm, & Sander, 2007) theories of working memory capacity (WMC). We assessed 288 subjects' WMC and their performance and mind-wandering rates during…

  15. Sounding rocket measurement of the absolute solar EUV flux utilizing a silicon photodiode

    NASA Technical Reports Server (NTRS)

    Ogawa, H. S.; Mcmullin, D.; Judge, D. L.; Canfield, L. R.

    1990-01-01

    A newly developed stable and high quantum efficiency silicon photodiode was used to obtain an accurate measurement of the integrated absolute magnitude of the solar extreme UV photon flux in the spectral region between 50 and 800 A. The adjusted daily 10.7-cm solar radio flux and sunspot number were 168.4 and 121, respectively. The unattenuated absolute value of the solar EUV flux at 1 AU in the specified wavelength region was 6.81 x 10 to the 10th photons/sq cm per s. Based on a nominal probable error of 7 percent for National Institute of Standards and Technology detector efficiency measurements in the 50- to 500-A region (5 percent on longer wavelength measurements between 500 and 1216 A), and based on experimental errors associated with the present rocket instrumentation and analysis, a conservative total error estimate of about 14 percent is assigned to the absolute integral solar flux obtained.

  16. Precision goniometer equipped with a 22-bit absolute rotary encoder.

    PubMed

    Xiaowei, Z; Ando, M; Jidong, W

    1998-05-01

    The calibration of a compact precision goniometer equipped with a 22-bit absolute rotary encoder is presented. The goniometer is a modified Huber 410 goniometer: the diffraction angles can be coarsely generated by a stepping-motor-driven worm gear and precisely interpolated by a piezoactuator-driven tangent arm. The angular accuracy of the precision rotary stage was evaluated with an autocollimator. It was shown that the deviation from circularity of the rolling bearing utilized in the precision rotary stage restricts the angular positioning accuracy of the goniometer, and results in an angular accuracy ten times larger than the angular resolution of 0.01 arcsec. The 22-bit encoder was calibrated by an incremental rotary encoder. It became evident that the accuracy of the absolute encoder is approximately 18 bit due to systematic errors.

  17. Flow rate calibration for absolute cell counting rationale and design.

    PubMed

    Walker, Clare; Barnett, David

    2006-05-01

    There is a need for absolute leukocyte enumeration in the clinical setting, and accurate, reliable (and affordable) technology to determine absolute leukocyte counts has been developed. Such technology includes single platform and dual platform approaches. Derivations of these counts commonly incorporate the addition of a known number of latex microsphere beads to a blood sample, although it has been suggested that the addition of beads to a sample may only be required to act as an internal quality control procedure for assessing the pipetting error. This unit provides the technical details for undertaking flow rate calibration that obviates the need to add reference beads to each sample. It is envisaged that this report will provide the basis for subsequent clinical evaluations of this novel approach. PMID:18770842

  18. Absolute magnitude calibration using trigonometric parallax - Incomplete, spectroscopic samples

    NASA Technical Reports Server (NTRS)

    Ratnatunga, Kavan U.; Casertano, Stefano

    1991-01-01

    A new numerical algorithm is used to calibrate the absolute magnitude of spectroscopically selected stars from their observed trigonometric parallax. This procedure, based on maximum-likelihood estimation, can retrieve unbiased estimates of the intrinsic absolute magnitude and its dispersion even from incomplete samples suffering from selection biases in apparent magnitude and color. It can also make full use of low accuracy and negative parallaxes and incorporate censorship on reported parallax values. Accurate error estimates are derived for each of the fitted parameters. The algorithm allows an a posteriori check of whether the fitted model gives a good representation of the observations. The procedure is described in general and applied to both real and simulated data.

  19. Digital spatial data for observed, predicted, and misclassification errors for observations in the training dataset for nitrate and arsenic concentrations in basin-fill aquifers in the Southwest Principal Aquifers study area

    USGS Publications Warehouse

    McKinney, Tim S.; Anning, David W.

    2012-01-01

    This product "Digital spatial data for observed, predicted, and misclassification errors for observations in the training dataset for nitrate and arsenic concentrations in basin-fill aquifers in the Southwest Principal Aquifers study area" is a 1:250,000-scale point spatial dataset developed as part of a regional Southwest Principal Aquifers (SWPA) study (Anning and others, 2012). The study examined the vulnerability of basin-fill aquifers in the southwestern United States to nitrate contamination and arsenic enrichment. Statistical models were developed by using the random forest classifier algorithm to predict concentrations of nitrate and arsenic across a model grid that represents local- and basin-scale measures of source, aquifer susceptibility, and geochemical conditions.

  20. Ability of the planar spring-mass model to predict mechanical parameters in running humans.

    PubMed

    Bullimore, Sharon R; Burn, Jeremy F

    2007-10-21

    The planar spring-mass model is a simple mathematical model of bouncing gaits, such as running, trotting and hopping. Although this model has been widely used in the study of locomotion, its accuracy in predicting locomotor mechanics has not been systematically quantified. We determined the percent error of the model in predicting 10 locomotor parameters in running humans by comparing the model predictions to experimental data from humans running in normal gravity and simulated reduced gravity. We tested the hypotheses that the model would overestimate horizontal impulse and the change in mechanical energy of the centre of mass (COM) during stance. The model provided good predictions of stance time, vertical impulse, contact length, duty factor, relative stride length and relative peak force. All predictions of these parameters were within 20% of measured values and at least 90% of predictions of each parameter were within 10% of measured values (median absolute errors: <7%). This suggests that the model incorporates all features of running humans that have a significant influence upon these six parameters. As simulated gravity level decreased, the magnitude of the errors in predicting each of these parameters either decreased or stayed constant, indicating that this is a good model of running in simulated reduced gravity. As hypothesised, horizontal impulse and change in mechanical energy of the COM during stance were overestimated (median absolute errors: 43.6% and 26.2%, respectively). Aerial time and peak vertical COM displacement during stance were also systematically overestimated (median absolute errors: 17.7% and 22.9%, respectively). Care should be taken to ensure that the model is used only to investigate parameters which it can predict accurately. It would be useful to extend this analysis to other species and gaits.

  1. Absolute configuration of isovouacapenol C

    PubMed Central

    Fun, Hoong-Kun; Yodsaoue, Orapun; Karalai, Chatchanok; Chantrapromma, Suchada

    2010-01-01

    The title compound, C27H34O5 {systematic name: (4aR,5R,6R,6aS,7R,11aS,11bR)-4a,6-dihy­droxy-4,4,7,11b-tetra­methyl-1,2,3,4,4a,5,6,6a,7,11,11a,11b-dodeca­hydro­phenanthro[3,2-b]furan-5-yl benzoate}, is a cassane furan­oditerpene, which was isolated from the roots of Caesalpinia pulcherrima. The three cyclo­hexane rings are trans fused: two of these are in chair conformations with the third in a twisted half-chair conformation, whereas the furan ring is almost planar (r.m.s. deviation = 0.003 Å). An intra­molecular C—H⋯O inter­action generates an S(6) ring. The absolute configurations of the stereogenic centres at positions 4a, 5, 6, 6a, 7, 11a and 11b are R, R, R, S, R, S and R, respectively. In the crystal, mol­ecules are linked into infinite chains along [010] by O—H⋯O hydrogen bonds. C⋯O [3.306 (2)–3.347 (2) Å] short contacts and C—H⋯π inter­actions also occur. PMID:21588364

  2. Error growth in operational ECMWF forecasts

    NASA Technical Reports Server (NTRS)

    Kalnay, E.; Dalcher, A.

    1985-01-01

    A parameterization scheme used at the European Centre for Medium Range Forecasting to model the average growth of the difference between forecasts on consecutive days was extended by including the effect of error growth on forecast model deficiencies. Error was defined as the difference between the forecast and analysis fields during the verification time. Systematic and random errors were considered separately in calculating the error variance for a 10 day operational forecast. A good fit was obtained with measured forecast errors and a satisfactory trend was achieved in the difference between forecasts. Fitting six parameters to forecast errors and differences that were performed separately for each wavenumber revealed that the error growth rate grew with wavenumber. The saturation error decreased with the total wavenumber and the limit of predictability, i.e., when error variance reaches 95 percent of saturation, decreased monotonically with the total wavenumber.

  3. Measuring Postglacial Rebound with GPS and Absolute Gravity

    NASA Technical Reports Server (NTRS)

    Larson, Kristine M.; vanDam, Tonie

    2000-01-01

    We compare vertical rates of deformation derived from continuous Global Positioning System (GPS) observations and episodic measurements of absolute gravity. We concentrate on four sites in a region of North America experiencing postglacial rebound. The rates of uplift from gravity and GPS agree within one standard deviation for all sites. The GPS vertical deformation rates are significantly more precise than the gravity rates, primarily because of the denser temporal spacing provided by continuous GPS tracking. We conclude that continuous GPS observations are more cost efficient and provide more precise estimates of vertical deformation rates than campaign style gravity observations where systematic errors are difficult to quantify.

  4. Sun compass error model

    NASA Technical Reports Server (NTRS)

    Blucker, T. J.; Ferry, W. W.

    1971-01-01

    An error model is described for the Apollo 15 sun compass, a contingency navigational device. Field test data are presented along with significant results of the test. The errors reported include a random error resulting from tilt in leveling the sun compass, a random error because of observer sighting inaccuracies, a bias error because of mean tilt in compass leveling, a bias error in the sun compass itself, and a bias error because the device is leveled to the local terrain slope.

  5. On the Absolute Age of the Metal-rich Globular M71 (NGC 6838). I. Optical Photometry

    NASA Astrophysics Data System (ADS)

    Di Cecco, A.; Bono, G.; Prada Moroni, P. G.; Tognelli, E.; Allard, F.; Stetson, P. B.; Buonanno, R.; Ferraro, I.; Iannicola, G.; Monelli, M.; Nonino, M.; Pulone, L.

    2015-08-01

    We investigated the absolute age of the Galactic globular cluster M71 (NGC 6838) using optical ground-based images (u\\prime ,g\\prime ,r\\prime ,i\\prime ,z\\prime ) collected with the MegaCam camera at the Canada-France-Hawaii Telescope (CFHT). We performed a robust selection of field and cluster stars by applying a new method based on the 3D (r\\prime ,u\\prime -g\\prime ,g\\prime -r\\prime ) color-color-magnitude diagram. A comparison between the color-magnitude diagram (CMD) of the candidate cluster stars and a new set of isochrones at the locus of the main sequence turn-off (MSTO) suggests an absolute age of 12 ± 2 Gyr. The absolute age was also estimated using the difference in magnitude between the MSTO and the so-called main sequence knee, a well-defined bending occurring in the lower main sequence. This feature was originally detected in the near-infrared bands and explained as a consequence of an opacity mechanism (collisionally induced absorption of molecular hydrogen) in the atmosphere of cool low-mass stars. The same feature was also detected in the r‧, u\\prime -g\\prime , and in the r\\prime ,g\\prime -r\\prime CMD, thus supporting previous theoretical predictions by Borysow et al. The key advantage in using the {{{Δ }}}{TO}{Knee} as an age diagnostic is that it is independent of uncertainties affecting the distance, the reddening, and the photometric zero point. We found an absolute age of 12 ± 1 Gyr that agrees, within the errors, with similar age estimates, but the uncertainty is on average a factor of two smaller. We also found that the {{{Δ }}}{TO}{Knee} is more sensitive to the metallicity than the MSTO, but the dependence vanishes when using the difference in color between the MSK and the MSTO.

  6. Tropical errors and convection

    NASA Astrophysics Data System (ADS)

    Bechtold, P.; Bauer, P.; Engelen, R. J.

    2012-12-01

    Tropical convection is analysed in the ECMWF Integrated Forecast System (IFS) through tropical errors and their evolution during the last decade as a function of model resolution and model changes. As the characterization of these errors is particularly difficult over tropical oceans due to sparse in situ upper-air data, more weight compared to the middle latitudes is given in the analysis to the underlying forecast model. Therefore, special attention is paid to available near-surface observations and to comparison with analysis from other Centers. There is a systematic lack of low-level wind convergence in the Inner Tropical Convergence Zone (ITCZ) in the IFS, leading to a spindown of the Hadley cell. Critical areas with strong cross-equatorial flow and large wind errors are the Indian Ocean with large interannual variations in forecast errors, and the East Pacific with persistent systematic errors that have evolved little during the last decade. The analysis quality in the East Pacific is affected by observation errors inherent to the atmospheric motion vector wind product. The model's tropical climate and its variability and teleconnections are also evaluated, with a particular focus on the Madden-Julian Oscillation (MJO) during the Year of Tropical Convection (YOTC). The model is shown to reproduce the observed tropical large-scale wave spectra and teleconnections, but overestimates the precipitation during the South-East Asian summer monsoon. The recent improvements in tropical precipitation, convectively coupled wave and MJO predictability are shown to be strongly related to improvements in the convection parameterization that realistically represents the convection sensitivity to environmental moisture, and the large-scale forcing due to the use of strong entrainment and a variable adjustment time-scale. There is however a remaining slight moistening tendency and low-level wind imbalance in the model that is responsible for the Asian Monsoon bias and for too

  7. Unforced errors and error reduction in tennis

    PubMed Central

    Brody, H

    2006-01-01

    Only at the highest level of tennis is the number of winners comparable to the number of unforced errors. As the average player loses many more points due to unforced errors than due to winners by an opponent, if the rate of unforced errors can be reduced, it should lead to an increase in points won. This article shows how players can improve their game by understanding and applying the laws of physics to reduce the number of unforced errors. PMID:16632568

  8. Accurate calculation of the absolute free energy of binding for drug molecules† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c5sc02678d Click here for additional data file.

    PubMed Central

    Aldeghi, Matteo; Heifetz, Alexander; Bodkin, Michael J.; Knapp, Stefan

    2016-01-01

    Accurate prediction of binding affinities has been a central goal of computational chemistry for decades, yet remains elusive. Despite good progress, the required accuracy for use in a drug-discovery context has not been consistently achieved for drug-like molecules. Here, we perform absolute free energy calculations based on a thermodynamic cycle for a set of diverse inhibitors binding to bromodomain-containing protein 4 (BRD4) and demonstrate that a mean absolute error of 0.6 kcal mol–1 can be achieved. We also show a similar level of accuracy (1.0 kcal mol–1) can be achieved in pseudo prospective approach. Bromodomains are epigenetic mark readers that recognize acetylation motifs and regulate gene transcription, and are currently being investigated as therapeutic targets for cancer and inflammation. The unprecedented accuracy offers the exciting prospect that the binding free energy of drug-like compounds can be predicted for pharmacologically relevant targets. PMID:26798447

  9. Error in radiology.

    PubMed

    Goddard, P; Leslie, A; Jones, A; Wakeley, C; Kabala, J

    2001-10-01

    The level of error in radiology has been tabulated from articles on error and on "double reporting" or "double reading". The level of error varies depending on the radiological investigation, but the range is 2-20% for clinically significant or major error. The greatest reduction in error rates will come from changes in systems.

  10. Updated Absolute Flux Calibration of the COS FUV Modes

    NASA Astrophysics Data System (ADS)

    Massa, D.; Ely, J.; Osten, R.; Penton, S.; Aloisi, A.; Bostroem, A.; Roman-Duval, J.; Proffitt, C.

    2014-03-01

    We present newly derived point source absolute flux calibrations for the COS FUV modes at both the original and second lifetime positions. The analysis includes observa- tions through the Primary Science Aperture (PSA) of the standard stars WD0308-565, GD71, WD1057+729 and WD0947+857 obtained as part of two calibration programs. Data were were obtained for all of the gratings at all of the original CENWAVE settings at both the original and second lifetime positions and for the G130M CENWAVE = 1222 at the second lifetime position. Data were also obtained with the FUVB segment for the G130M CENWAVE = 1055 and 1096 setting at the second lifetime position. We also present the derivation of L-flats that were used in processing the data and show that the internal consistency of the primary standards is 1%. The accuracy of the absolute flux calibrations over the UV are estimated to be 1-2% for the medium resolution gratings, and 2-3% over most of the wavelength range of the G140L grating, although the uncertainty can be as large as 5% or more at some G140L wavelengths. We note that these errors are all relative to the optical flux near the V band and small additional errors may be present due to inaccuracies in the V band calibration. In addition, these error estimates are for the time at which the flux calibration data were obtained; the accuracy of the flux calibration at other times can be affected by errors in the time dependent sensitivity (TDS) correction.

  11. Absolute Income, Relative Income, and Happiness

    ERIC Educational Resources Information Center

    Ball, Richard; Chernova, Kateryna

    2008-01-01

    This paper uses data from the World Values Survey to investigate how an individual's self-reported happiness is related to (i) the level of her income in absolute terms, and (ii) the level of her income relative to other people in her country. The main findings are that (i) both absolute and relative income are positively and significantly…

  12. Investigating Absolute Value: A Real World Application

    ERIC Educational Resources Information Center

    Kidd, Margaret; Pagni, David

    2009-01-01

    Making connections between various representations is important in mathematics. In this article, the authors discuss the numeric, algebraic, and graphical representations of sums of absolute values of linear functions. The initial explanations are accessible to all students who have experience graphing and who understand that absolute value simply…

  13. Preschoolers' Success at Coding Absolute Size Values.

    ERIC Educational Resources Information Center

    Russell, James

    1980-01-01

    Forty-five 2-year-old and forty-five 3-year-old children coded relative and absolute sizes using 1.5-inch, 6-inch, and 18-inch cardboard squares. Results indicate that absolute coding is possible for children of this age. (Author/RH)

  14. Introducing the Mean Absolute Deviation "Effect" Size

    ERIC Educational Resources Information Center

    Gorard, Stephen

    2015-01-01

    This paper revisits the use of effect sizes in the analysis of experimental and similar results, and reminds readers of the relative advantages of the mean absolute deviation as a measure of variation, as opposed to the more complex standard deviation. The mean absolute deviation is easier to use and understand, and more tolerant of extreme…

  15. Monolithically integrated absolute frequency comb laser system

    DOEpatents

    Wanke, Michael C.

    2016-07-12

    Rather than down-convert optical frequencies, a QCL laser system directly generates a THz frequency comb in a compact monolithically integrated chip that can be locked to an absolute frequency without the need of a frequency-comb synthesizer. The monolithic, absolute frequency comb can provide a THz frequency reference and tool for high-resolution broad band spectroscopy.

  16. Absolute optical metrology : nanometers to kilometers

    NASA Technical Reports Server (NTRS)

    Dubovitsky, Serge; Lay, O. P.; Peters, R. D.; Liebe, C. C.

    2005-01-01

    We provide and overview of the developments in the field of high-accuracy absolute optical metrology with emphasis on space-based applications. Specific work on the Modulation Sideband Technology for Absolute Ranging (MSTAR) sensor is described along with novel applications of the sensor.

  17. Absolute instability of the Gaussian wake profile

    NASA Technical Reports Server (NTRS)

    Hultgren, Lennart S.; Aggarwal, Arun K.

    1987-01-01

    Linear parallel-flow stability theory has been used to investigate the effect of viscosity on the local absolute instability of a family of wake profiles with a Gaussian velocity distribution. The type of local instability, i.e., convective or absolute, is determined by the location of a branch-point singularity with zero group velocity of the complex dispersion relation for the instability waves. The effects of viscosity were found to be weak for values of the wake Reynolds number, based on the center-line velocity defect and the wake half-width, larger than about 400. Absolute instability occurs only for sufficiently large values of the center-line wake defect. The critical value of this parameter increases with decreasing wake Reynolds number, thereby indicating a shrinking region of absolute instability with decreasing wake Reynolds number. If backflow is not allowed, absolute instability does not occur for wake Reynolds numbers smaller than about 38.

  18. Explaining errors in children's questions.

    PubMed

    Rowland, Caroline F

    2007-07-01

    The ability to explain the occurrence of errors in children's speech is an essential component of successful theories of language acquisition. The present study tested some generativist and constructivist predictions about error on the questions produced by ten English-learning children between 2 and 5 years of age. The analyses demonstrated that, as predicted by some generativist theories [e.g. Santelmann, L., Berk, S., Austin, J., Somashekar, S. & Lust. B. (2002). Continuity and development in the acquisition of inversion in yes/no questions: dissociating movement and inflection, Journal of Child Language, 29, 813-842], questions with auxiliary DO attracted higher error rates than those with modal auxiliaries. However, in wh-questions, questions with modals and DO attracted equally high error rates, and these findings could not be explained in terms of problems forming questions with why or negated auxiliaries. It was concluded that the data might be better explained in terms of a constructivist account that suggests that entrenched item-based constructions may be protected from error in children's speech, and that errors occur when children resort to other operations to produce questions [e.g. Dabrowska, E. (2000). From formula to schema: the acquisition of English questions. Cognitive Liguistics, 11, 83-102; Rowland, C. F. & Pine, J. M. (2000). Subject-auxiliary inversion errors and wh-question acquisition: What children do know? Journal of Child Language, 27, 157-181; Tomasello, M. (2003). Constructing a language: A usage-based theory of language acquisition. Cambridge, MA: Harvard University Press]. However, further work on constructivist theory development is required to allow researchers to make predictions about the nature of these operations.

  19. Errors, error detection, error correction and hippocampal-region damage: data and theories.

    PubMed

    MacKay, Donald G; Johnson, Laura W

    2013-11-01

    This review and perspective article outlines 15 observational constraints on theories of errors, error detection, and error correction, and their relation to hippocampal-region (HR) damage. The core observations come from 10 studies with H.M., an amnesic with cerebellar and HR damage but virtually no neocortical damage. Three studies examined the detection of errors planted in visual scenes (e.g., a bird flying in a fish bowl in a school classroom) and sentences (e.g., I helped themselves to the birthday cake). In all three experiments, H.M. detected reliably fewer errors than carefully matched memory-normal controls. Other studies examined the detection and correction of self-produced errors, with controls for comprehension of the instructions, impaired visual acuity, temporal factors, motoric slowing, forgetting, excessive memory load, lack of motivation, and deficits in visual scanning or attention. In these studies, H.M. corrected reliably fewer errors than memory-normal and cerebellar controls, and his uncorrected errors in speech, object naming, and reading aloud exhibited two consistent features: omission and anomaly. For example, in sentence production tasks, H.M. omitted one or more words in uncorrected encoding errors that rendered his sentences anomalous (incoherent, incomplete, or ungrammatical) reliably more often than controls. Besides explaining these core findings, the theoretical principles discussed here explain H.M.'s retrograde amnesia for once familiar episodic and semantic information; his anterograde amnesia for novel information; his deficits in visual cognition, sentence comprehension, sentence production, sentence reading, and object naming; and effects of aging on his ability to read isolated low frequency words aloud. These theoretical principles also explain a wide range of other data on error detection and correction and generate new predictions for future test.

  20. Errors, error detection, error correction and hippocampal-region damage: data and theories.

    PubMed

    MacKay, Donald G; Johnson, Laura W

    2013-11-01

    This review and perspective article outlines 15 observational constraints on theories of errors, error detection, and error correction, and their relation to hippocampal-region (HR) damage. The core observations come from 10 studies with H.M., an amnesic with cerebellar and HR damage but virtually no neocortical damage. Three studies examined the detection of errors planted in visual scenes (e.g., a bird flying in a fish bowl in a school classroom) and sentences (e.g., I helped themselves to the birthday cake). In all three experiments, H.M. detected reliably fewer errors than carefully matched memory-normal controls. Other studies examined the detection and correction of self-produced errors, with controls for comprehension of the instructions, impaired visual acuity, temporal factors, motoric slowing, forgetting, excessive memory load, lack of motivation, and deficits in visual scanning or attention. In these studies, H.M. corrected reliably fewer errors than memory-normal and cerebellar controls, and his uncorrected errors in speech, object naming, and reading aloud exhibited two consistent features: omission and anomaly. For example, in sentence production tasks, H.M. omitted one or more words in uncorrected encoding errors that rendered his sentences anomalous (incoherent, incomplete, or ungrammatical) reliably more often than controls. Besides explaining these core findings, the theoretical principles discussed here explain H.M.'s retrograde amnesia for once familiar episodic and semantic information; his anterograde amnesia for novel information; his deficits in visual cognition, sentence comprehension, sentence production, sentence reading, and object naming; and effects of aging on his ability to read isolated low frequency words aloud. These theoretical principles also explain a wide range of other data on error detection and correction and generate new predictions for future test. PMID:23999403

  1. CONTROLLING ABSOLUTE FREQUENCY OF FEEDBACK IN A SELF-CONTROLLED SITUATION ENHANCES MOTOR LEARNING.

    PubMed

    Tsai, Min-Jen; Jwo, Hank

    2015-12-01

    The guidance hypothesis suggested that excessive extrinsic feedback facilitates motor performance but blocks the processing of intrinsic information. The present study tested the tenet of guidance hypothesis in self-controlled feedback by controlling the feedback frequency. The motor learning effect of limiting absolute feedback frequency was examined. Thirty-six participants (25 men, 11 women; M age=25.1 yr., SD=2.2) practiced a hand-grip force control task on a dynamometer by the non-dominant hand with varying amounts of feedback. They were randomly assigned to: (a) Self-controlled, (b) Yoked with self-controlled, and (c) Limited self-controlled conditions. In acquisition, two-way analysis of variance indicated significantly lower absolute error in both the yoked and limited self-controlled groups than the self-controlled group. The effect size of absolute error between trials with feedback and without feedback in the limited self-controlled condition was larger than that of the self-controlled condition. In the retention and transfer tests, the Limited self-controlled feedback group had significantly lower absolute error than the other two groups. The results indicated an increased motor learning effect of limiting absolute frequency of feedback in the self-controlled condition.

  2. The Errors of Our Ways: Understanding Error Representations in Cerebellar-Dependent Motor Learning

    PubMed Central

    Popa, Laurentiu S.; Streng, Martha L.; Hewitt, Angela L.; Ebner, Timothy J.

    2015-01-01

    The cerebellum is essential for error-driven motor learning and is strongly implicated in detecting and correcting for motor errors. Therefore, elucidating how motor errors are represented in the cerebellum is essential in understanding cerebellar function, in general, and its role in motor learning, in particular. This review examines how motor errors are encoded in the cerebellar cortex in the context of a forward internal model that generates predictions about the upcoming movement and drives learning and adaptation. In this framework, sensory prediction errors, defined as the discrepancy between the predicted consequences of motor commands and the sensory feedback, are crucial for both on-line movement control and motor learning. While many studies support the dominant view that motor errors are encoded in the complex spike discharge of Purkinje cells, others have failed to relate complex spike activity with errors. Given these limitations, we review recent findings in the monkey showing that complex spike modulation is not necessarily required for motor learning or for simple spike adaptation. Also, new results demonstrate that the simple spike discharge provides continuous error signals that both lead and lag the actual movements in time, suggesting errors are encoded as both an internal prediction of motor commands and the actual sensory feedback. These dual error representations have opposing effects on simple spike discharge, consistent with the signals needed to generate sensory prediction errors used to update a forward internal model. PMID:26112422

  3. Conditional Density Estimation in Measurement Error Problems.

    PubMed

    Wang, Xiao-Feng; Ye, Deping

    2015-01-01

    This paper is motivated by a wide range of background correction problems in gene array data analysis, where the raw gene expression intensities are measured with error. Estimating a conditional density function from the contaminated expression data is a key aspect of statistical inference and visualization in these studies. We propose re-weighted deconvolution kernel methods to estimate the conditional density function in an additive error model, when the error distribution is known as well as when it is unknown. Theoretical properties of the proposed estimators are investigated with respect to the mean absolute error from a "double asymptotic" view. Practical rules are developed for the selection of smoothing-parameters. Simulated examples and an application to an Illumina bead microarray study are presented to illustrate the viability of the methods. PMID:25284902

  4. Moments of inclination error distribution computer program

    NASA Technical Reports Server (NTRS)

    Myler, T. R.

    1981-01-01

    A FORTRAN coded computer program is described which calculates orbital inclination error statistics using a closed-form solution. This solution uses a data base of trajectory errors from actual flights to predict the orbital inclination error statistics. The Scott flight history data base consists of orbit insertion errors in the trajectory parameters - altitude, velocity, flight path angle, flight azimuth, latitude and longitude. The methods used to generate the error statistics are of general interest since they have other applications. Program theory, user instructions, output definitions, subroutine descriptions and detailed FORTRAN coding information are included.

  5. Measurement error analysis of taxi meter

    NASA Astrophysics Data System (ADS)

    He, Hong; Li, Dan; Li, Hang; Zhang, Da-Jian; Hou, Ming-Feng; Zhang, Shi-pu

    2011-12-01

    The error test of the taximeter is divided into two aspects: (1) the test about time error of the taximeter (2) distance test about the usage error of the machine. The paper first gives the working principle of the meter and the principle of error verification device. Based on JJG517 - 2009 "Taximeter Verification Regulation ", the paper focuses on analyzing the machine error and test error of taxi meter. And the detect methods of time error and distance error are discussed as well. In the same conditions, standard uncertainty components (Class A) are evaluated, while in different conditions, standard uncertainty components (Class B) are also evaluated and measured repeatedly. By the comparison and analysis of the results, the meter accords with JJG517-2009, "Taximeter Verification Regulation ", thereby it improves the accuracy and efficiency largely. In actual situation, the meter not only makes up the lack of accuracy, but also makes sure the deal between drivers and passengers fair. Absolutely it enriches the value of the taxi as a way of transportation.

  6. Measurement of absolute optical thickness of mask glass by wavelength-tuning Fourier analysis.

    PubMed

    Kim, Yangjin; Hbino, Kenichi; Sugita, Naohiko; Mitsuishi, Mamoru

    2015-07-01

    Optical thickness is a fundamental characteristic of an optical component. A measurement method combining discrete Fourier-transform (DFT) analysis and a phase-shifting technique gives an appropriate value for the absolute optical thickness of a transparent plate. However, there is a systematic error caused by the nonlinearity of the phase-shifting technique. In this research the absolute optical-thickness distribution of mask blank glass was measured using DFT and wavelength-tuning Fizeau interferometry without using sensitive phase-shifting techniques. The error occurring during the DFT analysis was compensated for by using the unwrapping correlation. The experimental results indicated that the absolute optical thickness of mask glass was measured with an accuracy of 5 nm.

  7. Absolute flatness testing of skip-flat interferometry by matrix analysis in polar coordinates.

    PubMed

    Han, Zhi-Gang; Yin, Lu; Chen, Lei; Zhu, Ri-Hong

    2016-03-20

    A new method utilizing matrix analysis in polar coordinates has been presented for absolute testing of skip-flat interferometry. The retrieval of the absolute profile mainly includes three steps: (1) transform the wavefront maps of the two cavity measurements into data in polar coordinates; (2) retrieve the profile of the reflective flat in polar coordinates by matrix analysis; and (3) transform the profile of the reflective flat back into data in Cartesian coordinates and retrieve the profile of the sample. Simulation of synthetic surface data has been provided, showing the capability of the approach to achieve an accuracy of the order of 0.01 nm RMS. The absolute profile can be retrieved by a set of closed mathematical formulas without polynomial fitting of wavefront maps or the iterative evaluation of an error function, making the new method more efficient for absolute testing.

  8. Pantomime-Grasping: Advance Knowledge of Haptic Feedback Availability Supports an Absolute Visuo-Haptic Calibration.

    PubMed

    Davarpanah Jazi, Shirin; Heath, Matthew

    2016-01-01

    An emerging issue in movement neurosciences is whether haptic feedback influences the nature of the information supporting a simulated grasping response (i.e., pantomime-grasping). In particular, recent work by our group contrasted pantomime-grasping responses performed with (i.e., PH+ trials) and without (i.e., PH- trials) terminal haptic feedback in separate blocks of trials. Results showed that PH- trials were mediated via relative visual information. In contrast, PH+ trials showed evidence of an absolute visuo-haptic calibration-a finding attributed to an error signal derived from a comparison between expected and actual haptic feedback (i.e., an internal forward model). The present study examined whether advanced knowledge of haptic feedback availability influences the aforementioned calibration process. To that end, PH- and PH+ trials were completed in separate blocks (i.e., the feedback schedule used in our group's previous study) and a block wherein PH- and PH+ trials were randomly interleaved on a trial-by-trial basis (i.e., random feedback schedule). In other words, the random feedback schedule precluded participants from predicting whether haptic feedback would be available at the movement goal location. We computed just-noticeable-difference (JND) values to determine whether responses adhered to, or violated, the relative psychophysical principles of Weber's law. Results for the blocked feedback schedule replicated our group's previous work, whereas in the random feedback schedule PH- and PH+ trials were supported via relative visual information. Accordingly, we propose that a priori knowledge of haptic feedback is necessary to support an absolute visuo-haptic calibration. Moreover, our results demonstrate that the presence and expectancy of haptic feedback is an important consideration in contrasting the behavioral and neural properties of natural and simulated grasping. PMID:27199718

  9. Pantomime-Grasping: Advance Knowledge of Haptic Feedback Availability Supports an Absolute Visuo-Haptic Calibration

    PubMed Central

    Davarpanah Jazi, Shirin; Heath, Matthew

    2016-01-01

    An emerging issue in movement neurosciences is whether haptic feedback influences the nature of the information supporting a simulated grasping response (i.e., pantomime-grasping). In particular, recent work by our group contrasted pantomime-grasping responses performed with (i.e., PH+ trials) and without (i.e., PH− trials) terminal haptic feedback in separate blocks of trials. Results showed that PH− trials were mediated via relative visual information. In contrast, PH+ trials showed evidence of an absolute visuo-haptic calibration—a finding attributed to an error signal derived from a comparison between expected and actual haptic feedback (i.e., an internal forward model). The present study examined whether advanced knowledge of haptic feedback availability influences the aforementioned calibration process. To that end, PH− and PH+ trials were completed in separate blocks (i.e., the feedback schedule used in our group’s previous study) and a block wherein PH− and PH+ trials were randomly interleaved on a trial-by-trial basis (i.e., random feedback schedule). In other words, the random feedback schedule precluded participants from predicting whether haptic feedback would be available at the movement goal location. We computed just-noticeable-difference (JND) values to determine whether responses adhered to, or violated, the relative psychophysical principles of Weber’s law. Results for the blocked feedback schedule replicated our group’s previous work, whereas in the random feedback schedule PH− and PH+ trials were supported via relative visual information. Accordingly, we propose that a priori knowledge of haptic feedback is necessary to support an absolute visuo-haptic calibration. Moreover, our results demonstrate that the presence and expectancy of haptic feedback is an important consideration in contrasting the behavioral and neural properties of natural and simulated grasping. PMID:27199718

  10. Neurophysiological responses to gun-shooting errors.

    PubMed

    Xu, Xiaowen; Inzlicht, Michael

    2015-03-01

    The present study investigated the neural responses to errors in a shooting game - and how these neural responses may relate to behavioral performance - by examining the ERP components related to error detection (error-related negativity; ERN) and error awareness (error-related positivity; Pe). The participants completed a Shooter go/no-go task, which required them to shoot at armed targets using a gaming gun, and avoid shooting innocent non-targets. The amplitude of the ERN and Pe was greater for shooting errors than correct shooting responses. The ERN and Pe amplitudes elicited by incorrect shooting appeared to have good internal reliability. The ERN and Pe amplitudes elicited by shooting behaviors also predicted better behavioral sensitivity towards shoot/don't-shoot stimuli. These results suggest that it is possible to obtain online brain response measures to shooting responses and that neural responses to shooting are predictive of behavioral responses. PMID:25448268

  11. Neurophysiological responses to gun-shooting errors.

    PubMed

    Xu, Xiaowen; Inzlicht, Michael

    2015-03-01

    The present study investigated the neural responses to errors in a shooting game - and how these neural responses may relate to behavioral performance - by examining the ERP components related to error detection (error-related negativity; ERN) and error awareness (error-related positivity; Pe). The participants completed a Shooter go/no-go task, which required them to shoot at armed targets using a gaming gun, and avoid shooting innocent non-targets. The amplitude of the ERN and Pe was greater for shooting errors than correct shooting responses. The ERN and Pe amplitudes elicited by incorrect shooting appeared to have good internal reliability. The ERN and Pe amplitudes elicited by shooting behaviors also predicted better behavioral sensitivity towards shoot/don't-shoot stimuli. These results suggest that it is possible to obtain online brain response measures to shooting responses and that neural responses to shooting are predictive of behavioral responses.

  12. Absolute magnitudes of trans-neptunian objects

    NASA Astrophysics Data System (ADS)

    Duffard, R.; Alvarez-candal, A.; Pinilla-Alonso, N.; Ortiz, J. L.; Morales, N.; Santos-Sanz, P.; Thirouin, A.

    2015-10-01

    Accurate measurements of diameters of trans- Neptunian objects are extremely complicated to obtain. Radiomatric techniques applied to thermal measurements can provide good re