Relational memory generalization and integration in a transitive inference task with and without instructed awareness.

PubMed

Munnelly, Anita; Dymond, Simon

2014-03-01

Two experiments investigated the potential facilitative effects of prior instructed awareness and predetermined learning criteria on humans' ability to make transitive inference (TI) judgments. Participants were first exposed to a learning phase and required to learn five premise pairs (A+B-, B+C-, C+D-, D+E-, E+F-). Testing followed, where participants made judgments on novel non-endpoint (BD, BE and CE) and endpoint inferential pairs (AC, AD, AE, AF, BF, CF and DF), as well as learned premise pairs. Across both experiments, one group were made aware that the stimuli could be arranged in a hierarchy, while another group were not given this instruction. Results demonstrated that prior instructional task awareness led to a minor performance advantage, but that this difference was not significant. Furthermore, in Experiment 2, inferential test trial accuracy was not correlated with a post-experimental measure of awareness. Thus, the current findings suggest that successful TI task performance may occur in the absence of awareness, and that repeated exposure to learning and test phases may allow weak inferential performances to emerge gradually. Further research and alternative methods of measuring awareness and its role in TI are needed. Copyright © 2014 Elsevier Inc. All rights reserved.

Impaired inference in a case of developmental amnesia.

PubMed

D'Angelo, Maria C; Rosenbaum, R Shayna; Ryan, Jennifer D

2016-10-01

Amnesia is associated with impairments in relational memory, which is critically supported by the hippocampus. By adapting the transitivity paradigm, we previously showed that age-related impairments in inference were mitigated when judgments could be predicated on known pairwise relations, however, such advantages were not observed in the adult-onset amnesic case D.A. Here, we replicate and extend this finding in a developmental amnesic case (N.C.), who also shows impaired relational learning and transitive expression. Unlike D.A., N.C.'s damage affected the extended hippocampal system and diencephalic structures, and does not extend to neocortical areas that are affected in D.A. Critically, despite their differences in etiology and affected structures, N.C. and D.A. perform similarly on the task. N.C. showed intact pairwise knowledge, suggesting that he is able to use existing semantic information, but this semantic knowledge was insufficient to support transitive expression. The present results suggest a critical role for regions connected to the hippocampus and/or medial prefrontal cortex in inference beyond learning of pairwise relations. © 2016 The Authors Hippocampus Published by Wiley Periodicals, Inc. © 2016 The Authors. Wiley Periodicals, Inc.

Transitivity performance, relational hierarchy knowledge and awareness: Results of an instructional framing manipulation

PubMed Central

Kumaran, Dharshan; Ludwig, Hans

2013-01-01

The transitive inference (TI) paradigm has been widely used to examine the role of the hippocampus in generalization. Here we consider a surprising feature of experimental findings in this task: the relatively poor transitivity performance and levels of hierarchy knowledge achieved by adult human subjects. We focused on the influence of the task instructions on participants’ subsequent performance—a single-word framing manipulation which either specified the relation between items as transitive (i.e., OLD-FRAME: choose which item is “older”) or left it ambiguous (i.e., NO-FRAME: choose which item is “correct”). We show a marked but highly specific effect of manipulating prior knowledge through instruction: transitivity performance and levels of relational hierarchy knowledge were enhanced, but premise performance unchanged. Further, we show that hierarchy recall accuracy, but not conventional awareness scores, was a significant predictor of inferential performance across the entire group of participants. The current study has four main implications: first, our findings establish the importance of the task instructions, and prior knowledge, in the TI paradigm—suggesting that they influence the size of the overall hypothesis space (e.g., to favor a linear hierarchical structure over other possibilities in the OLD-FRAME). Second, the dissociable effects of the instructional frame on premise and inference performance provide evidence for the operation of distinct underlying mechanisms (i.e., an associative mechanism vs. relational hierarchy knowledge). Third, our findings suggest that a detailed measurement of hierarchy recall accuracy may be a more sensitive index of relational hierarchy knowledge, than conventional awareness score—and should be used in future studies investigating links between awareness and inferential performance. Finally, our study motivates an experimental setting that ensures robust hierarchy learning across participants—therefore facilitating study of the neural mechanisms underlying the learning and representation of linear hierarchies. PMID:23804544

Transitivity performance, relational hierarchy knowledge and awareness: results of an instructional framing manipulation.

PubMed

Kumaran, Dharshan; Ludwig, Hans

2013-12-01

The transitive inference (TI) paradigm has been widely used to examine the role of the hippocampus in generalization. Here we consider a surprising feature of experimental findings in this task: the relatively poor transitivity performance and levels of hierarchy knowledge achieved by adult human subjects. We focused on the influence of the task instructions on participants' subsequent performance--a single-word framing manipulation which either specified the relation between items as transitive (i.e., OLD-FRAME: choose which item is "older") or left it ambiguous (i.e., NO-FRAME: choose which item is "correct"). We show a marked but highly specific effect of manipulating prior knowledge through instruction: transitivity performance and levels of relational hierarchy knowledge were enhanced, but premise performance unchanged. Further, we show that hierarchy recall accuracy, but not conventional awareness scores, was a significant predictor of inferential performance across the entire group of participants. The current study has four main implications: first, our findings establish the importance of the task instructions, and prior knowledge, in the TI paradigm--suggesting that they influence the size of the overall hypothesis space (e.g., to favor a linear hierarchical structure over other possibilities in the OLD-FRAME). Second, the dissociable effects of the instructional frame on premise and inference performance provide evidence for the operation of distinct underlying mechanisms (i.e., an associative mechanism vs. relational hierarchy knowledge). Third, our findings suggest that a detailed measurement of hierarchy recall accuracy may be a more sensitive index of relational hierarchy knowledge, than conventional awareness score--and should be used in future studies investigating links between awareness and inferential performance. Finally, our study motivates an experimental setting that ensures robust hierarchy learning across participants--therefore facilitating study of the neural mechanisms underlying the learning and representation of linear hierarchies. Copyright © 2013 The Authors. Hippocampus Published by Wiley Periodicals, Inc.

Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback.

PubMed

Orhan, A Emin; Ma, Wei Ji

2017-07-26

Animals perform near-optimal probabilistic inference in a wide range of psychophysical tasks. Probabilistic inference requires trial-to-trial representation of the uncertainties associated with task variables and subsequent use of this representation. Previous work has implemented such computations using neural networks with hand-crafted and task-dependent operations. We show that generic neural networks trained with a simple error-based learning rule perform near-optimal probabilistic inference in nine common psychophysical tasks. In a probabilistic categorization task, error-based learning in a generic network simultaneously explains a monkey's learning curve and the evolution of qualitative aspects of its choice behavior. In all tasks, the number of neurons required for a given level of performance grows sublinearly with the input population size, a substantial improvement on previous implementations of probabilistic inference. The trained networks develop a novel sparsity-based probabilistic population code. Our results suggest that probabilistic inference emerges naturally in generic neural networks trained with error-based learning rules.Behavioural tasks often require probability distributions to be inferred about task specific variables. Here, the authors demonstrate that generic neural networks can be trained using a simple error-based learning rule to perform such probabilistic computations efficiently without any need for task specific operations.

Memory-Based Simple Heuristics as Attribute Substitution: Competitive Tests of Binary Choice Inference Models.

PubMed

Honda, Hidehito; Matsuka, Toshihiko; Ueda, Kazuhiro

2017-05-01

Some researchers on binary choice inference have argued that people make inferences based on simple heuristics, such as recognition, fluency, or familiarity. Others have argued that people make inferences based on available knowledge. To examine the boundary between heuristic and knowledge usage, we examine binary choice inference processes in terms of attribute substitution in heuristic use (Kahneman & Frederick, 2005). In this framework, it is predicted that people will rely on heuristic or knowledge-based inference depending on the subjective difficulty of the inference task. We conducted competitive tests of binary choice inference models representing simple heuristics (fluency and familiarity heuristics) and knowledge-based inference models. We found that a simple heuristic model (especially a familiarity heuristic model) explained inference patterns for subjectively difficult inference tasks, and that a knowledge-based inference model explained subjectively easy inference tasks. These results were consistent with the predictions of the attribute substitution framework. Issues on usage of simple heuristics and psychological processes are discussed. Copyright © 2016 Cognitive Science Society, Inc.

Atypical Learning in Autism Spectrum Disorders: A Functional Magnetic Resonance Imaging Study of Transitive Inference

PubMed Central

Solomon, Marjorie; Ragland, J. Daniel; Niendam, Tara A.; Lesh, Tyler A.; Beck, Jonathan S.; Matter, John C.; Frank, Michael J.; Carter, Cameron S.

2015-01-01

Objective To investigate the neural mechanisms underlying impairments in generalizing learning shown by adolescents with autism spectrum disorder (ASD). Method Twenty-one high-functioning individuals with ASD aged 12–18 years, and 23 gender, IQ, and age-matched adolescents with typical development (TYP) completed a transitive inference (TI) task implemented using rapid event-related functional magnetic resonance imaging (fMRI). They were trained on overlapping pairs in a stimulus hierarchy of colored ovals where A>B>C>D>E>F and then tested on generalizing this training to new stimulus pairings (AF, BD, BE) in a “Big Game.” Whole-brain univariate, region of interest, and functional connectivity analyses were used. Results During training, TYP exhibited increased recruitment of the prefrontal cortex (PFC), while the group with ASD showed greater functional connectivity between the PFC and the anterior cingulate cortex (ACC). Both groups recruited the hippocampus and caudate comparably; however, functional connectivity between these regions was positively associated with TI performance for only the group with ASD. During the Big Game, TYP showed greater recruitment of the PFC, parietal cortex, and the ACC. Recruitment of these regions increased with age in the group with ASD. Conclusion During TI, TYP recruited cognitive control-related brain regions implicated in mature problem solving/reasoning including the PFC, parietal cortex, and ACC, while the group with ASD showed functional connectivity of the hippocampus and the caudate that was associated with task performance. Failure to reliably engage cognitive control-related brain regions may produce less integrated flexible learning in those with ASD unless they are provided with task support that in essence provides them with cognitive control, but this pattern may normalize with age. PMID:26506585

Prefrontal Cortex: Role in Acquisition of Overlapping Associations and Transitive Inference

ERIC Educational Resources Information Center

DeVito, Loren M.; Lykken, Christine; Kanter, Benjamin R.; Eichenbaum, Howard

2010-01-01

"Transitive inference" refers to the ability to judge from memory the relationships between indirectly related items that compose a hierarchically organized series, and this capacity is considered a fundamental feature of relational memory. Here we explored the role of the prefrontal cortex in transitive inference by examining the performance of…

Effects of Knowledge and Display Design on Comprehension of Complex Graphics

ERIC Educational Resources Information Center

Canham, Matt; Hegarty, Mary

2010-01-01

In two experiments, participants made inferences from weather maps, before and after they received instruction about relevant meteorological principles. Different versions of the maps showed either task-relevant information alone, or both task-relevant and task-irrelevant information. Participants improved on the inference task after instruction,…

Social representations and contextual adjustments as two distinct components of the Theory of Mind brain network: Evidence from the REMICS task.

PubMed

Lavoie, Marie-Audrey; Vistoli, Damien; Sutliff, Stephanie; Jackson, Philip L; Achim, Amélie M

2016-08-01

Theory of mind (ToM) refers to the ability to infer the mental states of others. Behavioral measures of ToM usually present information about both a character and the context in which this character is placed, and these different pieces of information can be used to infer the character's mental states. A set of brain regions designated as the ToM brain network is recognized to support (ToM) inferences. Different brain regions within that network could however support different ToM processes. This functional magnetic resonance imaging (fMRI) study aimed to distinguish the brain regions supporting two aspects inherent to many ToM tasks, i.e., the ability to infer or represent mental states and the ability to use the context to adjust these inferences. Nineteen healthy subjects were scanned during the REMICS task, a novel task designed to orthogonally manipulate mental state inferences (as opposed to physical inferences) and contextual adjustments of inferences (as opposed to inferences that do not require contextual adjustments). We observed that mental state inferences and contextual adjustments, which are important aspects of most behavioral ToM tasks, rely on distinct brain regions or subregions within the classical brain network activated in previous ToM research. Notably, an interesting dissociation emerged within the medial prefrontal cortex (mPFC) and temporo-parietal junctions (TPJ) such that the inferior part of these brain regions responded to mental state inferences while the superior part of these brain regions responded to the requirement for contextual adjustments. This study provides evidence that the overall set of brain regions activated during ToM tasks supports different processes, and highlights that cognitive processes related to contextual adjustments have an important role in ToM and should be further studied. Copyright © 2016 Elsevier Ltd. All rights reserved.

Inference of beliefs and emotions in patients with Alzheimer's disease.

PubMed

Zaitchik, Deborah; Koff, Elissa; Brownell, Hiram; Winner, Ellen; Albert, Marilyn

2006-01-01

The present study compared 20 patients with mild to moderate Alzheimer's disease with 20 older controls (ages 69-94 years) on their ability to make inferences about emotions and beliefs in others. Six tasks tested their ability to make 1st-order and 2nd-order inferences as well as to offer explanations and moral evaluations of human action by appeal to emotions and beliefs. Results showed that the ability to infer emotions and beliefs in 1st-order tasks remains largely intact in patients with mild to moderate Alzheimer's. Patients were able to use mental states in the prediction, explanation, and moral evaluation of behavior. Impairment on 2nd-order tasks involving inference of mental states was equivalent to impairment on control tasks, suggesting that patients' difficulty is secondary to their cognitive impairments. ((c) 2006 APA, all rights reserved).

Recognition of human activity characteristics based on state transitions modeling technique

NASA Astrophysics Data System (ADS)

Elangovan, Vinayak; Shirkhodaie, Amir

2012-06-01

Human Activity Discovery & Recognition (HADR) is a complex, diverse and challenging task but yet an active area of ongoing research in the Department of Defense. By detecting, tracking, and characterizing cohesive Human interactional activity patterns, potential threats can be identified which can significantly improve situation awareness, particularly, in Persistent Surveillance Systems (PSS). Understanding the nature of such dynamic activities, inevitably involves interpretation of a collection of spatiotemporally correlated activities with respect to a known context. In this paper, we present a State Transition model for recognizing the characteristics of human activities with a link to a prior contextbased ontology. Modeling the state transitions between successive evidential events determines the activities' temperament. The proposed state transition model poses six categories of state transitions including: Human state transitions of Object handling, Visibility, Entity-entity relation, Human Postures, Human Kinematics and Distance to Target. The proposed state transition model generates semantic annotations describing the human interactional activities via a technique called Casual Event State Inference (CESI). The proposed approach uses a low cost kinect depth camera for indoor and normal optical camera for outdoor monitoring activities. Experimental results are presented here to demonstrate the effectiveness and efficiency of the proposed technique.

A quantum probability framework for human probabilistic inference.

PubMed

Trueblood, Jennifer S; Yearsley, James M; Pothos, Emmanuel M

2017-09-01

There is considerable variety in human inference (e.g., a doctor inferring the presence of a disease, a juror inferring the guilt of a defendant, or someone inferring future weight loss based on diet and exercise). As such, people display a wide range of behaviors when making inference judgments. Sometimes, people's judgments appear Bayesian (i.e., normative), but in other cases, judgments deviate from the normative prescription of classical probability theory. How can we combine both Bayesian and non-Bayesian influences in a principled way? We propose a unified explanation of human inference using quantum probability theory. In our approach, we postulate a hierarchy of mental representations, from 'fully' quantum to 'fully' classical, which could be adopted in different situations. In our hierarchy of models, moving from the lowest level to the highest involves changing assumptions about compatibility (i.e., how joint events are represented). Using results from 3 experiments, we show that our modeling approach explains 5 key phenomena in human inference including order effects, reciprocity (i.e., the inverse fallacy), memorylessness, violations of the Markov condition, and antidiscounting. As far as we are aware, no existing theory or model can explain all 5 phenomena. We also explore transitions in our hierarchy, examining how representations change from more quantum to more classical. We show that classical representations provide a better account of data as individuals gain familiarity with a task. We also show that representations vary between individuals, in a way that relates to a simple measure of cognitive style, the Cognitive Reflection Test. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A Bayesian Interpretation of First-Order Phase Transitions

NASA Astrophysics Data System (ADS)

Davis, Sergio; Peralta, Joaquín; Navarrete, Yasmín; González, Diego; Gutiérrez, Gonzalo

2016-03-01

In this work we review the formalism used in describing the thermodynamics of first-order phase transitions from the point of view of maximum entropy inference. We present the concepts of transition temperature, latent heat and entropy difference between phases as emergent from the more fundamental concept of internal energy, after a statistical inference analysis. We explicitly demonstrate this point of view by making inferences on a simple game, resulting in the same formalism as in thermodynamical phase transitions. We show that analogous quantities will inevitably arise in any problem of inferring the result of a yes/no question, given two different states of knowledge and information in the form of expectation values. This exposition may help to clarify the role of these thermodynamical quantities in the context of different first-order phase transitions such as the case of magnetic Hamiltonians (e.g. the Potts model).

Inferential Learning of Serial Order of Perceptual Categories by Rhesus Monkeys (Macaca mulatta)

PubMed Central

2017-01-01

Category learning in animals is typically trained explicitly, in most instances by varying the exemplars of a single category in a matching-to-sample task. Here, we show that male rhesus macaques can learn categories by a transitive inference paradigm in which novel exemplars of five categories were presented throughout training. Instead of requiring decisions about a constant set of repetitively presented stimuli, we studied the macaque's ability to determine the relative order of multiple exemplars of particular stimuli that were rarely repeated. Ordinal decisions generalized both to novel stimuli and, as a consequence, to novel pairings. Thus, we showed that rhesus monkeys could learn to categorize on the basis of implied ordinal position, without prior matching-to-sample training, and that they could then make inferences about category order. Our results challenge the plausibility of association models of category learning and broaden the scope of the transitive inference paradigm. SIGNIFICANCE STATEMENT The cognitive abilities of nonhuman animals are of enduring interest to scientists and the general public because they blur the dividing line between human and nonhuman intelligence. Categorization and sequence learning are highly abstract cognitive abilities each in their own right. This study is the first to provide evidence that visual categories can be ordered serially by macaque monkeys using a behavioral paradigm that provides no explicit feedback about category or serial order. These results strongly challenge accounts of learning based on stimulus–response associations. PMID:28546309

Atypical Learning in Autism Spectrum Disorders: A Functional Magnetic Resonance Imaging Study of Transitive Inference.

PubMed

Solomon, Marjorie; Ragland, J Daniel; Niendam, Tara A; Lesh, Tyler A; Beck, Jonathan S; Matter, John C; Frank, Michael J; Carter, Cameron S

2015-11-01

To investigate the neural mechanisms underlying impairments in generalizing learning shown by adolescents with autism spectrum disorder (ASD). A total of 21 high-functioning individuals with ASD aged 12 to 18 years, and 23 gender-, IQ-, and age-matched adolescents with typical development (TYP), completed a transitive inference (TI) task implemented using rapid event-related functional magnetic resonance imaging (fMRI). Participants were trained on overlapping pairs in a stimulus hierarchy of colored ovals where A>B>C>D>E>F and then tested on generalizing this training to new stimulus pairings (AF, BD, BE) in a "Big Game." Whole-brain univariate, region of interest, and functional connectivity analyses were used. During training, the TYP group exhibited increased recruitment of the prefrontal cortex (PFC), whereas the group with ASD showed greater functional connectivity between the PFC and the anterior cingulate cortex (ACC). Both groups recruited the hippocampus and caudate comparably; however, functional connectivity between these regions was positively associated with TI performance for only the group with ASD. During the Big Game, the TYP group showed greater recruitment of the PFC, parietal cortex, and the ACC. Recruitment of these regions increased with age in the group with ASD. During TI, TYP individuals recruited cognitive control-related brain regions implicated in mature problem solving/reasoning including the PFC, parietal cortex, and ACC, whereas the group with ASD showed functional connectivity of the hippocampus and the caudate that was associated with task performance. Failure to reliably engage cognitive control-related brain regions may produce less integrated flexible learning in individuals with ASD unless they are provided with task support that, in essence, provides them with cognitive control; however, this pattern may normalize with age. Copyright © 2015 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

Development of the Ability to Use Facial, Situational, and Vocal Cues to Infer Others' Affective States.

ERIC Educational Resources Information Center

Farber, Ellen A.; Moely, Barbara E.

Results of two studies investigating children's abilities to use different kinds of cues to infer another's affective state are reported in this paper. In the first study, 48 children (3, 4, and 6 to 7 years of age) were given three different kinds of tasks (interpersonal task, facial recognition task, and vocal recognition task). A cross-age…

Human Inferences about Sequences: A Minimal Transition Probability Model

PubMed Central

2016-01-01

The brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations. Experimental evidence for these expectations and their violations include explicit reports, sequential effects on reaction times, and mismatch or surprise signals recorded in electrophysiology and functional MRI. Here, we explore the hypothesis that the brain acts as a near-optimal inference device that constantly attempts to infer the time-varying matrix of transition probabilities between the stimuli it receives, even when those stimuli are in fact fully unpredictable. This parsimonious Bayesian model, with a single free parameter, accounts for a broad range of findings on surprise signals, sequential effects and the perception of randomness. Notably, it explains the pervasive asymmetry between repetitions and alternations encountered in those studies. Our analysis suggests that a neural machinery for inferring transition probabilities lies at the core of human sequence knowledge. PMID:28030543

Memory Indexing: A Novel Method for Tracing Memory Processes in Complex Cognitive Tasks

ERIC Educational Resources Information Center

Renkewitz, Frank; Jahn, Georg

2012-01-01

We validate an eye-tracking method applicable for studying memory processes in complex cognitive tasks. The method is tested with a task on probabilistic inferences from memory. It provides valuable data on the time course of processing, thus clarifying previous results on heuristic probabilistic inference. Participants learned cue values of…

Premise and Inference Memory as a Function of Age and Context.

ERIC Educational Resources Information Center

Wagner, Michael; Rohwer, William D., Jr.

A sentence completion task was used to investigate age differences in childrens' ability or inclination to invoke premises and inferences during prose processing, tasks which (according to the constructive hypothesis) adults typically perform. A pilot study confirmed that the sentence completion task was preferable to the recognition paradigm,…

The Sense of Confidence during Probabilistic Learning: A Normative Account.

PubMed

Meyniel, Florent; Schlunegger, Daniel; Dehaene, Stanislas

2015-06-01

Learning in a stochastic environment consists of estimating a model from a limited amount of noisy data, and is therefore inherently uncertain. However, many classical models reduce the learning process to the updating of parameter estimates and neglect the fact that learning is also frequently accompanied by a variable "feeling of knowing" or confidence. The characteristics and the origin of these subjective confidence estimates thus remain largely unknown. Here we investigate whether, during learning, humans not only infer a model of their environment, but also derive an accurate sense of confidence from their inferences. In our experiment, humans estimated the transition probabilities between two visual or auditory stimuli in a changing environment, and reported their mean estimate and their confidence in this report. To formalize the link between both kinds of estimate and assess their accuracy in comparison to a normative reference, we derive the optimal inference strategy for our task. Our results indicate that subjects accurately track the likelihood that their inferences are correct. Learning and estimating confidence in what has been learned appear to be two intimately related abilities, suggesting that they arise from a single inference process. We show that human performance matches several properties of the optimal probabilistic inference. In particular, subjective confidence is impacted by environmental uncertainty, both at the first level (uncertainty in stimulus occurrence given the inferred stochastic characteristics) and at the second level (uncertainty due to unexpected changes in these stochastic characteristics). Confidence also increases appropriately with the number of observations within stable periods. Our results support the idea that humans possess a quantitative sense of confidence in their inferences about abstract non-sensory parameters of the environment. This ability cannot be reduced to simple heuristics, it seems instead a core property of the learning process.

The Sense of Confidence during Probabilistic Learning: A Normative Account

PubMed Central

Meyniel, Florent; Schlunegger, Daniel; Dehaene, Stanislas

2015-01-01

Learning in a stochastic environment consists of estimating a model from a limited amount of noisy data, and is therefore inherently uncertain. However, many classical models reduce the learning process to the updating of parameter estimates and neglect the fact that learning is also frequently accompanied by a variable “feeling of knowing” or confidence. The characteristics and the origin of these subjective confidence estimates thus remain largely unknown. Here we investigate whether, during learning, humans not only infer a model of their environment, but also derive an accurate sense of confidence from their inferences. In our experiment, humans estimated the transition probabilities between two visual or auditory stimuli in a changing environment, and reported their mean estimate and their confidence in this report. To formalize the link between both kinds of estimate and assess their accuracy in comparison to a normative reference, we derive the optimal inference strategy for our task. Our results indicate that subjects accurately track the likelihood that their inferences are correct. Learning and estimating confidence in what has been learned appear to be two intimately related abilities, suggesting that they arise from a single inference process. We show that human performance matches several properties of the optimal probabilistic inference. In particular, subjective confidence is impacted by environmental uncertainty, both at the first level (uncertainty in stimulus occurrence given the inferred stochastic characteristics) and at the second level (uncertainty due to unexpected changes in these stochastic characteristics). Confidence also increases appropriately with the number of observations within stable periods. Our results support the idea that humans possess a quantitative sense of confidence in their inferences about abstract non-sensory parameters of the environment. This ability cannot be reduced to simple heuristics, it seems instead a core property of the learning process. PMID:26076466

Thinking about the Weather: How Display Salience and Knowledge Affect Performance in a Graphic Inference Task

ERIC Educational Resources Information Center

Hegarty, Mary; Canham, Matt S.; Fabrikant, Sara I.

2010-01-01

Three experiments examined how bottom-up and top-down processes interact when people view and make inferences from complex visual displays (weather maps). Bottom-up effects of display design were investigated by manipulating the relative visual salience of task-relevant and task-irrelevant information across different maps. Top-down effects of…

Inductive reasoning and the understanding of intention in schizophrenia.

PubMed

Corcoran, Rhiannon

2003-08-01

The study explored the relationship between the understanding of intention in veiled speech acts and the ability to reason inductively. A total of 39 people with DSM-IV-defined schizophrenia with no behavioural signs and 44 healthy participants performed the Hinting Task, a measure of pragmatic language in which the speaker's intention must be inferred, and a measure of inductive reasoning (Aha! Sentences) in which the meaning of ambiguous nonsocial sentences had to be inferred. The participants also completed measures of general intellectual ability, immediate memory for narrative and social problem-solving ability. A substantial correlation was found between performance on the inductive reasoning task and the Hinting Task in the sample of people with schizophrenia. The same relationship was not seen in the normal control sample. The robust relationship between these two measures in this sample survived when the roles of immediate memory for narrative and intellectual ability were controlled for. Furthermore, the relationship was distinctly more compelling for the patients who were currently ill compared to those in remission. These data suggest that people with schizophrenia use a different strategy to infer the meaning behind pragmatic language than that used by normally functioning adults. It is suggested that a reliance on different, possibly less specialised, skills in this group to perform this simple social inference task underlies their deficient performance on this and other measures of social inference. The fact that the relationship between the tasks in patients in remission is not as robust implies that the use of specialised skills to perform social inference tasks may be compromised most significantly during acute phases.

Distinguishing logic from association in the solution of an invisible displacement task by children (Homo sapiens) and dogs (Canis familiaris): using negation of disjunction.

PubMed

Watson, J S; Gergely, G; Csanyi, V; Topal, J; Gacsi, M; Sarkozi, Z

2001-09-01

Prior research on the ability to solve the Piagetian invisible displacement task has focused on prerequisite representational capacity. This study examines the additional prerequisite of deduction. As in other tasks (e.g., conservation and transitivity), it is difficult to distinguish between behavior that reflects logical inference from behavior that reflects associative generalization. Using the role of negation in logic whereby negative feedback about one belief increases the certainty of another (e.g., a disjunctive syllogism), task-naive dogs (Canis familiaris; n=19) and 4- to 6-year-old children (Homo sapiens; n=24) were given a task wherein a desirable object was shown to have disappeared from a container after it had passed behind 3 separate screens. As predicted, children (as per logic of negated disjunction) tended to increase their speed of checking the 3rd screen after failing to find the object behind the first 2 screens, whereas dogs (as per associative extinction) tended to significantly decrease their speed of checking the 3rd screen after failing to find the object behind the first 2 screens.

Recurrence networks from multivariate signals for uncovering dynamic transitions of horizontal oil-water stratified flows

NASA Astrophysics Data System (ADS)

Gao, Zhong-Ke; Zhang, Xin-Wang; Jin, Ning-De; Donner, Reik V.; Marwan, Norbert; Kurths, Jürgen

2013-09-01

Characterizing the mechanism of drop formation at the interface of horizontal oil-water stratified flows is a fundamental problem eliciting a great deal of attention from different disciplines. We experimentally and theoretically investigate the formation and transition of horizontal oil-water stratified flows. We design a new multi-sector conductance sensor and measure multivariate signals from two different stratified flow patterns. Using the Adaptive Optimal Kernel Time-Frequency Representation (AOK TFR) we first characterize the flow behavior from an energy and frequency point of view. Then, we infer multivariate recurrence networks from the experimental data and investigate the cross-transitivity for each constructed network. We find that the cross-transitivity allows quantitatively uncovering the flow behavior when the stratified flow evolves from a stable state to an unstable one and recovers deeper insights into the mechanism governing the formation of droplets at the interface of stratified flows, a task that existing methods based on AOK TFR fail to work. These findings present a first step towards an improved understanding of the dynamic mechanism leading to the transition of horizontal oil-water stratified flows from a complex-network perspective.

Unraveling multiple changes in complex climate time series using Bayesian inference

NASA Astrophysics Data System (ADS)

Berner, Nadine; Trauth, Martin H.; Holschneider, Matthias

2016-04-01

Change points in time series are perceived as heterogeneities in the statistical or dynamical characteristics of observations. Unraveling such transitions yields essential information for the understanding of the observed system. The precise detection and basic characterization of underlying changes is therefore of particular importance in environmental sciences. We present a kernel-based Bayesian inference approach to investigate direct as well as indirect climate observations for multiple generic transition events. In order to develop a diagnostic approach designed to capture a variety of natural processes, the basic statistical features of central tendency and dispersion are used to locally approximate a complex time series by a generic transition model. A Bayesian inversion approach is developed to robustly infer on the location and the generic patterns of such a transition. To systematically investigate time series for multiple changes occurring at different temporal scales, the Bayesian inversion is extended to a kernel-based inference approach. By introducing basic kernel measures, the kernel inference results are composed into a proxy probability to a posterior distribution of multiple transitions. Thus, based on a generic transition model a probability expression is derived that is capable to indicate multiple changes within a complex time series. We discuss the method's performance by investigating direct and indirect climate observations. The approach is applied to environmental time series (about 100 a), from the weather station in Tuscaloosa, Alabama, and confirms documented instrumentation changes. Moreover, the approach is used to investigate a set of complex terrigenous dust records from the ODP sites 659, 721/722 and 967 interpreted as climate indicators of the African region of the Plio-Pleistocene period (about 5 Ma). The detailed inference unravels multiple transitions underlying the indirect climate observations coinciding with established global climate events.

The APA Task Force on Statistical Inference (TFSI) Report as a Framework for Teaching and Evaluating Students' Understandings of Study Validity.

ERIC Educational Resources Information Center

Thompson, Bruce

Web-based statistical instruction, like all statistical instruction, ought to focus on teaching the essence of the research endeavor: the exercise of reflective judgment. Using the framework of the recent report of the American Psychological Association (APA) Task Force on Statistical Inference (Wilkinson and the APA Task Force on Statistical…

Improved probabilistic inference as a general learning mechanism with action video games.

PubMed

Green, C Shawn; Pouget, Alexandre; Bavelier, Daphne

2010-09-14

Action video game play benefits performance in an array of sensory, perceptual, and attentional tasks that go well beyond the specifics of game play [1-9]. That a training regimen may induce improvements in so many different skills is notable because the majority of studies on training-induced learning report improvements on the trained task but limited transfer to other, even closely related, tasks ([10], but see also [11-13]). Here we ask whether improved probabilistic inference may explain such broad transfer. By using a visual perceptual decision making task [14, 15], the present study shows for the first time that action video game experience does indeed improve probabilistic inference. A neural model of this task [16] establishes how changing a single parameter, namely the strength of the connections between the neural layer providing the momentary evidence and the layer integrating the evidence over time, captures improvements in action-gamers behavior. These results were established in a visual, but also in a novel auditory, task, indicating generalization across modalities. Thus, improved probabilistic inference provides a general mechanism for why action video game playing enhances performance in a wide variety of tasks. In addition, this mechanism may serve as a signature of training regimens that are likely to produce transfer of learning. Copyright © 2010 Elsevier Ltd. All rights reserved.

Working memory supports inference learning just like classification learning.

PubMed

Craig, Stewart; Lewandowsky, Stephan

2013-08-01

Recent research has found a positive relationship between people's working memory capacity (WMC) and their speed of category learning. To date, only classification-learning tasks have been considered, in which people learn to assign category labels to objects. It is unknown whether learning to make inferences about category features might also be related to WMC. We report data from a study in which 119 participants undertook classification learning and inference learning, and completed a series of WMC tasks. Working memory capacity was positively related to people's classification and inference learning performance.

Understanding behavior under nonverbal transitive-inference procedures: Stimulus-control-topography analyses.

PubMed

Galizio, Ann; Doughty, Adam H; Williams, Dean C; Saunders, Kathryn J

2017-07-01

Following training with verbal stimulus relations involving A is greater than B and B is greater than C, verbally-competent individuals reliably select A>C when asked "which is greater, A or C?" (i.e., verbal transitive inference). This result is easy to interpret. Nonhuman animals and humans with and without intellectual disabilities have been exposed to nonverbal transitive-inference procedures involving trained arbitrary stimulus relations. Following the training of A+B-, B+C-, C+D-, and D+E-, B reliably is selected over D (i.e., nonverbal transitive inference). Such findings are more challenging to interpret. The present research explored accounts of nonverbal transitive inference based in transitive inference per se, reinforcement, such as value-transfer theory, and operant stimulus control. In Experiment 1, college students selected B>G following the training of A+B-, B+C-, C+D-///E+F-, F+G-, and G+H- (where///signifies the omission of D+E-). In Experiment 2, college students selected B>G following the training of A+B-, B+C-, C+D-///E+F-, F+G-, and G+X- (where X refers to 10 stimuli that alternated across trials). In Experiment 3, college students selected G>B following the training of Y+B-, B+C-, C+D-///E+F-, F+G-, and G+X- (where Y and X refer to 10 stimuli, respectively, that alternated across trials). These findings are discussed in the context of operant stimulus control by offering an approach based in stimulus B typically acquiring only a select stimulus control topography. Copyright © 2017 Elsevier B.V. All rights reserved.

Neural correlates of species-typical illogical cognitive bias in human inference.

PubMed

Ogawa, Akitoshi; Yamazaki, Yumiko; Ueno, Kenichi; Cheng, Kang; Iriki, Atsushi

2010-09-01

The ability to think logically is a hallmark of human intelligence, yet our innate inferential abilities are marked by implicit biases that often lead to illogical inference. For example, given AB ("if A then B"), people frequently but fallaciously infer the inverse, BA. This mode of inference, called symmetry, is logically invalid because, although it may be true, it is not necessarily true. Given pairs of conditional relations, such as AB and BC, humans reflexively perform two additional modes of inference: transitivity, whereby one (validly) infers AC; and equivalence, whereby one (invalidly) infers CA. In sharp contrast, nonhuman animals can handle transitivity but can rarely be made to acquire symmetry or equivalence. In the present study, human subjects performed logical and illogical inferences about the relations between abstract, visually presented figures while their brain activation was monitored with fMRI. The prefrontal, medial frontal, and intraparietal cortices were activated during all modes of inference. Additional activation in the precuneus and posterior parietal cortex was observed during transitivity and equivalence, which may reflect the need to retrieve the intermediate stimulus (B) from memory. Surprisingly, the patterns of brain activation in illogical and logical inference were very similar. We conclude that the observed inference-related fronto-parietal network is adapted for processing categorical, but not logical, structures of association among stimuli. Humans might prefer categorization over the memorization of logical structures in order to minimize the cognitive working memory load when processing large volumes of information.

A Drawing Task to Assess Emotion Inference in Language-Impaired Children.

PubMed

Vendeville, Nathalie; Blanc, Nathalie; Brechet, Claire

2015-10-01

Studies investigating the ability of children with language impairment (LI) to infer emotions rely on verbal responses (which can be challenging for these children) and/or the selection of a card representing an emotion (which limits the response range). In contrast, a drawing task might allow a broad spectrum of responses without involving language. This study used a drawing task to compare the ability to make emotional inferences in children with and without LI. Twenty-two children with LI and 22 typically developing children ages 6 to 10 years were assessed in school during 3 sessions. They were asked to listen to audio stories. At specific moments, the experimenter stopped the recording and asked children to complete the drawing of a face to depict the emotion felt by the story's character. Three adult study-blind judges were subsequently asked to evaluate the expressiveness of the drawings. Children with LI had more difficulty than typically developing children making emotional inferences. Children with LI also made more errors of different valence than their typically developing peers. Our findings confirm that children with LI show difficulty in producing emotional inferences, even when performing a drawing task--a relatively language-free response mode.

Real-time value-driven diagnosis

NASA Technical Reports Server (NTRS)

Dambrosio, Bruce

1995-01-01

Diagnosis is often thought of as an isolated task in theoretical reasoning (reasoning with the goal of updating our beliefs about the world). We present a decision-theoretic interpretation of diagnosis as a task in practical reasoning (reasoning with the goal of acting in the world), and sketch components of our approach to this task. These components include an abstract problem description, a decision-theoretic model of the basic task, a set of inference methods suitable for evaluating the decision representation in real-time, and a control architecture to provide the needed continuing coordination between the agent and its environment. A principal contribution of this work is the representation and inference methods we have developed, which extend previously available probabilistic inference methods and narrow, somewhat, the gap between probabilistic and logical models of diagnosis.

Constructive Processes in Memory for Order.

ERIC Educational Resources Information Center

Smith, Kirk H.; And Others

The transformation of episodic inputs to semantic representations was studied in two very similar tasks. In one, subjects were required to infer the underlying four-term linear ordering from three comparative sentences such as, "The teacher is taller than the doctor." In the second task, subjects inferred underlying 4- and 5-digit…

Learning about the internal structure of categories through classification and feature inference.

PubMed

Jee, Benjamin D; Wiley, Jennifer

2014-01-01

Previous research on category learning has found that classification tasks produce representations that are skewed toward diagnostic feature dimensions, whereas feature inference tasks lead to richer representations of within-category structure. Yet, prior studies often measure category knowledge through tasks that involve identifying only the typical features of a category. This neglects an important aspect of a category's internal structure: how typical and atypical features are distributed within a category. The present experiments tested the hypothesis that inference learning results in richer knowledge of internal category structure than classification learning. We introduced several new measures to probe learners' representations of within-category structure. Experiment 1 found that participants in the inference condition learned and used a wider range of feature dimensions than classification learners. Classification learners, however, were more sensitive to the presence of atypical features within categories. Experiment 2 provided converging evidence that classification learners were more likely to incorporate atypical features into their representations. Inference learners were less likely to encode atypical category features, even in a "partial inference" condition that focused learners' attention on the feature dimensions relevant to classification. Overall, these results are contrary to the hypothesis that inference learning produces superior knowledge of within-category structure. Although inference learning promoted representations that included a broad range of category-typical features, classification learning promoted greater sensitivity to the distribution of typical and atypical features within categories.

Genetic network inference as a series of discrimination tasks.

PubMed

Kimura, Shuhei; Nakayama, Satoshi; Hatakeyama, Mariko

2009-04-01

Genetic network inference methods based on sets of differential equations generally require a great deal of time, as the equations must be solved many times. To reduce the computational cost, researchers have proposed other methods for inferring genetic networks by solving sets of differential equations only a few times, or even without solving them at all. When we try to obtain reasonable network models using these methods, however, we must estimate the time derivatives of the gene expression levels with great precision. In this study, we propose a new method to overcome the drawbacks of inference methods based on sets of differential equations. Our method infers genetic networks by obtaining classifiers capable of predicting the signs of the derivatives of the gene expression levels. For this purpose, we defined a genetic network inference problem as a series of discrimination tasks, then solved the defined series of discrimination tasks with a linear programming machine. Our experimental results demonstrated that the proposed method is capable of correctly inferring genetic networks, and doing so more than 500 times faster than the other inference methods based on sets of differential equations. Next, we applied our method to actual expression data of the bacterial SOS DNA repair system. And finally, we demonstrated that our approach relates to the inference method based on the S-system model. Though our method provides no estimation of the kinetic parameters, it should be useful for researchers interested only in the network structure of a target system. Supplementary data are available at Bioinformatics online.

Effects of Forward and Backward Contextual Elaboration on Lexical Inferences: Evidence from a Semantic Relatedness Judgment Task

ERIC Educational Resources Information Center

Hamada, Akira

2015-01-01

Three experiments examined whether the process of lexical inferences differs according to the direction of contextual elaboration using a semantic relatedness judgment task. In Experiment 1, Japanese university students read English sentences where target unknown words were semantically elaborated by prior contextual information (forward lexical…

Natural frequencies improve Bayesian reasoning in simple and complex inference tasks

PubMed Central

Hoffrage, Ulrich; Krauss, Stefan; Martignon, Laura; Gigerenzer, Gerd

2015-01-01

Representing statistical information in terms of natural frequencies rather than probabilities improves performance in Bayesian inference tasks. This beneficial effect of natural frequencies has been demonstrated in a variety of applied domains such as medicine, law, and education. Yet all the research and applications so far have been limited to situations where one dichotomous cue is used to infer which of two hypotheses is true. Real-life applications, however, often involve situations where cues (e.g., medical tests) have more than one value, where more than two hypotheses (e.g., diseases) are considered, or where more than one cue is available. In Study 1, we show that natural frequencies, compared to information stated in terms of probabilities, consistently increase the proportion of Bayesian inferences made by medical students in four conditions—three cue values, three hypotheses, two cues, or three cues—by an average of 37 percentage points. In Study 2, we show that teaching natural frequencies for simple tasks with one dichotomous cue and two hypotheses leads to a transfer of learning to complex tasks with three cue values and two cues, with a proportion of 40 and 81% correct inferences, respectively. Thus, natural frequencies facilitate Bayesian reasoning in a much broader class of situations than previously thought. PMID:26528197

Towards the unification of inference structures in medical diagnostic tasks.

PubMed

Mira, J; Rives, J; Delgado, A E; Martínez, R

1998-01-01

The central purpose of artificial intelligence applied to medicine is to develop models for diagnosis and therapy planning at the knowledge level, in the Newell sense, and software environments to facilitate the reduction of these models to the symbol level. The usual methodology (KADS, Common-KADS, GAMES, HELIOS, Protégé, etc) has been to develop libraries of generic tasks and reusable problem-solving methods with explicit ontologies. The principal problem which clinicians have with these methodological developments concerns the diversity and complexity of new terms whose meaning is not sufficiently clear, precise, unambiguous and consensual for them to be accessible in the daily clinical environment. As a contribution to the solution of this problem, we develop in this article the conjecture that one inference structure is enough to describe the set of analysis tasks associated with medical diagnoses. To this end, we first propose a modification of the systematic diagnostic inference scheme to obtain an analysis generic task and then compare it with the monitoring and the heuristic classification task inference schemes using as comparison criteria the compatibility of domain roles (data structures), the similarity in the inferences, and the commonality in the set of assumptions which underlie the functionally equivalent models. The equivalences proposed are illustrated with several examples. Note that though our ongoing work aims to simplify the methodology and to increase the precision of the terms used, the proposal presented here should be viewed more in the nature of a conjecture.

Usefulness of near-infrared spectroscopy to detect brain dysfunction in children with autism spectrum disorder when inferring the mental state of others.

PubMed

Iwanaga, Ryoichiro; Tanaka, Goro; Nakane, Hideyuki; Honda, Sumihisa; Imamura, Akira; Ozawa, Hiroki

2013-05-01

The purpose of this study was to examine the usefulness of near-infrared spectroscopy (NIRS) for identifying abnormalities in prefrontal brain activity in children with autism spectrum disorders (ASD) as they inferred the mental states of others. The subjects were 16 children with ASD aged between 8 and 14 years and 16 age-matched healthy control children. Oxygenated hemoglobin concentration was measured in the subject's prefrontal brain region on NIRS during tasks expressing a person's mental state (MS task) and expressing an object's characteristics (OC task). There was a significant main effect of group (ASD vs control), with the control group having more activity than the ASD group. But there was no significant main effect of task (MS task vs OC task) or hemisphere (right vs left). Significant interactions of task and group were found, with the control group showing more activity than the ASD group during the MS task relative to the OC task. NIRS showed that there was lower activity in the prefrontal brain area when children with ASD performed MS tasks. Therefore, clinicians might be able to use NIRS and these tasks for conveniently detecting brain dysfunction in children with ASD related to inferring mental states, in the clinical setting. © 2013 The Authors. Psychiatry and Clinical Neurosciences © 2013 Japanese Society of Psychiatry and Neurology.

Automatic-heuristic and executive-analytic processing during reasoning: Chronometric and dual-task considerations.

PubMed

De Neys, Wim

2006-06-01

Human reasoning has been shown to overly rely on intuitive, heuristic processing instead of a more demanding analytic inference process. Four experiments tested the central claim of current dual-process theories that analytic operations involve time-consuming executive processing whereas the heuristic system would operate automatically. Participants solved conjunction fallacy problems and indicative and deontic selection tasks. Experiment 1 established that making correct analytic inferences demanded more processing time than did making heuristic inferences. Experiment 2 showed that burdening the executive resources with an attention-demanding secondary task decreased correct, analytic responding and boosted the rate of conjunction fallacies and indicative matching card selections. Results were replicated in Experiments 3 and 4 with a different secondary-task procedure. Involvement of executive resources for the deontic selection task was less clear. Findings validate basic processing assumptions of the dual-process framework and complete the correlational research programme of K. E. Stanovich and R. F. West (2000).

A Drawing Task to Assess Emotion Inference in Language-Impaired Children

ERIC Educational Resources Information Center

Vendeville, Nathalie; Blanc, Nathalie; Brechet, Claire

2015-01-01

Purpose: Studies investigating the ability of children with language impairment (LI) to infer emotions rely on verbal responses (which can be challenging for these children) and/or the selection of a card representing an emotion (which limits the response range). In contrast, a drawing task might allow a broad spectrum of responses without…

The Development of Categorization: Effects of Classification and Inference Training on Category Representation

ERIC Educational Resources Information Center

Deng, Wei; Sloutsky, Vladimir M.

2015-01-01

Does category representation change in the course of development? And if so, how and why? The current study attempted to answer these questions by examining category learning and category representation. In Experiment 1, 4-year-olds, 6-year-olds, and adults were trained with either a classification task or an inference task and their…

A quantum probability account of order effects in inference.

PubMed

Trueblood, Jennifer S; Busemeyer, Jerome R

2011-01-01

Order of information plays a crucial role in the process of updating beliefs across time. In fact, the presence of order effects makes a classical or Bayesian approach to inference difficult. As a result, the existing models of inference, such as the belief-adjustment model, merely provide an ad hoc explanation for these effects. We postulate a quantum inference model for order effects based on the axiomatic principles of quantum probability theory. The quantum inference model explains order effects by transforming a state vector with different sequences of operators for different orderings of information. We demonstrate this process by fitting the quantum model to data collected in a medical diagnostic task and a jury decision-making task. To further test the quantum inference model, a new jury decision-making experiment is developed. Using the results of this experiment, we compare the quantum inference model with two versions of the belief-adjustment model, the adding model and the averaging model. We show that both the quantum model and the adding model provide good fits to the data. To distinguish the quantum model from the adding model, we develop a new experiment involving extreme evidence. The results from this new experiment suggest that the adding model faces limitations when accounting for tasks involving extreme evidence, whereas the quantum inference model does not. Ultimately, we argue that the quantum model provides a more coherent account for order effects that was not possible before. Copyright © 2011 Cognitive Science Society, Inc.

Dynamics of embryonic stem cell differentiation inferred from single-cell transcriptomics show a series of transitions through discrete cell states

PubMed Central

Jang, Sumin; Choubey, Sandeep; Furchtgott, Leon; Zou, Ling-Nan; Doyle, Adele; Menon, Vilas; Loew, Ethan B; Krostag, Anne-Rachel; Martinez, Refugio A; Madisen, Linda; Levi, Boaz P; Ramanathan, Sharad

2017-01-01

The complexity of gene regulatory networks that lead multipotent cells to acquire different cell fates makes a quantitative understanding of differentiation challenging. Using a statistical framework to analyze single-cell transcriptomics data, we infer the gene expression dynamics of early mouse embryonic stem (mES) cell differentiation, uncovering discrete transitions across nine cell states. We validate the predicted transitions across discrete states using flow cytometry. Moreover, using live-cell microscopy, we show that individual cells undergo abrupt transitions from a naïve to primed pluripotent state. Using the inferred discrete cell states to build a probabilistic model for the underlying gene regulatory network, we further predict and experimentally verify that these states have unique response to perturbations, thus defining them functionally. Our study provides a framework to infer the dynamics of differentiation from single cell transcriptomics data and to build predictive models of the gene regulatory networks that drive the sequence of cell fate decisions during development. DOI: http://dx.doi.org/10.7554/eLife.20487.001 PMID:28296635

Implicit Value Updating Explains Transitive Inference Performance: The Betasort Model

PubMed Central

Jensen, Greg; Muñoz, Fabian; Alkan, Yelda; Ferrera, Vincent P.; Terrace, Herbert S.

2015-01-01

Transitive inference (the ability to infer that B > D given that B > C and C > D) is a widespread characteristic of serial learning, observed in dozens of species. Despite these robust behavioral effects, reinforcement learning models reliant on reward prediction error or associative strength routinely fail to perform these inferences. We propose an algorithm called betasort, inspired by cognitive processes, which performs transitive inference at low computational cost. This is accomplished by (1) representing stimulus positions along a unit span using beta distributions, (2) treating positive and negative feedback asymmetrically, and (3) updating the position of every stimulus during every trial, whether that stimulus was visible or not. Performance was compared for rhesus macaques, humans, and the betasort algorithm, as well as Q-learning, an established reward-prediction error (RPE) model. Of these, only Q-learning failed to respond above chance during critical test trials. Betasort’s success (when compared to RPE models) and its computational efficiency (when compared to full Markov decision process implementations) suggests that the study of reinforcement learning in organisms will be best served by a feature-driven approach to comparing formal models. PMID:26407227

Implicit Value Updating Explains Transitive Inference Performance: The Betasort Model.

PubMed

Jensen, Greg; Muñoz, Fabian; Alkan, Yelda; Ferrera, Vincent P; Terrace, Herbert S

2015-01-01

Transitive inference (the ability to infer that B > D given that B > C and C > D) is a widespread characteristic of serial learning, observed in dozens of species. Despite these robust behavioral effects, reinforcement learning models reliant on reward prediction error or associative strength routinely fail to perform these inferences. We propose an algorithm called betasort, inspired by cognitive processes, which performs transitive inference at low computational cost. This is accomplished by (1) representing stimulus positions along a unit span using beta distributions, (2) treating positive and negative feedback asymmetrically, and (3) updating the position of every stimulus during every trial, whether that stimulus was visible or not. Performance was compared for rhesus macaques, humans, and the betasort algorithm, as well as Q-learning, an established reward-prediction error (RPE) model. Of these, only Q-learning failed to respond above chance during critical test trials. Betasort's success (when compared to RPE models) and its computational efficiency (when compared to full Markov decision process implementations) suggests that the study of reinforcement learning in organisms will be best served by a feature-driven approach to comparing formal models.

Task Models in the Digital Ocean

ERIC Educational Resources Information Center

DiCerbo, Kristen E.

2014-01-01

The Task Model is a description of each task in a workflow. It defines attributes associated with that task. The creation of task models becomes increasingly important as the assessment tasks become more complex. Explicitly delineating the impact of task variables on the ability to collect evidence and make inferences demands thoughtfulness from…

When underadditivity of factor effects in the Psychological Refractory Period paradigm implies a bottleneck: evidence from psycholinguistics.

PubMed

Besner, Derek; Reynolds, Mike; O'Malley, Shannon

2009-11-01

The Psychological Refractory Period (PRP) paradigm is a dual-task procedure that can be used to examine the resource demands of specific cognitive processes. Inferences about the underlying processes are typically based on performance in the second of two speeded tasks. If the effect of a factor manipulated in Task 2 decreases as the stimulus onset asynchrony (SOA) between tasks decreases (underadditivity), the normative inference is that the effect of this factor occurs prior to a limited-capacity central processing mechanism. In contrast, if the effect of a factor is additive with SOA then the inference is that this indexes a process that either uses a limited-capacity central processing mechanism or occurs after some process that uses this mechanism. A heretofore unidentified exception to this logic arises when Task 2 involves two separate processes that operate in parallel, but compete. Interference with one process in Task 2 because of work on Task 1 will eliminate or reduce competition within Task 2 and is hence manifest as an underadditive interaction with decreasing SOA. This is illustrated here by reference to a PRP experiment in which the ubiquitous effect of spelling-to-sound regularity on reading aloud time is eliminated at a short SOA and by consideration of three converging lines of investigation in the PRP paradigm when Task 2 involves reading aloud.

The role of data and inference in the development and application of ecological site concepts and state-and-transition models

USDA-ARS?s Scientific Manuscript database

Information embodied in ecological site descriptions and their state-and-transition models is crucial to effective land management, and as such is needed now. There is not time (or money) to employ a traditional research-based approach (i.e., inductive/deductive, hypothesis driven inference) to addr...

Seeing it my way: a case of a selective deficit in inhibiting self-perspective.

PubMed

Samson, Dana; Apperly, Ian A; Kathirgamanathan, Umalini; Humphreys, Glyn W

2005-05-01

Little is known about the functional and neural architecture of social reasoning, one major obstacle being that we crucially lack the relevant tools to test potentially different social reasoning components. In the case of belief reasoning, previous studies have tried to separate the processes involved in belief reasoning per se from those involved in the processing of the high incidental demands such as the working memory demands of typical belief tasks. In this study, we developed new belief tasks in order to disentangle, for the first time, two perspective taking components involved in belief reasoning: (i) the ability to inhibit one's own perspective (self-perspective inhibition); and (ii) the ability to infer someone else's perspective as such (other-perspective taking). The two tasks had similar demands in other-perspective taking as they both required the participant to infer that a character has a false belief about an object's location. However, the tasks varied in the self-perspective inhibition demands. In the task with the lowest self-perspective inhibition demands, at the time the participant had to infer the character's false belief, he or she had no idea what the new object's location was. In contrast, in the task with the highest self-perspective inhibition demands, at the time the participant had to infer the character's false belief, he or she knew where the object was actually located (and this knowledge had thus to be inhibited). The two tasks were presented to a stroke patient, WBA, with right prefrontal and temporal damage. WBA performed well in the low-inhibition false-belief task but showed striking difficulty in the task placing high self-perspective inhibition demands, showing a selective deficit in inhibiting self-perspective. WBA also made egocentric errors in other social and visual perspective taking tasks, indicating a difficulty with belief attribution extending to the attribution of emotions, desires and visual experiences to other people. The case of WBA, together with the recent report of three patients impaired in belief reasoning even when self-perspective inhibition demands were reduced, provide the first neuropsychological evidence that the inhibition of one's own point of view and the ability to infer someone else's point of view rely on distinct neural and functional processes.

Inferring thermodynamic stability relationship of polymorphs from melting data.

PubMed

Yu, L

1995-08-01

This study investigates the possibility of inferring the thermodynamic stability relationship of polymorphs from their melting data. Thermodynamic formulas are derived for calculating the Gibbs free energy difference (delta G) between two polymorphs and its temperature slope from mainly the temperatures and heats of melting. This information is then used to estimate delta G, thus relative stability, at other temperatures by extrapolation. Both linear and nonlinear extrapolations are considered. Extrapolating delta G to zero gives an estimation of the transition (or virtual transition) temperature, from which the presence of monotropy or enantiotropy is inferred. This procedure is analogous to the use of solubility data measured near the ambient temperature to estimate a transition point at higher temperature. For several systems examined, the two methods are in good agreement. The qualitative rule introduced this way for inferring the presence of monotropy or enantiotropy is approximately the same as The Heat of Fusion Rule introduced previously on a statistical mechanical basis. This method is applied to 96 pairs of polymorphs from the literature. In most cases, the result agrees with the previous determination. The deviation of the calculated transition temperatures from their previous values (n = 18) is 2% on average and 7% at maximum.

Spinal motor outputs during step-to-step transitions of diverse human gaits.

PubMed

La Scaleia, Valentina; Ivanenko, Yuri P; Zelik, Karl E; Lacquaniti, Francesco

2014-01-01

Aspects of human motor control can be inferred from the coordination of muscles during movement. For instance, by combining multimuscle electromyographic (EMG) recordings with human neuroanatomy, it is possible to estimate alpha-motoneuron (MN) pool activations along the spinal cord. It has previously been shown that the spinal motor output fluctuates with the body's center-of-mass motion, with bursts of activity around foot-strike and foot lift-off during walking. However, it is not known whether these MN bursts are generalizable to other ambulation tasks, nor is it clear if the spatial locus of the activity (along the rostrocaudal axis of the spinal cord) is fixed or variable. Here we sought to address these questions by investigating the spatiotemporal characteristics of the spinal motor output during various tasks: walking forward, backward, tiptoe and uphill. We reconstructed spinal maps from 26 leg muscle EMGs, including some intrinsic foot muscles. We discovered that the various walking tasks shared qualitative similarities in their temporal spinal activation profiles, exhibiting peaks around foot-strike and foot-lift. However, we also observed differences in the segmental level and intensity of spinal activations, particularly following foot-strike. For example, forward level-ground walking exhibited a mean motor output roughly 2 times lower than the other gaits. Finally, we found that the reconstruction of the spinal motor output from multimuscle EMG recordings was relatively insensitive to the subset of muscles analyzed. In summary, our results suggested temporal similarities, but spatial differences in the segmental spinal motor outputs during the step-to-step transitions of disparate walking behaviors.

Is a "Complex" Task Really Complex? Validating the Assumption of Cognitive Task Complexity

ERIC Educational Resources Information Center

Sasayama, Shoko

2016-01-01

In research on task-based learning and teaching, it has traditionally been assumed that differing degrees of cognitive task complexity can be inferred through task design and/or observations of differing qualities in linguistic production elicited by second language (L2) communication tasks. Without validating this assumption, however, it is…

Inferring Learners' Knowledge from Their Actions

ERIC Educational Resources Information Center

Rafferty, Anna N.; LaMar, Michelle M.; Griffiths, Thomas L.

2015-01-01

Watching another person take actions to complete a goal and making inferences about that person's knowledge is a relatively natural task for people. This ability can be especially important in educational settings, where the inferences can be used for assessment, diagnosing misconceptions, and providing informative feedback. In this paper, we…

Causal Inference and Developmental Psychology

ERIC Educational Resources Information Center

Foster, E. Michael

2010-01-01

Causal inference is of central importance to developmental psychology. Many key questions in the field revolve around improving the lives of children and their families. These include identifying risk factors that if manipulated in some way would foster child development. Such a task inherently involves causal inference: One wants to know whether…

Unbiased Inference of the Masses of Transiting Planets from Radial Velocity Follow-up

NASA Astrophysics Data System (ADS)

Montet, Benjamin T.

2018-05-01

Data from the TESS mission will be used to discover hundreds of small planets amenable to radial velocity (RV) followup. Often, RV observations are obtained until a particular fractional precision on the inferred mass is achieved. I show that when the decision to stop collecting RV observations depends on the mass inferred from already-collected data, this will bias mass measurements upward, particularly when the fixed length of RV observing campaigns is considered. I suggest that observing teams should determine their observing strategy for each star before any data are collected, and all stopping criteria should not directly depend on the inferred mass. Observing teams should explicitly publish both their criteria for observing targets and deciding to end their observations, as well as their mass non-detections to avoid introducing biases into the masses---and thus inferences on the densities, compositions, and atmospheres---of transiting planets.

Inference comprehension in text reading: Performance of individuals with right- versus left-hemisphere lesions and the influence of cognitive functions.

PubMed

Silagi, Marcela Lima; Radanovic, Marcia; Conforto, Adriana Bastos; Mendonça, Lucia Iracema Zanotto; Mansur, Leticia Lessa

2018-01-01

Right-hemisphere lesions (RHL) may impair inference comprehension. However, comparative studies between left-hemisphere lesions (LHL) and RHL are rare, especially regarding reading comprehension. Moreover, further knowledge of the influence of cognition on inferential processing in this task is needed. To compare the performance of patients with RHL and LHL on an inference reading comprehension task. We also aimed to analyze the effects of lesion site and to verify correlations between cognitive functions and performance on the task. Seventy-five subjects were equally divided into the groups RHL, LHL, and control group (CG). The Implicit Management Test was used to evaluate inference comprehension. In this test, subjects read short written passages and subsequently answer five types of questions (explicit, logical, distractor, pragmatic, and other), which require different types of inferential reasoning. The cognitive functional domains of attention, memory, executive functions, language, and visuospatial abilities were assessed using the Cognitive Linguistic Quick Test (CLQT). The LHL and RHL groups presented difficulties in inferential comprehension in comparison with the CG. However, the RHL group presented lower scores than the LHL group on logical, pragmatic and other questions. A covariance analysis did not show any effect of lesion site within the hemispheres. Overall, all cognitive domains were correlated with all the types of questions from the inference test (especially logical, pragmatic, and other). Attention and visuospatial abilities affected the scores of both the RHL and LHL groups, and only memory influenced the performance of the RHL group. Lesions in either hemisphere may cause difficulties in making inferences during reading. However, processing more complex inferences was more difficult for patients with RHL than for those with LHL, which suggests that the right hemisphere plays an important role in tasks with higher comprehension demands. Cognition influences inferential processing during reading in brain-injured subjects.

Oxytocin improves behavioural and neural deficits in inferring others' social emotions in autism.

PubMed

Aoki, Yuta; Yahata, Noriaki; Watanabe, Takamitsu; Takano, Yosuke; Kawakubo, Yuki; Kuwabara, Hitoshi; Iwashiro, Norichika; Natsubori, Tatsunobu; Inoue, Hideyuki; Suga, Motomu; Takao, Hidemasa; Sasaki, Hiroki; Gonoi, Wataru; Kunimatsu, Akira; Kasai, Kiyoto; Yamasue, Hidenori

2014-11-01

Recent studies have suggested oxytocin's therapeutic effects on deficits in social communication and interaction in autism spectrum disorder through improvement of emotion recognition with direct emotional cues, such as facial expression and voice prosody. Although difficulty in understanding of others' social emotions and beliefs under conditions without direct emotional cues also plays an important role in autism spectrum disorder, no study has examined the potential effect of oxytocin on this difficulty. Here, we sequentially conducted both a case-control study and a clinical trial to investigate the potential effects of oxytocin on this difficulty at behavioural and neural levels measured using functional magnetic resonance imaging during a psychological task. This task was modified from the Sally-Anne Task, a well-known first-order false belief task. The task was optimized for investigation of the abilities to infer another person's social emotions and beliefs distinctively so as to test the hypothesis that oxytocin improves deficit in inferring others' social emotions rather than beliefs, under conditions without direct emotional cues. In the case-control study, 17 males with autism spectrum disorder showed significant behavioural deficits in inferring others' social emotions (P = 0.018) but not in inferring others' beliefs (P = 0.064) compared with 17 typically developing demographically-matched male participants. They also showed significantly less activity in the right anterior insula and posterior superior temporal sulcus during inferring others' social emotions, and in the dorsomedial prefrontal cortex during inferring others' beliefs compared with the typically developing participants (P < 0.001 and cluster size > 10 voxels). Then, to investigate potential effects of oxytocin on these behavioural and neural deficits, we conducted a double-blind placebo-controlled crossover within-subject trial for single-dose intranasal administration of 24 IU oxytocin in an independent group of 20 males with autism spectrum disorder. Behaviourally, oxytocin significantly increased the correct rate in inferring others' social emotions (P = 0.043, one-tail). At the neural level, the peptide significantly enhanced the originally-diminished brain activity in the right anterior insula during inferring others' social emotions (P = 0.004), but not in the dorsomedial prefrontal cortex during inferring others' beliefs (P = 0.858). The present findings suggest that oxytocin enhances the ability to understand others' social emotions that have also required second-order false belief rather than first-order false beliefs under conditions without direct emotional cues in autism spectrum disorder at both the behaviour and neural levels. © The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Reconstructing directed gene regulatory network by only gene expression data.

PubMed

Zhang, Lu; Feng, Xi Kang; Ng, Yen Kaow; Li, Shuai Cheng

2016-08-18

Accurately identifying gene regulatory network is an important task in understanding in vivo biological activities. The inference of such networks is often accomplished through the use of gene expression data. Many methods have been developed to evaluate gene expression dependencies between transcription factor and its target genes, and some methods also eliminate transitive interactions. The regulatory (or edge) direction is undetermined if the target gene is also a transcription factor. Some methods predict the regulatory directions in the gene regulatory networks by locating the eQTL single nucleotide polymorphism, or by observing the gene expression changes when knocking out/down the candidate transcript factors; regrettably, these additional data are usually unavailable, especially for the samples deriving from human tissues. In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are related to Alzheimer's disease; 2. ZNF329 and RB1 significantly regulate those 'mesenchymal' gene expression signature genes for brain tumors. By merely leveraging gene expression data, CBDN can efficiently infer the existence of gene-gene interactions as well as their regulatory directions. The constructed networks are helpful in the identification of important regulators for complex diseases.

Causal strength induction from time series data.

PubMed

Soo, Kevin W; Rottman, Benjamin M

2018-04-01

One challenge when inferring the strength of cause-effect relations from time series data is that the cause and/or effect can exhibit temporal trends. If temporal trends are not accounted for, a learner could infer that a causal relation exists when it does not, or even infer that there is a positive causal relation when the relation is negative, or vice versa. We propose that learners use a simple heuristic to control for temporal trends-that they focus not on the states of the cause and effect at a given instant, but on how the cause and effect change from one observation to the next, which we call transitions. Six experiments were conducted to understand how people infer causal strength from time series data. We found that participants indeed use transitions in addition to states, which helps them to reach more accurate causal judgments (Experiments 1A and 1B). Participants use transitions more when the stimuli are presented in a naturalistic visual format than a numerical format (Experiment 2), and the effect of transitions is not driven by primacy or recency effects (Experiment 3). Finally, we found that participants primarily use the direction in which variables change rather than the magnitude of the change for estimating causal strength (Experiments 4 and 5). Collectively, these studies provide evidence that people often use a simple yet effective heuristic for inferring causal strength from time series data. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Performance on a probabilistic inference task in healthy subjects receiving ketamine compared with patients with schizophrenia

PubMed Central

Almahdi, Basil; Sultan, Pervez; Sohanpal, Imrat; Brandner, Brigitta; Collier, Tracey; Shergill, Sukhi S; Cregg, Roman; Averbeck, Bruno B

2012-01-01

Evidence suggests that some aspects of schizophrenia can be induced in healthy volunteers through acute administration of the non-competitive NMDA-receptor antagonist, ketamine. In probabilistic inference tasks, patients with schizophrenia have been shown to ‘jump to conclusions’ (JTC) when asked to make a decision. We aimed to test whether healthy participants receiving ketamine would adopt a JTC response pattern resembling that of patients. The paradigmatic task used to investigate JTC has been the ‘urn’ task, where participants are shown a sequence of beads drawn from one of two ‘urns’, each containing coloured beads in different proportions. Participants make a decision when they think they know the urn from which beads are being drawn. We compared performance on the urn task between controls receiving acute ketamine or placebo with that of patients with schizophrenia and another group of controls matched to the patient group. Patients were shown to exhibit a JTC response pattern relative to their matched controls, whereas JTC was not evident in controls receiving ketamine relative to placebo. Ketamine does not appear to promote JTC in healthy controls, suggesting that ketamine does not affect probabilistic inferences. PMID:22389244

Inference generation and story comprehension among children with ADHD.

PubMed

Van Neste, Jessica; Hayden, Angela; Lorch, Elizabeth P; Milich, Richard

2015-02-01

Academic difficulties are well-documented among children with ADHD. Exploring these difficulties through story comprehension research has revealed deficits among children with ADHD in making causal connections between events and in using causal structure and thematic importance to guide recall of stories. Important to theories of story comprehension and implied in these deficits is the ability to make inferences. Often, characters' goals are implicit and explanations of events must be inferred. The purpose of the present study was to compare the inferences generated during story comprehension by 23 7- to 11-year-old children with ADHD (16 males) and 35 comparison peers (19 males). Children watched two televised stories, each paused at five points. In the experimental condition, at each pause children told what they were thinking about the story, whereas in the control condition no responses were made during pauses. After viewing, children recalled the story. Several types of inferences and inference plausibility were coded. Children with ADHD generated fewer of the most essential inferences, plausible explanatory inferences, than did comparison children, both during story processing and during story recall. The groups did not differ on production of other types of inferences. Group differences in generating inferences during the think-aloud task significantly mediated group differences in patterns of recall. Both groups recalled more of the most important story information after completing the think-aloud task. Generating fewer explanatory inferences has important implications for story comprehension deficits in children with ADHD.

Optimal inference with suboptimal models: Addiction and active Bayesian inference

PubMed Central

Schwartenbeck, Philipp; FitzGerald, Thomas H.B.; Mathys, Christoph; Dolan, Ray; Wurst, Friedrich; Kronbichler, Martin; Friston, Karl

2015-01-01

When casting behaviour as active (Bayesian) inference, optimal inference is defined with respect to an agent’s beliefs – based on its generative model of the world. This contrasts with normative accounts of choice behaviour, in which optimal actions are considered in relation to the true structure of the environment – as opposed to the agent’s beliefs about worldly states (or the task). This distinction shifts an understanding of suboptimal or pathological behaviour away from aberrant inference as such, to understanding the prior beliefs of a subject that cause them to behave less ‘optimally’ than our prior beliefs suggest they should behave. Put simply, suboptimal or pathological behaviour does not speak against understanding behaviour in terms of (Bayes optimal) inference, but rather calls for a more refined understanding of the subject’s generative model upon which their (optimal) Bayesian inference is based. Here, we discuss this fundamental distinction and its implications for understanding optimality, bounded rationality and pathological (choice) behaviour. We illustrate our argument using addictive choice behaviour in a recently described ‘limited offer’ task. Our simulations of pathological choices and addictive behaviour also generate some clear hypotheses, which we hope to pursue in ongoing empirical work. PMID:25561321

Effective Bayesian Transfer Learning

DTIC Science & Technology

2010-03-01

reasonable value of k , defined by the task B training set size. Transfer Regret 1 Regret = 100 * G AB B No Transfer With Transfer AB...a. REPORT U b . ABSTRACT U c. THIS PAGE U 19b. TELEPHONE NUMBER (Include area code) N/A Standard Form 298 (Rev. 8-98) Prescribed...rule set given the prior and developed staged approximate inference strategy, in which data from observed tasks 1 to k are used to infer general rule

An Excel sheet for inferring children's number-knower levels from give-N data.

PubMed

Negen, James; Sarnecka, Barbara W; Lee, Michael D

2012-03-01

Number-knower levels are a series of stages of number concept development in early childhood. A child's number-knower level is typically assessed using the give-N task. Although the task procedure has been highly refined, the standard ways of analyzing give-N data remain somewhat crude. Lee and Sarnecka (Cogn Sci 34:51-67, 2010, in press) have developed a Bayesian model of children's performance on the give-N task that allows knower level to be inferred in a more principled way. However, this model requires considerable expertise and computational effort to implement and apply to data. Here, we present an approximation to the model's inference that can be computed with Microsoft Excel. We demonstrate the accuracy of the approximation and provide instructions for its use. This makes the powerful inferential capabilities of the Bayesian model accessible to developmental researchers interested in estimating knower levels from give-N data.

Computational Natural Language Inference: Robust and Interpretable Question Answering

ERIC Educational Resources Information Center

Sharp, Rebecca Reynolds

2017-01-01

We address the challenging task of "computational natural language inference," by which we mean bridging two or more natural language texts while also providing an explanation of how they are connected. In the context of question answering (i.e., finding short answers to natural language questions), this inference connects the question…

The hippocampus and memory for orderly stimulus relations

PubMed Central

Dusek, Jeffery A.; Eichenbaum, Howard

1997-01-01

Human declarative memory involves a systematic organization of information that supports generalizations and inferences from acquired knowledge. This kind of memory depends on the hippocampal region in humans, but the extent to which animals also have declarative memory, and whether inferential expression of memory depends on the hippocampus in animals, remains a major challenge in cognitive neuroscience. To examine these issues, we used a test of transitive inference pioneered by Piaget to assess capacities for systematic organization of knowledge and logical inference in children. In our adaptation of the test, rats were trained on a set of four overlapping odor discrimination problems that could be encoded either separately or as a single representation of orderly relations among the odor stimuli. Normal rats learned the problems and demonstrated the relational memory organization through appropriate transitive inferences about items not presented together during training. By contrast, after disconnection of the hippocampus from either its cortical or subcortical pathway, rats succeeded in acquiring the separate discrimination problems but did not demonstrate transitive inference, indicating that they had failed to develop or could not inferentially express the orderly organization of the stimulus elements. These findings strongly support the view that the hippocampus mediates a general declarative memory capacity in animals, as it does in humans. PMID:9192700

Adult Age Differences in Categorization and Multiple-Cue Judgment

ERIC Educational Resources Information Center

Mata, Rui; von Helversen, Bettina; Karlsson, Linnea; Cupper, Lutz

2012-01-01

We often need to infer unknown properties of objects from observable ones, just like detectives must infer guilt from observable clues and behavior. But how do inferential processes change with age? We examined young and older adults' reliance on rule-based and similarity-based processes in an inference task that can be considered either a…

Task constraints distinguish perspective inferences from perspective use during discourse interpretation in a false belief task.

PubMed

Ferguson, Heather J; Apperly, Ian; Ahmad, Jumana; Bindemann, Markus; Cane, James

2015-06-01

Interpreting other peoples' actions relies on an understanding of their current mental states (e.g. beliefs, desires and intentions). In this paper, we distinguish between listeners' ability to infer others' perspectives and their explicit use of this knowledge to predict subsequent actions. In a visual-world study, two groups of participants (passive observers vs. active participants) watched short videos, depicting transfer events, where one character ('Jane') either held a true or false belief about an object's location. We tracked participants' eye-movements around the final visual scene, time-locked to related auditory descriptions (e.g. "Jane will look for the chocolates in the container on the left".). Results showed that active participants had already inferred the character's belief in the 1s preview period prior to auditory onset, before it was possible to use this information to predict an outcome. Moreover, they used this inference to correctly anticipate reference to the object's initial location on false belief trials at the earliest possible point (i.e. from "Jane" onwards). In contrast, passive observers only showed evidence of a belief inference from the onset of "Jane", and did not show reliable use of this inference to predict Jane's behaviour on false belief trials until much later, when the location ("left/right") was auditorily available. These results show that active engagement in a task activates earlier inferences about others' perspectives, and drives immediate use of this information to anticipate others' actions, compared to passive observers, who are susceptible to influences from egocentric or reality biases. Finally, we review evidence that using other peoples' perspectives to predict their behaviour is more cognitively effortful than simply using one's own. Copyright © 2015 Elsevier B.V. All rights reserved.

Inferring common cognitive mechanisms from brain blood-flow lateralization data: a new methodology for fTCD analysis.

PubMed

Meyer, Georg F; Spray, Amy; Fairlie, Jo E; Uomini, Natalie T

2014-01-01

Current neuroimaging techniques with high spatial resolution constrain participant motion so that many natural tasks cannot be carried out. The aim of this paper is to show how a time-locked correlation-analysis of cerebral blood flow velocity (CBFV) lateralization data, obtained with functional TransCranial Doppler (fTCD) ultrasound, can be used to infer cerebral activation patterns across tasks. In a first experiment we demonstrate that the proposed analysis method results in data that are comparable with the standard Lateralization Index (LI) for within-task comparisons of CBFV patterns, recorded during cued word generation (CWG) at two difficulty levels. In the main experiment we demonstrate that the proposed analysis method shows correlated blood-flow patterns for two different cognitive tasks that are known to draw on common brain areas, CWG, and Music Synthesis. We show that CBFV patterns for Music and CWG are correlated only for participants with prior musical training. CBFV patterns for tasks that draw on distinct brain areas, the Tower of London and CWG, are not correlated. The proposed methodology extends conventional fTCD analysis by including temporal information in the analysis of cerebral blood-flow patterns to provide a robust, non-invasive method to infer whether common brain areas are used in different cognitive tasks. It complements conventional high resolution imaging techniques.

Spontaneous evaluative inferences and their relationship to spontaneous trait inferences.

PubMed

Schneid, Erica D; Carlston, Donal E; Skowronski, John J

2015-05-01

Three experiments are reported that explore affectively based spontaneous evaluative impressions (SEIs) of stimulus persons. Experiments 1 and 2 used modified versions of the savings in relearning paradigm (Carlston & Skowronski, 1994) to confirm the occurrence of SEIs, indicating that they are equivalent whether participants are instructed to form trait impressions, evaluative impressions, or neither. These experiments also show that SEIs occur independently of explicit recall for the trait implications of the stimuli. Experiment 3 provides a single dissociation test to distinguish SEIs from spontaneous trait inferences (STIs), showing that disrupting cognitive processing interferes with a trait-based prediction task that presumably reflects STIs, but not with an affectively based social approach task that presumably reflects SEIs. Implications of these findings for the potential independence of spontaneous trait and evaluative inferences, as well as limitations and important steps for future study are discussed. (c) 2015 APA, all rights reserved).

Reading Rate, Readability, and Variations in Task-Induced Processing

ERIC Educational Resources Information Center

Coke, Esther U.

1976-01-01

This study explored the hypothesis that task variables account for previous findings that reading rate is unaffected by readability. The findings suggest that when appropriate reading tasks are chosen, reading rate can be used to infer underlying processes in reading. (Author/DEP)

Toward an ecological analysis of Bayesian inferences: how task characteristics influence responses

PubMed Central

Hafenbrädl, Sebastian; Hoffrage, Ulrich

2015-01-01

In research on Bayesian inferences, the specific tasks, with their narratives and characteristics, are typically seen as exchangeable vehicles that merely transport the structure of the problem to research participants. In the present paper, we explore whether, and possibly how, task characteristics that are usually ignored influence participants’ responses in these tasks. We focus on both quantitative dimensions of the tasks, such as their base rates, hit rates, and false-alarm rates, as well as qualitative characteristics, such as whether the task involves a norm violation or not, whether the stakes are high or low, and whether the focus is on the individual case or on the numbers. Using a data set of 19 different tasks presented to 500 different participants who provided a total of 1,773 responses, we analyze these responses in two ways: first, on the level of the numerical estimates themselves, and second, on the level of various response strategies, Bayesian and non-Bayesian, that might have produced the estimates. We identified various contingencies, and most of the task characteristics had an influence on participants’ responses. Typically, this influence has been stronger when the numerical information in the tasks was presented in terms of probabilities or percentages, compared to natural frequencies – and this effect cannot be fully explained by a higher proportion of Bayesian responses when natural frequencies were used. One characteristic that did not seem to influence participants’ response strategy was the numerical value of the Bayesian solution itself. Our exploratory study is a first step toward an ecological analysis of Bayesian inferences, and highlights new avenues for future research. PMID:26300791

Reveal, A General Reverse Engineering Algorithm for Inference of Genetic Network Architectures

NASA Technical Reports Server (NTRS)

Liang, Shoudan; Fuhrman, Stefanie; Somogyi, Roland

1998-01-01

Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(exp 15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.

Birth/birth-death processes and their computable transition probabilities with biological applications.

PubMed

Ho, Lam Si Tung; Xu, Jason; Crawford, Forrest W; Minin, Vladimir N; Suchard, Marc A

2018-03-01

Birth-death processes track the size of a univariate population, but many biological systems involve interaction between populations, necessitating models for two or more populations simultaneously. A lack of efficient methods for evaluating finite-time transition probabilities of bivariate processes, however, has restricted statistical inference in these models. Researchers rely on computationally expensive methods such as matrix exponentiation or Monte Carlo approximation, restricting likelihood-based inference to small systems, or indirect methods such as approximate Bayesian computation. In this paper, we introduce the birth/birth-death process, a tractable bivariate extension of the birth-death process, where rates are allowed to be nonlinear. We develop an efficient algorithm to calculate its transition probabilities using a continued fraction representation of their Laplace transforms. Next, we identify several exemplary models arising in molecular epidemiology, macro-parasite evolution, and infectious disease modeling that fall within this class, and demonstrate advantages of our proposed method over existing approaches to inference in these models. Notably, the ubiquitous stochastic susceptible-infectious-removed (SIR) model falls within this class, and we emphasize that computable transition probabilities newly enable direct inference of parameters in the SIR model. We also propose a very fast method for approximating the transition probabilities under the SIR model via a novel branching process simplification, and compare it to the continued fraction representation method with application to the 17th century plague in Eyam. Although the two methods produce similar maximum a posteriori estimates, the branching process approximation fails to capture the correlation structure in the joint posterior distribution.

How Mood and Task Complexity Affect Children's Recognition of Others’ Emotions

PubMed Central

Cummings, Andrew J.; Rennels, Jennifer L.

2013-01-01

Previous studies examined how mood affects children's accuracy in matching emotional expressions and labels (label-based tasks). This study was the first to assess how induced mood (positive, neutral, or negative) influenced 5- to 8-year-olds’ accuracy and reaction time using both context-based tasks, which required inferring a character's emotion from a vignette, and label-based tasks. Both tasks required choosing one of four facial expressions to respond. Children responded more accurately to label-based questions relative to context-based questions at 5 to 7 years of age, but showed no differences at 8 years of age, and when the emotional expression being identified was happiness, sadness, or surprise, but not disgust. For the context-based questions, children were more accurate at inferring sad and disgusted emotions compared to happy and surprised emotions. Induced positive mood facilitated 5-year-olds’ processing (decreased reaction time) in both tasks compared to induced negative and neutral moods. Results demonstrate how task type and children's mood influence children's emotion processing at different ages. PMID:24489442

Probabilistic learning and inference in schizophrenia

PubMed Central

Averbeck, Bruno B.; Evans, Simon; Chouhan, Viraj; Bristow, Eleanor; Shergill, Sukhwinder S.

2010-01-01

Patients with schizophrenia make decisions on the basis of less evidence when required to collect information to make an inference, a behavior often called jumping to conclusions. The underlying basis for this behaviour remains controversial. We examined the cognitive processes underpinning this finding by testing subjects on the beads task, which has been used previously to elicit jumping to conclusions behaviour, and a stochastic sequence learning task, with a similar decision theoretic structure. During the sequence learning task, subjects had to learn a sequence of button presses, while receiving noisy feedback on their choices. We fit a Bayesian decision making model to the sequence task and compared model parameters to the choice behavior in the beads task in both patients and healthy subjects. We found that patients did show a jumping to conclusions style; and those who picked early in the beads task tended to learn less from positive feedback in the sequence task. This favours the likelihood of patients selecting early because they have a low threshold for making decisions, and that they make choices on the basis of relatively little evidence. PMID:20810252

Probabilistic learning and inference in schizophrenia.

PubMed

Averbeck, Bruno B; Evans, Simon; Chouhan, Viraj; Bristow, Eleanor; Shergill, Sukhwinder S

2011-04-01

Patients with schizophrenia make decisions on the basis of less evidence when required to collect information to make an inference, a behavior often called jumping to conclusions. The underlying basis for this behavior remains controversial. We examined the cognitive processes underpinning this finding by testing subjects on the beads task, which has been used previously to elicit jumping to conclusions behavior, and a stochastic sequence learning task, with a similar decision theoretic structure. During the sequence learning task, subjects had to learn a sequence of button presses, while receiving a noisy feedback on their choices. We fit a Bayesian decision making model to the sequence task and compared model parameters to the choice behavior in the beads task in both patients and healthy subjects. We found that patients did show a jumping to conclusions style; and those who picked early in the beads task tended to learn less from positive feedback in the sequence task. This favours the likelihood of patients selecting early because they have a low threshold for making decisions, and that they make choices on the basis of relatively little evidence. Published by Elsevier B.V.

Flexibility to contingency changes distinguishes habitual and goal-directed strategies in humans

PubMed Central

Keramati, Mehdi

2017-01-01

Decision-making in the real world presents the challenge of requiring flexible yet prompt behavior, a balance that has been characterized in terms of a trade-off between a slower, prospective goal-directed model-based (MB) strategy and a fast, retrospective habitual model-free (MF) strategy. Theory predicts that flexibility to changes in both reward values and transition contingencies can determine the relative influence of the two systems in reinforcement learning, but few studies have manipulated the latter. Therefore, we developed a novel two-level contingency change task in which transition contingencies between states change every few trials; MB and MF control predict different responses following these contingency changes, allowing their relative influence to be inferred. Additionally, we manipulated the rate of contingency changes in order to determine whether contingency change volatility would play a role in shifting subjects between a MB and MF strategy. We found that human subjects employed a hybrid MB/MF strategy on the task, corroborating the parallel contribution of MB and MF systems in reinforcement learning. Further, subjects did not remain at one level of MB/MF behaviour but rather displayed a shift towards more MB behavior over the first two blocks that was not attributable to the rate of contingency changes but rather to the extent of training. We demonstrate that flexibility to contingency changes can distinguish MB and MF strategies, with human subjects utilizing a hybrid strategy that shifts towards more MB behavior over blocks, consequently corresponding to a higher payoff. PMID:28957319

Flexibility to contingency changes distinguishes habitual and goal-directed strategies in humans.

PubMed

Lee, Julie J; Keramati, Mehdi

2017-09-01

Decision-making in the real world presents the challenge of requiring flexible yet prompt behavior, a balance that has been characterized in terms of a trade-off between a slower, prospective goal-directed model-based (MB) strategy and a fast, retrospective habitual model-free (MF) strategy. Theory predicts that flexibility to changes in both reward values and transition contingencies can determine the relative influence of the two systems in reinforcement learning, but few studies have manipulated the latter. Therefore, we developed a novel two-level contingency change task in which transition contingencies between states change every few trials; MB and MF control predict different responses following these contingency changes, allowing their relative influence to be inferred. Additionally, we manipulated the rate of contingency changes in order to determine whether contingency change volatility would play a role in shifting subjects between a MB and MF strategy. We found that human subjects employed a hybrid MB/MF strategy on the task, corroborating the parallel contribution of MB and MF systems in reinforcement learning. Further, subjects did not remain at one level of MB/MF behaviour but rather displayed a shift towards more MB behavior over the first two blocks that was not attributable to the rate of contingency changes but rather to the extent of training. We demonstrate that flexibility to contingency changes can distinguish MB and MF strategies, with human subjects utilizing a hybrid strategy that shifts towards more MB behavior over blocks, consequently corresponding to a higher payoff.

Improvements and important considerations for the 5-choice serial reaction time task-An effective measurement of visual attention in rats.

PubMed

Bhandari, Jayant; Daya, Ritesh; Mishra, Ram K

2016-09-01

The 5-choice serial reaction time task (5-CSRTT) is an automated operant conditioning task that measures rodent attention. The task allows the measurement of several parameters such as response accuracy, speed of processing, motivation, and impulsivity. The task has been widely used to investigate attentional processes in rodents for attention deficit and hyperactivity disorder and has expanded to other illnesses such as Alzheimer's disease, depression, and schizophrenia. The 5-CSRTT is accompanied with two significant caveats: a time intensive training period and largely varied individual rat capability to learn and perform the task. Here we provide a regimented acquisition protocol to enhance training for the 5-CSRTT and discuss important considerations for researchers using the 5-CSRTT. We offer guidelines to ensure that inferences on performance in the 5-CSRTT are in fact a result of experimental manipulation rather than training differences, or individual animal capability. According to our findings only rats that have been trained successfully within a limited time frame should be used for the remainder of the study. Currently the 5-CSRTT employs a training period of variable duration and procedure, and its inferences on attention must overcome heterogeneous innate animal differences. The 5-CSRTT offers valuable and valid insights on various rodent attentional processes and their translation to the underpinnings of illnesses such as schizophrenia. The recommendations made here provide important criteria to ensure inferences made from this task are in fact relevant to the attentional processes being measured. Copyright © 2016 Elsevier B.V. All rights reserved.

77 FR 68744 - Department of Defense Task Force on the Care, Management, and Transition of Recovering Wounded...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-11-16

... DEPARTMENT OF DEFENSE Office of the Secretary Department of Defense Task Force on the Care, Management, and Transition of Recovering Wounded, Ill, and Injured Members of the Armed Forces; Federal... Committee meeting of the Department of Defense Task Force on the Care, Management, and Transition of...

Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses

PubMed Central

Stephen, Emily P.; Lepage, Kyle Q.; Eden, Uri T.; Brunner, Peter; Schalk, Gerwin; Brumberg, Jonathan S.; Guenther, Frank H.; Kramer, Mark A.

2014-01-01

The brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty—both in the functional network edges and the corresponding aggregate measures of network topology—are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here—appropriate for static and dynamic network inference and different statistical measures of coupling—permits the evaluation of confidence in network measures in a variety of settings common to neuroscience. PMID:24678295

Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses.

PubMed

Stephen, Emily P; Lepage, Kyle Q; Eden, Uri T; Brunner, Peter; Schalk, Gerwin; Brumberg, Jonathan S; Guenther, Frank H; Kramer, Mark A

2014-01-01

The brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty-both in the functional network edges and the corresponding aggregate measures of network topology-are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here-appropriate for static and dynamic network inference and different statistical measures of coupling-permits the evaluation of confidence in network measures in a variety of settings common to neuroscience.

Online inferential and textual processing by adolescents with attention-deficit/hyperactivity disorder during reading comprehension: Evidence from a probing method.

PubMed

Yeari, Menahem; Avramovich, Adi; Schiff, Rachel

2017-06-01

Previous studies have demonstrated that students with attention-deficit/hyperactivity disorder (ADHD) struggle particularly with grasping the implicit, inferential level of narratives that is crucial for story comprehension. However, these studies used offline tasks (i.e., after story presentation), used indirect measurements (e.g., identifying main ideas), and/or yielded inconclusive results using think-aloud techniques. Moreover, most studies were conducted with preschool or elementary school children with ADHD, using listening or televised story comprehension. In this study, we were interested in examining the spontaneous, immediate activation and/or suppression of forward-predictive inferences, backward-explanatory inferences, and inference-evoking textual information, as they occur online during reading comprehension by adolescents with ADHD. Participants with and without ADHD read short narrative texts, each of which included a predictive sentence, a bridging sentence that referred back to the predictive sentence via actualization of the predicted event, and two intervening sentences positioned between the predictive and bridging sentences that introduced a temporary transition from the main (predictive) episode. Activation and suppression of inferential and/or textual information were assessed using naming times of word probes that were implied by the preceding text, explicitly mentioned in it, or neither when following control texts. In some cases, a true-false inferential or textual question followed the probe. Naming facilitations were observed for the control but not for the ADHD group, in responding to inference probes that followed the predictive and bridging sentences, and to text probes that followed the predictive sentences. Participants with ADHD were accurate, albeit slower, than controls in answering the true-false questions. Adolescents with ADHD have difficulties in generating predictive and explanatory inferences and in retaining relevant textual information in working memory while reading, although they can answer questions after reading when texts are relatively short. These findings are discussed with regard to development of comprehension strategies for individuals with ADHD.

Sequential Effects in Deduction: Cost of Inference Switch

ERIC Educational Resources Information Center

Milan, Emilio G.; Moreno-Rios, Sergio; Espino, Orlando; Santamaria, Carlos; Gonzalez-Hernandez, Antonio

2010-01-01

The task-switch paradigm has helped psychologists gain insight into the processes involved in changing from one activity to another. The literature has yielded consistent results about switch cost reconfiguration (abrupt offset in regular task-switch vs. gradual reduction in random task-switch; endogenous and exogenous components of switch cost;…

The gene normalization task in BioCreative III

PubMed Central

2011-01-01

Background We report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of the genes detected in full-text articles. For training, 32 fully and 500 partially annotated articles were prepared. A total of 507 articles were selected as the test set. Due to the high annotation cost, it was not feasible to obtain gold-standard human annotations for all test articles. Instead, we developed an Expectation Maximization (EM) algorithm approach for choosing a small number of test articles for manual annotation that were most capable of differentiating team performance. Moreover, the same algorithm was subsequently used for inferring ground truth based solely on team submissions. We report team performance on both gold standard and inferred ground truth using a newly proposed metric called Threshold Average Precision (TAP-k). Results We received a total of 37 runs from 14 different teams for the task. When evaluated using the gold-standard annotations of the 50 articles, the highest TAP-k scores were 0.3297 (k=5), 0.3538 (k=10), and 0.3535 (k=20), respectively. Higher TAP-k scores of 0.4916 (k=5, 10, 20) were observed when evaluated using the inferred ground truth over the full test set. When combining team results using machine learning, the best composite system achieved TAP-k scores of 0.3707 (k=5), 0.4311 (k=10), and 0.4477 (k=20) on the gold standard, representing improvements of 12.4%, 21.8%, and 26.6% over the best team results, respectively. Conclusions By using full text and being species non-specific, the GN task in BioCreative III has moved closer to a real literature curation task than similar tasks in the past and presents additional challenges for the text mining community, as revealed in the overall team results. By evaluating teams using the gold standard, we show that the EM algorithm allows team submissions to be differentiated while keeping the manual annotation effort feasible. Using the inferred ground truth we show measures of comparative performance between teams. Finally, by comparing team rankings on gold standard vs. inferred ground truth, we further demonstrate that the inferred ground truth is as effective as the gold standard for detecting good team performance. PMID:22151901

No interpretation without representation: the role of domain-specific representations and inferences in the Wason selection task.

PubMed

Fiddick, L; Cosmides, L; Tooby, J

2000-10-16

The Wason selection task is a tool used to study reasoning about conditional rules. Performance on this task changes systematically when one varies its content, and these content effects have been used to argue that the human cognitive architecture contains a number of domain-specific representation and inference systems, such as social contract algorithms and hazard management systems. Recently, however, Sperber, Cara & Girotto (Sperber, D., Cara, F., & Girotto, V. (1995). Relevance theory explains the selection task. Cognition, 57, 31-95) have proposed that relevance theory can explain performance on the selection task - including all content effects - without invoking inference systems that are content-specialized. Herein, we show that relevance theory alone cannot explain a variety of content effects - effects that were predicted in advance and are parsimoniously explained by theories that invoke domain-specific algorithms for representing and making inferences about (i) social contracts and (ii) reducing risk in hazardous situations. Moreover, although Sperber et al. (1995) were able to use relevance theory to produce some new content effects in other domains, they conducted no experiments involving social exchanges or precautions, and so were unable to determine which - content-specialized algorithms or relevance effects - dominate reasoning when the two conflict. When experiments, reported herein, are constructed so that the different theories predict divergent outcomes, the results support the predictions of social contract theory and hazard management theory, indicating that these inference systems override content-general relevance factors. The fact that social contract and hazard management algorithms provide better explanations for performance in their respective domains does not mean that the content-general logical procedures posited by relevance theory do not exist, or that relevance effects never occur. It does mean, however, that one needs a principled way of explaining which effects will dominate when a set of inputs activate more than one reasoning system. We propose the principle of pre-emptive specificity - that the human cognitive architecture should be designed so that more specialized inference systems pre-empt more general ones whenever the stimuli centrally fit the input conditions of the more specialized system. This principle follows from evolutionary and computational considerations that are common to both relevance theory and the ecological rationality approach.

The gene normalization task in BioCreative III.

PubMed

Lu, Zhiyong; Kao, Hung-Yu; Wei, Chih-Hsuan; Huang, Minlie; Liu, Jingchen; Kuo, Cheng-Ju; Hsu, Chun-Nan; Tsai, Richard Tzong-Han; Dai, Hong-Jie; Okazaki, Naoaki; Cho, Han-Cheol; Gerner, Martin; Solt, Illes; Agarwal, Shashank; Liu, Feifan; Vishnyakova, Dina; Ruch, Patrick; Romacker, Martin; Rinaldi, Fabio; Bhattacharya, Sanmitra; Srinivasan, Padmini; Liu, Hongfang; Torii, Manabu; Matos, Sergio; Campos, David; Verspoor, Karin; Livingston, Kevin M; Wilbur, W John

2011-10-03

We report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of the genes detected in full-text articles. For training, 32 fully and 500 partially annotated articles were prepared. A total of 507 articles were selected as the test set. Due to the high annotation cost, it was not feasible to obtain gold-standard human annotations for all test articles. Instead, we developed an Expectation Maximization (EM) algorithm approach for choosing a small number of test articles for manual annotation that were most capable of differentiating team performance. Moreover, the same algorithm was subsequently used for inferring ground truth based solely on team submissions. We report team performance on both gold standard and inferred ground truth using a newly proposed metric called Threshold Average Precision (TAP-k). We received a total of 37 runs from 14 different teams for the task. When evaluated using the gold-standard annotations of the 50 articles, the highest TAP-k scores were 0.3297 (k=5), 0.3538 (k=10), and 0.3535 (k=20), respectively. Higher TAP-k scores of 0.4916 (k=5, 10, 20) were observed when evaluated using the inferred ground truth over the full test set. When combining team results using machine learning, the best composite system achieved TAP-k scores of 0.3707 (k=5), 0.4311 (k=10), and 0.4477 (k=20) on the gold standard, representing improvements of 12.4%, 21.8%, and 26.6% over the best team results, respectively. By using full text and being species non-specific, the GN task in BioCreative III has moved closer to a real literature curation task than similar tasks in the past and presents additional challenges for the text mining community, as revealed in the overall team results. By evaluating teams using the gold standard, we show that the EM algorithm allows team submissions to be differentiated while keeping the manual annotation effort feasible. Using the inferred ground truth we show measures of comparative performance between teams. Finally, by comparing team rankings on gold standard vs. inferred ground truth, we further demonstrate that the inferred ground truth is as effective as the gold standard for detecting good team performance.

78 FR 7415 - Department of Defense Task Force on the Care, Management, and Transition of Recovering Wounded...

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2013-02-01

... DEPARTMENT OF DEFENSE Office of the Secretary Department of Defense Task Force on the Care, Management, and Transition of Recovering Wounded, Ill, and Injured Members of the Armed Forces; Notice of... Committee meeting of the Department of Defense Task Force on the Care, Management, and Transition of...

77 FR 31337 - Department of Defense Task Force on the Care, Management, and Transition of Recovering Wounded...

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... DEPARTMENT OF DEFENSE Office of the Secretary Department of Defense Task Force on the Care, Management, and Transition of Recovering Wounded, Ill, and Injured Members of the Armed Forces; Notice of... Department of Defense Task Force on the Care, Management, and Transition of Recovering Wounded, Ill, and...

78 FR 38015 - Department of Defense Task Force on the Care, Management, and Transition of Recovering Wounded...

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2013-06-25

... DEPARTMENT OF DEFENSE Office of the Secretary Department of Defense Task Force on the Care, Management, and Transition of Recovering Wounded, Ill, and Injured Members of the Armed Forces; Notice of... of the Department of Defense Task Force on the Care, Management, and Transition of Recovering Wounded...

A Problem-Solving Intervention Using iPads to Improve Transition-Related Task Performance of Students with Autism Spectrum Disorder

ERIC Educational Resources Information Center

Yakubova, Gulnoza; Zeleke, Waganesh A.

2016-01-01

In this study, the effectiveness of teaching problem-solving to improve transition-related task performance of three students with autism spectrum disorder (ASD) was examined using a multiple probe across students design. Target behaviors included various transition-related tasks individualized for each student based on their individual…

Understanding neuromotor strategy during functional upper extremity tasks using symbolic dynamics.

PubMed

Nathan, Dominic E; Guastello, Stephen J; Prost, Robert W; Jeutter, Dean C

2012-01-01

The ability to model and quantify brain activation patterns that pertain to natural neuromotor strategy of the upper extremities during functional task performance is critical to the development of therapeutic interventions such as neuroprosthetic devices. The mechanisms of information flow, activation sequence and patterns, and the interaction between anatomical regions of the brain that are specific to movement planning, intention and execution of voluntary upper extremity motor tasks were investigated here. This paper presents a novel method using symbolic dynamics (orbital decomposition) and nonlinear dynamic tools of entropy, self-organization and chaos to describe the underlying structure of activation shifts in regions of the brain that are involved with the cognitive aspects of functional upper extremity task performance. Several questions were addressed: (a) How is it possible to distinguish deterministic or causal patterns of activity in brain fMRI from those that are really random or non-contributory to the neuromotor control process? (b) Can the complexity of activation patterns over time be quantified? (c) What are the optimal ways of organizing fMRI data to preserve patterns of activation, activation levels, and extract meaningful temporal patterns as they evolve over time? Analysis was performed using data from a custom developed time resolved fMRI paradigm involving human subjects (N=18) who performed functional upper extremity motor tasks with varying time delays between the onset of intention and onset of actual movements. The results indicate that there is structure in the data that can be quantified through entropy and dimensional complexity metrics and statistical inference, and furthermore, orbital decomposition is sensitive in capturing the transition of states that correlate with the cognitive aspects of functional task performance.

The impact of category structure and training methodology on learning and generalizing within-category representations.

PubMed

Ell, Shawn W; Smith, David B; Peralta, Gabriela; Hélie, Sébastien

2017-08-01

When interacting with categories, representations focused on within-category relationships are often learned, but the conditions promoting within-category representations and their generalizability are unclear. We report the results of three experiments investigating the impact of category structure and training methodology on the learning and generalization of within-category representations (i.e., correlational structure). Participants were trained on either rule-based or information-integration structures using classification (Is the stimulus a member of Category A or Category B?), concept (e.g., Is the stimulus a member of Category A, Yes or No?), or inference (infer the missing component of the stimulus from a given category) and then tested on either an inference task (Experiments 1 and 2) or a classification task (Experiment 3). For the information-integration structure, within-category representations were consistently learned, could be generalized to novel stimuli, and could be generalized to support inference at test. For the rule-based structure, extended inference training resulted in generalization to novel stimuli (Experiment 2) and inference training resulted in generalization to classification (Experiment 3). These data help to clarify the conditions under which within-category representations can be learned. Moreover, these results make an important contribution in highlighting the impact of category structure and training methodology on the generalization of categorical knowledge.

scEpath: Energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data.

PubMed

Jin, Suoqin; MacLean, Adam L; Peng, Tao; Nie, Qing

2018-02-05

Single-cell RNA-sequencing (scRNA-seq) offers unprecedented resolution for studying cellular decision-making processes. Robust inference of cell state transition paths and probabilities is an important yet challenging step in the analysis of these data. Here we present scEpath, an algorithm that calculates energy landscapes and probabilistic directed graphs in order to reconstruct developmental trajectories. We quantify the energy landscape using "single-cell energy" and distance-based measures, and find that the combination of these enables robust inference of the transition probabilities and lineage relationships between cell states. We also identify marker genes and gene expression patterns associated with cell state transitions. Our approach produces pseudotemporal orderings that are - in combination - more robust and accurate than current methods, and offers higher resolution dynamics of the cell state transitions, leading to new insight into key transition events during differentiation and development. Moreover, scEpath is robust to variation in the size of the input gene set, and is broadly unsupervised, requiring few parameters to be set by the user. Applications of scEpath led to the identification of a cell-cell communication network implicated in early human embryo development, and novel transcription factors important for myoblast differentiation. scEpath allows us to identify common and specific temporal dynamics and transcriptional factor programs along branched lineages, as well as the transition probabilities that control cell fates. A MATLAB package of scEpath is available at https://github.com/sqjin/scEpath. qnie@uci.edu. Supplementary data are available at Bioinformatics online. © The Author(s) 2018. Published by Oxford University Press.

More than one kind of inference: re-examining what's learned in feature inference and classification.

PubMed

Sweller, Naomi; Hayes, Brett K

2010-08-01

Three studies examined how task demands that impact on attention to typical or atypical category features shape the category representations formed through classification learning and inference learning. During training categories were learned via exemplar classification or by inferring missing exemplar features. In the latter condition inferences were made about missing typical features alone (typical feature inference) or about both missing typical and atypical features (mixed feature inference). Classification and mixed feature inference led to the incorporation of typical and atypical features into category representations, with both kinds of features influencing inferences about familiar (Experiments 1 and 2) and novel (Experiment 3) test items. Those in the typical inference condition focused primarily on typical features. Together with formal modelling, these results challenge previous accounts that have characterized inference learning as producing a focus on typical category features. The results show that two different kinds of inference learning are possible and that these are subserved by different kinds of category representations.

The role of the hippocampus in transitive inference

PubMed Central

Zalesak, Martin; Heckers, Stephan

2009-01-01

Transitive inference (TI) is the ability to infer the relationship between items (e.g., A>C) after having learned a set of premise pairs (e.g., A>B and B>C). Previous studies in humans have identified a distributed neural network, including cortex, hippocampus, and thalamus, during TI judgments. We studied two aspects of TI using fMRI of subjects who had acquired the 6-item sequence (A>B>C>D>E>F) of visual stimuli. First, the identification of novel pairs not containing end items (i.e., B>D, C>E, B>E) was associated with greater left hippocampal activation when compared to the identification of novel pairs containing end items A and F. This demonstrates that the identification of stimulus pairs requiring the flexible representation of a sequence is associated with hippocampal activation. Second, for the three novel pairs devoid of end items we found greater right hippocampal activation for pairs B>D and C>E compared with pair B>E. This indicates that TI decisions on pairs derived from more adjacent items in the sequence are associated with greater hippocampal activation. Hippocampal activation thus scales with the degree of relational processing necessary for TI judgments. Both findings confirm a role of the hippocampus in transitive inference in humans. PMID:19216061

Learning and Retention through Predictive Inference and Classification

ERIC Educational Resources Information Center

Sakamoto, Yasuaki; Love, Bradley C.

2010-01-01

Work in category learning addresses how humans acquire knowledge and, thus, should inform classroom practices. In two experiments, we apply and evaluate intuitions garnered from laboratory-based research in category learning to learning tasks situated in an educational context. In Experiment 1, learning through predictive inference and…

Workload and Performance in Air Traffic Control: Exploring the Influence of Levels of Automation and Variation in Task Demand

NASA Technical Reports Server (NTRS)

Edwards, Tamsyn El; Martin, Lynne; Bienert, Nancy; Mercer, Joey

2017-01-01

In air traffic control, task demand and workload have important implications for the safety and efficiency of air traffic. Task demand is dynamic, however, research on demand transitions and associated controller perception and performance is limited. In addition, there is a comparatively restricted understanding of the influence of task demand transitions on workload and performance, in association with automation. This study used an air traffic control simulation to investigate the influence of task demand transitions and two conditions of varying automation, on workload and efficiency-related performance. Findings showed that a both the direction of the task demand variation, and the amount of automation, influenced the relationship between workload and performance. Further research is needed to enhance understanding of demand transition and workload history effects on operator experience and performance, in both air traffic control and other safety-critical domains.

Self-regulated learning processes of medical students during an academic learning task.

PubMed

Gandomkar, Roghayeh; Mirzazadeh, Azim; Jalili, Mohammad; Yazdani, Kamran; Fata, Ladan; Sandars, John

2016-10-01

This study was designed to identify the self-regulated learning (SRL) processes of medical students during a biomedical science learning task and to examine the associations of the SRL processes with previous performance in biomedical science examinations and subsequent performance on a learning task. A sample of 76 Year 1 medical students were recruited based on their performance in biomedical science examinations and stratified into previous high and low performers. Participants were asked to complete a biomedical science learning task. Participants' SRL processes were assessed before (self-efficacy, goal setting and strategic planning), during (metacognitive monitoring) and after (causal attributions and adaptive inferences) their completion of the task using an SRL microanalytic interview. Descriptive statistics were used to analyse the means and frequencies of SRL processes. Univariate and multiple logistic regression analyses were conducted to examine the associations of SRL processes with previous examination performance and the learning task performance. Most participants (from 88.2% to 43.4%) reported task-specific processes for SRL measures. Students who exhibited higher self-efficacy (odds ratio [OR] 1.44, 95% confidence interval [CI] 1.09-1.90) and reported task-specific processes for metacognitive monitoring (OR 6.61, 95% CI 1.68-25.93) and causal attributions (OR 6.75, 95% CI 2.05-22.25) measures were more likely to be high previous performers. Multiple analysis revealed that similar SRL measures were associated with previous performance. The use of task-specific processes for causal attributions (OR 23.00, 95% CI 4.57-115.76) and adaptive inferences (OR 27.00, 95% CI 3.39-214.95) measures were associated with being a high learning task performer. In multiple analysis, only the causal attributions measure was associated with high learning task performance. Self-efficacy, metacognitive monitoring and causal attributions measures were associated positively with previous performance. Causal attributions and adaptive inferences measures were associated positively with learning task performance. These findings may inform remediation interventions in the early years of medical school training. © 2016 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

14- to 16-Month-Olds Attend to Distinct Labels in an Inductive Reasoning Task.

PubMed

Switzer, Jessica L; Graham, Susan A

2017-01-01

We examined how naming objects with unique labels influenced infants' reasoning about the non-obvious properties of novel objects. Seventy 14- to 16-month-olds participated in an imitation-based inductive inference task during which they were presented with target objects possessing a non-obvious sound property, followed by test objects that varied in shape similarity in comparison to the target. Infants were assigned to one of two groups: a No Label group in which objects were introduced with a general attentional phrase (i.e., "Look at this one") and a Distinct Label group in which target and test objects were labeled with two distinct count nouns (i.e., fep vs. wug ). Infants in the Distinct Label group performed significantly fewer target actions on the high-similarity objects than infants in the No Label group but did not differ in performance of actions on the low-similarity object. Within the Distinct Label group, performance on the inductive inference task was related to age, but not to working memory, inhibitory control, or vocabulary. Within the No Label condition, performance on the inductive inference task was related to a measure of inhibitory control. Our findings suggest that between 14- and 16-months, infants begin to use labels to carve out distinct categories, even when objects are highly perceptually similar.

Natural frequencies facilitate diagnostic inferences of managers

PubMed Central

Hoffrage, Ulrich; Hafenbrädl, Sebastian; Bouquet, Cyril

2015-01-01

In Bayesian inference tasks, information about base rates as well as hit rate and false-alarm rate needs to be integrated according to Bayes’ rule after the result of a diagnostic test became known. Numerous studies have found that presenting information in a Bayesian inference task in terms of natural frequencies leads to better performance compared to variants with information presented in terms of probabilities or percentages. Natural frequencies are the tallies in a natural sample in which hit rate and false-alarm rate are not normalized with respect to base rates. The present research replicates the beneficial effect of natural frequencies with four tasks from the domain of management, and with management students as well as experienced executives as participants. The percentage of Bayesian responses was almost twice as high when information was presented in natural frequencies compared to a presentation in terms of percentages. In contrast to most tasks previously studied, the majority of numerical responses were lower than the Bayesian solutions. Having heard of Bayes’ rule prior to the study did not affect Bayesian performance. An implication of our work is that textbooks explaining Bayes’ rule should teach how to represent information in terms of natural frequencies instead of how to plug probabilities or percentages into a formula. PMID:26157397

Event-related potential correlates of emergent inference in human arbitrary relational learning.

PubMed

Wang, Ting; Dymond, Simon

2013-01-01

Two experiments investigated the functional-anatomical correlates of cognition supporting untrained, emergent relational inference in a stimulus equivalence task. In Experiment 1, after learning a series of conditional relations involving words and pseudowords, participants performed a relatedness task during which EEG was recorded. Behavioural performance was faster and more accurate on untrained, indirectly related symmetry (i.e., learn AB and infer BA) and equivalence trials (i.e., learn AB and AC and infer CB) than on unrelated trials, regardless of whether or not a formal test for stimulus equivalence relations had been conducted. Consistent with previous results, event related potentials (ERPs) evoked by trained and emergent trials at parietal and occipital sites differed only for those participants who had not received a prior equivalence test. Experiment 2 further replicated and extended these behavioural and ERP findings using arbitrary symbols as stimuli and demonstrated time and frequency differences for trained and untrained relatedness trials. Overall, the findings demonstrate convincingly the ERP correlates of intra-experimentally established stimulus equivalence relations consisting entirely of arbitrary symbols and offer support for a contemporary cognitive-behavioural model of symbolic categorisation and relational inference. Copyright © 2012 Elsevier B.V. All rights reserved.

A New Modified Histogram Matching Normalization for Time Series Microarray Analysis.

PubMed

Astola, Laura; Molenaar, Jaap

2014-07-01

Microarray data is often utilized in inferring regulatory networks. Quantile normalization (QN) is a popular method to reduce array-to-array variation. We show that in the context of time series measurements QN may not be the best choice for this task, especially not if the inference is based on continuous time ODE model. We propose an alternative normalization method that is better suited for network inference from time series data.

Thinking about the weather: How display salience and knowledge affect performance in a graphic inference task.

PubMed

Hegarty, Mary; Canham, Matt S; Fabrikant, Sara I

2010-01-01

Three experiments examined how bottom-up and top-down processes interact when people view and make inferences from complex visual displays (weather maps). Bottom-up effects of display design were investigated by manipulating the relative visual salience of task-relevant and task-irrelevant information across different maps. Top-down effects of domain knowledge were investigated by examining performance and eye fixations before and after participants learned relevant meteorological principles. Map design and knowledge interacted such that salience had no effect on performance before participants learned the meteorological principles; however, after learning, participants were more accurate if they viewed maps that made task-relevant information more visually salient. Effects of display design on task performance were somewhat dissociated from effects of display design on eye fixations. The results support a model in which eye fixations are directed primarily by top-down factors (task and domain knowledge). They suggest that good display design facilitates performance not just by guiding where viewers look in a complex display but also by facilitating processing of the visual features that represent task-relevant information at a given display location. (PsycINFO Database Record (c) 2009 APA, all rights reserved).

The Causal Meaning of Genomic Predictors and How It Affects Construction and Comparison of Genome-Enabled Selection Models

PubMed Central

Valente, Bruno D.; Morota, Gota; Peñagaricano, Francisco; Gianola, Daniel; Weigel, Kent; Rosa, Guilherme J. M.

2015-01-01

The term “effect” in additive genetic effect suggests a causal meaning. However, inferences of such quantities for selection purposes are typically viewed and conducted as a prediction task. Predictive ability as tested by cross-validation is currently the most acceptable criterion for comparing models and evaluating new methodologies. Nevertheless, it does not directly indicate if predictors reflect causal effects. Such evaluations would require causal inference methods that are not typical in genomic prediction for selection. This suggests that the usual approach to infer genetic effects contradicts the label of the quantity inferred. Here we investigate if genomic predictors for selection should be treated as standard predictors or if they must reflect a causal effect to be useful, requiring causal inference methods. Conducting the analysis as a prediction or as a causal inference task affects, for example, how covariates of the regression model are chosen, which may heavily affect the magnitude of genomic predictors and therefore selection decisions. We demonstrate that selection requires learning causal genetic effects. However, genomic predictors from some models might capture noncausal signal, providing good predictive ability but poorly representing true genetic effects. Simulated examples are used to show that aiming for predictive ability may lead to poor modeling decisions, while causal inference approaches may guide the construction of regression models that better infer the target genetic effect even when they underperform in cross-validation tests. In conclusion, genomic selection models should be constructed to aim primarily for identifiability of causal genetic effects, not for predictive ability. PMID:25908318

Inference by Exclusion in Goffin Cockatoos (Cacatua goffini)

PubMed Central

O’Hara, Mark; Auersperg, Alice M. I.; Bugnyar, Thomas; Huber, Ludwig

2015-01-01

Inference by exclusion, the ability to base choices on the systematic exclusion of alternatives, has been studied in many nonhuman species over the past decade. However, the majority of methodologies employed so far are hard to integrate into a comparative framework as they rarely use controls for the effect of neophilia. Here, we present an improved approach that takes neophilia into account, using an abstract two-choice task on a touch screen, which is equally feasible for a large variety of species. To test this approach we chose Goffin cockatoos (Cacatua goffini), a highly explorative Indonesian parrot species, which have recently been reported to have sophisticated cognitive skills in the technical domain. Our results indicate that Goffin cockatoos are able to solve such abstract two-choice tasks employing inference by exclusion but also highlight the importance of other response strategies. PMID:26244692

Visual recognition and inference using dynamic overcomplete sparse learning.

PubMed

Murray, Joseph F; Kreutz-Delgado, Kenneth

2007-09-01

We present a hierarchical architecture and learning algorithm for visual recognition and other visual inference tasks such as imagination, reconstruction of occluded images, and expectation-driven segmentation. Using properties of biological vision for guidance, we posit a stochastic generative world model and from it develop a simplified world model (SWM) based on a tractable variational approximation that is designed to enforce sparse coding. Recent developments in computational methods for learning overcomplete representations (Lewicki & Sejnowski, 2000; Teh, Welling, Osindero, & Hinton, 2003) suggest that overcompleteness can be useful for visual tasks, and we use an overcomplete dictionary learning algorithm (Kreutz-Delgado, et al., 2003) as a preprocessing stage to produce accurate, sparse codings of images. Inference is performed by constructing a dynamic multilayer network with feedforward, feedback, and lateral connections, which is trained to approximate the SWM. Learning is done with a variant of the back-propagation-through-time algorithm, which encourages convergence to desired states within a fixed number of iterations. Vision tasks require large networks, and to make learning efficient, we take advantage of the sparsity of each layer to update only a small subset of elements in a large weight matrix at each iteration. Experiments on a set of rotated objects demonstrate various types of visual inference and show that increasing the degree of overcompleteness improves recognition performance in difficult scenes with occluded objects in clutter.

Bayesian structural inference for hidden processes.

PubMed

Strelioff, Christopher C; Crutchfield, James P

2014-04-01

We introduce a Bayesian approach to discovering patterns in structurally complex processes. The proposed method of Bayesian structural inference (BSI) relies on a set of candidate unifilar hidden Markov model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological ε-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be ε-machines, irrespective of estimated transition probabilities. Properties of ε-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of candidate model topologies, despite process internal structure being only indirectly present in data. We demonstrate BSI's effectiveness in estimating a process's randomness, as reflected by the Shannon entropy rate, and its structure, as quantified by the statistical complexity. We also compare using the posterior distribution over candidate models and the single, maximum a posteriori model for point estimation and show that the former more accurately reflects uncertainty in estimated values. We apply BSI to in-class examples of finite- and infinite-order Markov processes, as well to an out-of-class, infinite-state hidden process.

Bayesian structural inference for hidden processes

NASA Astrophysics Data System (ADS)

Strelioff, Christopher C.; Crutchfield, James P.

2014-04-01

We introduce a Bayesian approach to discovering patterns in structurally complex processes. The proposed method of Bayesian structural inference (BSI) relies on a set of candidate unifilar hidden Markov model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological ɛ-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be ɛ-machines, irrespective of estimated transition probabilities. Properties of ɛ-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of candidate model topologies, despite process internal structure being only indirectly present in data. We demonstrate BSI's effectiveness in estimating a process's randomness, as reflected by the Shannon entropy rate, and its structure, as quantified by the statistical complexity. We also compare using the posterior distribution over candidate models and the single, maximum a posteriori model for point estimation and show that the former more accurately reflects uncertainty in estimated values. We apply BSI to in-class examples of finite- and infinite-order Markov processes, as well to an out-of-class, infinite-state hidden process.

75 FR 76422 - Meeting of the Department of Defense Task Force on the Care, Management, and Transition of...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-08

... DEPARTMENT OF DEFENSE Office of the Secretary Meeting of the Department of Defense Task Force on... Forces (Subsequently Referred to as the Task Force) AGENCY: Department of Defense. ACTION: Notice... forthcoming meeting of the Department of Defense Task Force on the Care, Management, and Transition of...

A New Modified Histogram Matching Normalization for Time Series Microarray Analysis

PubMed Central

Astola, Laura; Molenaar, Jaap

2014-01-01

Microarray data is often utilized in inferring regulatory networks. Quantile normalization (QN) is a popular method to reduce array-to-array variation. We show that in the context of time series measurements QN may not be the best choice for this task, especially not if the inference is based on continuous time ODE model. We propose an alternative normalization method that is better suited for network inference from time series data. PMID:27600344

Ontology Mapping Neural Network: An Approach to Learning and Inferring Correspondences among Ontologies

ERIC Educational Resources Information Center

Peng, Yefei

2010-01-01

An ontology mapping neural network (OMNN) is proposed in order to learn and infer correspondences among ontologies. It extends the Identical Elements Neural Network (IENN)'s ability to represent and map complex relationships. The learning dynamics of simultaneous (interlaced) training of similar tasks interact at the shared connections of the…

The Argumentative Connective "Meme" in French: An Experimental Study in Eight- to Ten-Year-Old Children.

ERIC Educational Resources Information Center

Bassano, Dominique; Champaud, Christian

1989-01-01

Examines how children understand the argumentative function of the French connective meme (even). Two completion tasks, related to the argumentative properties of the morpheme, were used: 1) to infer the conclusion of an "even" sentence, and 2) to infer the argument position. (34 references) (Author/CB)

Gender Differences in Inference Generation by Fourth-Grade Students

ERIC Educational Resources Information Center

Clinton, Virginia; Seipel, Ben; Broek, Paul; McMaster, Kristen L.; Kendeou, Panayiota; Carlson, Sarah E.; Rapp, David N.

2014-01-01

The purpose of this study was to determine if there are gender differences among elementary school-aged students in regard to the inferences they generate during reading. Fourth-grade students (130 females; 126 males) completed think-aloud tasks while reading one practice and one experimental narrative text. Females generated a larger number and a…

Mental Rolodexing: Senior Chemistry Majors' Understanding of Chemical and Physical Properties

ERIC Educational Resources Information Center

DeFever, Ryan S.; Bruce, Heather; Bhattacharyya, Gautam

2015-01-01

Using a constructivist framework, eight senior chemistry majors were interviewed twice to determine: (i) structural inferences they are able to make from chemical and physical properties; and (ii) their ability to apply their inferences and understandings of these chemical and physical properties to solve tasks on the reactivity of organic…

Computational approaches to protein inference in shotgun proteomics

PubMed Central

2012-01-01

Shotgun proteomics has recently emerged as a powerful approach to characterizing proteomes in biological samples. Its overall objective is to identify the form and quantity of each protein in a high-throughput manner by coupling liquid chromatography with tandem mass spectrometry. As a consequence of its high throughput nature, shotgun proteomics faces challenges with respect to the analysis and interpretation of experimental data. Among such challenges, the identification of proteins present in a sample has been recognized as an important computational task. This task generally consists of (1) assigning experimental tandem mass spectra to peptides derived from a protein database, and (2) mapping assigned peptides to proteins and quantifying the confidence of identified proteins. Protein identification is fundamentally a statistical inference problem with a number of methods proposed to address its challenges. In this review we categorize current approaches into rule-based, combinatorial optimization and probabilistic inference techniques, and present them using integer programing and Bayesian inference frameworks. We also discuss the main challenges of protein identification and propose potential solutions with the goal of spurring innovative research in this area. PMID:23176300

Bridging the gap between implicit and explicit understanding: how language development promotes the processing and representation of false belief.

PubMed

San Juan, Valerie; Astington, Janet Wilde

2012-03-01

Recent advancements in the field of infant false-belief reasoning have brought into question whether performance on implicit and explicit measures of false belief is driven by the same level of representational understanding. The success of infants on implicit measures has also raised doubt over the role that language development plays in the development of false-belief reasoning. In the current paper, we argue that children's performance on disparate measures cannot be used to infer similarities in understanding across different age groups. Instead, we argue that development must continue to occur between the periods when children can reason implicitly and then explicitly about false belief. We then propose mechanisms by which language associated with false-belief tasks facilitates this transition by assisting with both the processes of elicited response selection and the formation of metarepresentational understanding. © 2011 The British Psychological Society.

Using E-Z Reader to Simulate Eye Movements in Nonreading Tasks: A Unified Framework for Understanding the Eye-Mind Link

ERIC Educational Resources Information Center

Reichle, Erik D.; Pollatsek, Alexander; Rayner, Keith

2012-01-01

Nonreading tasks that share some (but not all) of the task demands of reading have often been used to make inferences about how cognition influences when the eyes move during reading. In this article, we use variants of the E-Z Reader model of eye-movement control in reading to simulate eye-movement behavior in several of these tasks, including…

Inferring interventional predictions from observational learning data.

PubMed

Meder, Bjorn; Hagmayer, York; Waldmann, Michael R

2008-02-01

Previous research has shown that people are capable of deriving correct predictions for previously unseen actions from passive observations of causal systems (Waldmann & Hagmayer, 2005). However, these studies were limited, since learning data were presented as tabulated data only, which may have turned the task more into a reasoning rather than a learning task. In two experiments, we therefore presented learners with trial-by-trial observational learning input referring to a complex causal model consisting of four events. To test the robustness of the capacity to derive correct observational and interventional inferences, we pitted causal order against the temporal order of learning events. The results show that people are, in principle, capable of deriving correct predictions after purely observational trial-by-trial learning, even with relatively complex causal models. However, conflicting temporal information can impair performance, particularly when the inferences require taking alternative causal pathways into account.

The penumbra of learning: a statistical theory of synaptic tagging and capture.

PubMed

Gershman, Samuel J

2014-01-01

Learning in humans and animals is accompanied by a penumbra: Learning one task benefits from learning an unrelated task shortly before or after. At the cellular level, the penumbra of learning appears when weak potentiation of one synapse is amplified by strong potentiation of another synapse on the same neuron during a critical time window. Weak potentiation sets a molecular tag that enables the synapse to capture plasticity-related proteins synthesized in response to strong potentiation at another synapse. This paper describes a computational model which formalizes synaptic tagging and capture in terms of statistical learning mechanisms. According to this model, synaptic strength encodes a probabilistic inference about the dynamically changing association between pre- and post-synaptic firing rates. The rate of change is itself inferred, coupling together different synapses on the same neuron. When the inputs to one synapse change rapidly, the inferred rate of change increases, amplifying learning at other synapses.

Instantaneous Conventions

PubMed Central

Misyak, Jennifer; Noguchi, Takao; Chater, Nick

2016-01-01

Humans can communicate even with few existing conventions in common (e.g., when they lack a shared language). We explored what makes this phenomenon possible with a nonlinguistic experimental task requiring participants to coordinate toward a common goal. We observed participants creating new communicative conventions using the most minimal possible signals. These conventions, furthermore, changed on a trial-by-trial basis in response to shared environmental and task constraints. Strikingly, as a result, signals of the same form successfully conveyed contradictory messages from trial to trial. Such behavior is evidence for the involvement of what we term joint inference, in which social interactants spontaneously infer the most sensible communicative convention in light of the common ground between them. Joint inference may help to elucidate how communicative conventions emerge instantaneously and how they are modified and reshaped into the elaborate systems of conventions involved in human communication, including natural languages. PMID:27793986

Priming trait inferences through pictures and moving pictures: the impact of open and closed mindsets.

PubMed

Fiedler, Klaus; Schenck, Wolfram; Watling, Marlin; Menges, Jochen I

2005-02-01

A newly developed paradigm for studying spontaneous trait inferences (STI) was applied in 3 experiments. The authors primed dyadic stimulus behaviors involving a subject (S) and an object (O) person through degraded pictures or movies. An encoding task called for the verification of either a graphical feature or a semantic interpretation, which either fit or did not fit the primed behavior. Next, participants had to identify a trait word that appeared gradually behind a mask and that either matched or did not match the primed behavior. STI effects, defined as shorter identification latencies for matching than nonmatching traits, were stronger for S than for O traits, after graphical rather than semantic encoding decisions and after encoding failures. These findings can be explained by assuming that trait inferences are facilitated by open versus closed mindsets supposed to result from distracting (graphical) encoding tasks or encoding failures (involving nonfitting interpretations).

New normative standards of conditional reasoning and the dual-source model

PubMed Central

Singmann, Henrik; Klauer, Karl Christoph; Over, David

2014-01-01

There has been a major shift in research on human reasoning toward Bayesian and probabilistic approaches, which has been called a new paradigm. The new paradigm sees most everyday and scientific reasoning as taking place in a context of uncertainty, and inference is from uncertain beliefs and not from arbitrary assumptions. In this manuscript we present an empirical test of normative standards in the new paradigm using a novel probabilized conditional reasoning task. Our results indicated that for everyday conditional with at least a weak causal connection between antecedent and consequent only the conditional probability of the consequent given antecedent contributes unique variance to predicting the probability of conditional, but not the probability of the conjunction, nor the probability of the material conditional. Regarding normative accounts of reasoning, we found significant evidence that participants' responses were confidence preserving (i.e., p-valid in the sense of Adams, 1998) for MP inferences, but not for MT inferences. Additionally, only for MP inferences and to a lesser degree for DA inferences did the rate of responses inside the coherence intervals defined by mental probability logic (Pfeifer and Kleiter, 2005, 2010) exceed chance levels. In contrast to the normative accounts, the dual-source model (Klauer et al., 2010) is a descriptive model. It posits that participants integrate their background knowledge (i.e., the type of information primary to the normative approaches) and their subjective probability that a conclusion is seen as warranted based on its logical form. Model fits showed that the dual-source model, which employed participants' responses to a deductive task with abstract contents to estimate the form-based component, provided as good an account of the data as a model that solely used data from the probabilized conditional reasoning task. PMID:24860516

New normative standards of conditional reasoning and the dual-source model.

PubMed

Singmann, Henrik; Klauer, Karl Christoph; Over, David

2014-01-01

There has been a major shift in research on human reasoning toward Bayesian and probabilistic approaches, which has been called a new paradigm. The new paradigm sees most everyday and scientific reasoning as taking place in a context of uncertainty, and inference is from uncertain beliefs and not from arbitrary assumptions. In this manuscript we present an empirical test of normative standards in the new paradigm using a novel probabilized conditional reasoning task. Our results indicated that for everyday conditional with at least a weak causal connection between antecedent and consequent only the conditional probability of the consequent given antecedent contributes unique variance to predicting the probability of conditional, but not the probability of the conjunction, nor the probability of the material conditional. Regarding normative accounts of reasoning, we found significant evidence that participants' responses were confidence preserving (i.e., p-valid in the sense of Adams, 1998) for MP inferences, but not for MT inferences. Additionally, only for MP inferences and to a lesser degree for DA inferences did the rate of responses inside the coherence intervals defined by mental probability logic (Pfeifer and Kleiter, 2005, 2010) exceed chance levels. In contrast to the normative accounts, the dual-source model (Klauer et al., 2010) is a descriptive model. It posits that participants integrate their background knowledge (i.e., the type of information primary to the normative approaches) and their subjective probability that a conclusion is seen as warranted based on its logical form. Model fits showed that the dual-source model, which employed participants' responses to a deductive task with abstract contents to estimate the form-based component, provided as good an account of the data as a model that solely used data from the probabilized conditional reasoning task.

Reasoning in explanation-based decision making.

PubMed

Pennington, N; Hastie, R

1993-01-01

A general theory of explanation-based decision making is outlined and the multiple roles of inference processes in the theory are indicated. A typology of formal and informal inference forms, originally proposed by Collins (1978a, 1978b), is introduced as an appropriate framework to represent inferences that occur in the overarching explanation-based process. Results from the analysis of verbal reports of decision processes are presented to demonstrate the centrality and systematic character of reasoning in a representative legal decision-making task.

14- to 16-Month-Olds Attend to Distinct Labels in an Inductive Reasoning Task

PubMed Central

Switzer, Jessica L.; Graham, Susan A.

2017-01-01

We examined how naming objects with unique labels influenced infants’ reasoning about the non-obvious properties of novel objects. Seventy 14- to 16-month-olds participated in an imitation-based inductive inference task during which they were presented with target objects possessing a non-obvious sound property, followed by test objects that varied in shape similarity in comparison to the target. Infants were assigned to one of two groups: a No Label group in which objects were introduced with a general attentional phrase (i.e., “Look at this one”) and a Distinct Label group in which target and test objects were labeled with two distinct count nouns (i.e., fep vs. wug). Infants in the Distinct Label group performed significantly fewer target actions on the high-similarity objects than infants in the No Label group but did not differ in performance of actions on the low-similarity object. Within the Distinct Label group, performance on the inductive inference task was related to age, but not to working memory, inhibitory control, or vocabulary. Within the No Label condition, performance on the inductive inference task was related to a measure of inhibitory control. Our findings suggest that between 14- and 16-months, infants begin to use labels to carve out distinct categories, even when objects are highly perceptually similar. PMID:28484410

Switch Detection in Preschoolers’ Cognitive Flexibility

PubMed Central

Chevalier, Nicolas; Wiebe, Sandra A.; Huber, Kristina L.; Espy, Kimberly Andrews

2011-01-01

The present study addressed the role of switch detection in cognitive flexibility by testing the effect of transition cues (i.e., cues that directly signal the need to switch or maintain a given task goal) in a cued set-shifting paradigm at age 5. Children performed better, especially on switch trials, when transition cues were combined with traditional task cues (i.e., cues that directly signal the relevant task on a given trial), relative to conditions without transition cues. This effect was not influenced by explicit knowledge of transition cues or transition cue transparency, suggesting transition cues did not need to be semantically processed to be beneficial. These findings reveal that young children’s difficulties in set-shifting situations partially stem from failures to monitor for the need to switch. PMID:21353678

Perceptual Decision-Making as Probabilistic Inference by Neural Sampling.

PubMed

Haefner, Ralf M; Berkes, Pietro; Fiser, József

2016-05-04

We address two main challenges facing systems neuroscience today: understanding the nature and function of cortical feedback between sensory areas and of correlated variability. Starting from the old idea of perception as probabilistic inference, we show how to use knowledge of the psychophysical task to make testable predictions for the influence of feedback signals on early sensory representations. Applying our framework to a two-alternative forced choice task paradigm, we can explain multiple empirical findings that have been hard to account for by the traditional feedforward model of sensory processing, including the task dependence of neural response correlations and the diverging time courses of choice probabilities and psychophysical kernels. Our model makes new predictions and characterizes a component of correlated variability that represents task-related information rather than performance-degrading noise. It demonstrates a normative way to integrate sensory and cognitive components into physiologically testable models of perceptual decision-making. Copyright © 2016 Elsevier Inc. All rights reserved.

Deductive and inductive reasoning in obsessive-compulsive disorder.

PubMed

Pélissier, Marie-Claude; O'Connor, Kieron P

2002-03-01

This study tested the hypothesis that people with obsessive-compulsive disorder (OCD) show an inductive reasoning style distinct from people with generalized anxiety disorder (GAD) and from participants in a non-anxious (NA) control group. The experimental procedure consisted of administering a range of six deductive and inductive tasks and a probabilistic task in order to compare reasoning processes between groups. Recruitment was in the Montreal area within a French-speaking population. The participants were 12 people with OCD, 12 NA controls and 10 people with GAD. Participants completed a series of written and oral reasoning tasks including the Wason Selection Task, a Bayesian probability task and other inductive tasks, designed by the authors. There were no differences between groups in deductive reasoning. On an inductive "bridging task", the participants with OCD always took longer than the NA control and GAD groups to infer a link between two statements and to elaborate on this possible link. The OCD group alone showed a significant decrease in their degree of conviction about an arbitrary statement after inductively generating reasons to support this statement. Differences in probabilistic reasoning replicated those of previous authors. The results pinpoint the importance of examining inference processes in people with OCD in order to further refine the clinical applications of behavioural-cognitive therapy for this disorder.

The role of familiarity in binary choice inferences.

PubMed

Honda, Hidehito; Abe, Keiga; Matsuka, Toshihiko; Yamagishi, Kimihiko

2011-07-01

In research on the recognition heuristic (Goldstein & Gigerenzer, Psychological Review, 109, 75-90, 2002), knowledge of recognized objects has been categorized as "recognized" or "unrecognized" without regard to the degree of familiarity of the recognized object. In the present article, we propose a new inference model--familiarity-based inference. We hypothesize that when subjective knowledge levels (familiarity) of recognized objects differ, the degree of familiarity of recognized objects will influence inferences. Specifically, people are predicted to infer that the more familiar object in a pair of two objects has a higher criterion value on the to-be-judged dimension. In two experiments, using a binary choice task, we examined inferences about populations in a pair of two cities. Results support predictions of familiarity-based inference. Participants inferred that the more familiar city in a pair was more populous. Statistical modeling showed that individual differences in familiarity-based inference lie in the sensitivity to differences in familiarity. In addition, we found that familiarity-based inference can be generally regarded as an ecologically rational inference. Furthermore, when cue knowledge about the inference criterion was available, participants made inferences based on the cue knowledge about population instead of familiarity. Implications of the role of familiarity in psychological processes are discussed.

Probabilistic brains: knowns and unknowns

PubMed Central

Pouget, Alexandre; Beck, Jeffrey M; Ma, Wei Ji; Latham, Peter E

2015-01-01

There is strong behavioral and physiological evidence that the brain both represents probability distributions and performs probabilistic inference. Computational neuroscientists have started to shed light on how these probabilistic representations and computations might be implemented in neural circuits. One particularly appealing aspect of these theories is their generality: they can be used to model a wide range of tasks, from sensory processing to high-level cognition. To date, however, these theories have only been applied to very simple tasks. Here we discuss the challenges that will emerge as researchers start focusing their efforts on real-life computations, with a focus on probabilistic learning, structural learning and approximate inference. PMID:23955561

Inferential functioning in visually impaired children.

PubMed

Puche-Navarro, Rebeca; Millán, Rafael

2007-01-01

The current study explores the inferential abilities of visually impaired children in a task presented in two formats, manipulative and verbal. The results showed that in the group of visually impaired children, just as with children with normal sight, there was a wide range of inference types. It was found that the visually impaired children perform slightly better in the use of inductive and relational inferences in the verbal format, while in the manipulative format children with normal sight perform better. These results suggest that in inferential functioning of young children, and especially visually impaired children, the format of the task influences performance more than the child's visual ability.

Structural mapping in statistical word problems: A relational reasoning approach to Bayesian inference.

PubMed

Johnson, Eric D; Tubau, Elisabet

2017-06-01

Presenting natural frequencies facilitates Bayesian inferences relative to using percentages. Nevertheless, many people, including highly educated and skilled reasoners, still fail to provide Bayesian responses to these computationally simple problems. We show that the complexity of relational reasoning (e.g., the structural mapping between the presented and requested relations) can help explain the remaining difficulties. With a non-Bayesian inference that required identical arithmetic but afforded a more direct structural mapping, performance was universally high. Furthermore, reducing the relational demands of the task through questions that directed reasoners to use the presented statistics, as compared with questions that prompted the representation of a second, similar sample, also significantly improved reasoning. Distinct error patterns were also observed between these presented- and similar-sample scenarios, which suggested differences in relational-reasoning strategies. On the other hand, while higher numeracy was associated with better Bayesian reasoning, higher-numerate reasoners were not immune to the relational complexity of the task. Together, these findings validate the relational-reasoning view of Bayesian problem solving and highlight the importance of considering not only the presented task structure, but also the complexity of the structural alignment between the presented and requested relations.

Structural Analysis of Treatment Cycles Representing Transitions between Nursing Organizational Units Inferred from Diabetes

PubMed Central

Dehmer, Matthias; Kurt, Zeyneb; Emmert-Streib, Frank; Them, Christa; Schulc, Eva; Hofer, Sabine

2015-01-01

In this paper, we investigate treatment cycles inferred from diabetes data by means of graph theory. We define the term treatment cycles graph-theoretically and perform a descriptive as well as quantitative analysis thereof. Also, we interpret our findings in terms of nursing and clinical management. PMID:26030296

Informants' Traits Weigh Heavily in Young Children's Trust in Testimony and in Their Epistemic Inferences

ERIC Educational Resources Information Center

Lane, Jonathan D.; Wellman, Henry M.; Gelman, Susan A.

2013-01-01

This study examined how informants' traits affect how children seek information, trust testimony, and make inferences about informants' knowledge. Eighty-one 3- to 6-year-olds and 26 adults completed tasks where they requested and endorsed information provided by one of two informants with conflicting traits (e.g., honesty vs. dishonesty).…

Generic Language and Speaker Confidence Guide Preschoolers' Inferences about Novel Animate Kinds

ERIC Educational Resources Information Center

Stock, Hayli R.; Graham, Susan A.; Chambers, Craig G.

2009-01-01

We investigated the influence of speaker certainty on 156 four-year-old children's sensitivity to generic and nongeneric statements. An inductive inference task was implemented, in which a speaker described a nonobvious property of a novel creature using either a generic or a nongeneric statement. The speaker appeared to be confident, neutral, or…

EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning.

PubMed

Zhao, Chao; Jiang, Jingchi; Guan, Yi; Guo, Xitong; He, Bin

2018-05-01

Electronic medical records (EMRs) contain medical knowledge that can be used for clinical decision support (CDS). Our objective is to develop a general system that can extract and represent knowledge contained in EMRs to support three CDS tasks-test recommendation, initial diagnosis, and treatment plan recommendation-given the condition of a patient. We extracted four kinds of medical entities from records and constructed an EMR-based medical knowledge network (EMKN), in which nodes are entities and edges reflect their co-occurrence in a record. Three bipartite subgraphs (bigraphs) were extracted from the EMKN, one to support each task. One part of the bigraph was the given condition (e.g., symptoms), and the other was the condition to be inferred (e.g., diseases). Each bigraph was regarded as a Markov random field (MRF) to support the inference. We proposed three graph-based energy functions and three likelihood-based energy functions. Two of these functions are based on knowledge representation learning and can provide distributed representations of medical entities. Two EMR datasets and three metrics were utilized to evaluate the performance. As a whole, the evaluation results indicate that the proposed system outperformed the baseline methods. The distributed representation of medical entities does reflect similarity relationships with respect to knowledge level. Combining EMKN and MRF is an effective approach for general medical knowledge representation and inference. Different tasks, however, require individually designed energy functions. Copyright © 2018 Elsevier B.V. All rights reserved.

On the Assessment of Grammatical Gender Knowledge in Aphasia: The Danger of Relying on Explicit Metalinguistic Tasks.

ERIC Educational Resources Information Center

Scarna, Antonina; Ellis, Andrew W.

2002-01-01

Studied a bilingual Italian-English aphasic patient who was very poor in categorizing Italian nouns for grammatical gender in explicit metalinguistic tasks, and was at chance when gender could not be inferred from the word's phonology. However, he showed a good ability to modify adjectives to match the gender of nouns in a task that involved…

Task Specificity and the Influence of Memory on Visual Search: Comment on Vo and Wolfe (2012)

ERIC Educational Resources Information Center

Hollingworth, Andrew

2012-01-01

Recent results from Vo and Wolfe (2012b) suggest that the application of memory to visual search may be task specific: Previous experience searching for an object facilitated later search for that object, but object information acquired during a different task did not appear to transfer to search. The latter inference depended on evidence that a…

78 FR 28580 - Department of Defense Task Force on the Care, Management, and Transition of Recovering Wounded...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-15

... DEPARTMENT OF DEFENSE Office of the Secretary Department of Defense Task Force on the Care, Management, and Transition of Recovering Wounded, Ill, and Injured Members of the Armed Forces AGENCY: Office... Defense announces the following Federal Advisory Committee meeting of the Department of Defense Task Force...

Children's Task Oriented Patterns in Early Childhood: A Latent Transition Analysis

ERIC Educational Resources Information Center

Wang, Feihong; Algina, James; Snyder, Patricia; Cox, Martha; Vernon-Feagans, Lynne; Cox, Martha; Blair, Clancy; Burchinal, Margaret; Burton, Linda; Crnic, Keith; Crouter, Ann; Garrett-Peters, Patricia; Greenberg, Mark; Lanza, Stephanie; Mills-Koonce, Roger; Werner, Emily; Willoughby, Michael

2017-01-01

We examined individual differences and predictions of children's patterns in behavioral, emotional and attentional efforts toward challenging puzzle tasks at 24 and 35 months using data from a large longitudinal rural representative sample. Using latent transition analysis, we found four distinct task-oriented patterns in problem-solving tasks…

Using a Personal Digital Assistant to Increase Completion of Novel Tasks and Independent Transitioning by Students with Autism Spectrum Disorder

ERIC Educational Resources Information Center

Mechling, Linda C.; Savidge, Erin J.

2011-01-01

The purpose of this study was to evaluate the use of a Personal Digital Assistant with multiple prompt levels to increase completion of novel task boxes and transitioning within and between tasks. The study used a multiple probe design across three sets of task boxes replicated with three students with a diagnosis of autism spectrum disorder.…

Masses of Kepler-46b, c from Transit Timing Variations

NASA Astrophysics Data System (ADS)

Saad-Olivera, Ximena; Nesvorný, David; Kipping, David M.; Roig, Fernando

2017-04-01

We use 16 quarters of the Kepler mission data to analyze the transit timing variations (TTVs) of the extrasolar planet Kepler-46b (KOI-872). Our dynamical fits confirm that the TTVs of this planet (period P={33.648}-0.005+0.004 days) are produced by a non-transiting planet Kepler-46c (P={57.325}-0.098+0.116 days). The Bayesian inference tool MultiNest is used to infer the dynamical parameters of Kepler-46b and Kepler-46c. We find that the two planets have nearly coplanar and circular orbits, with eccentricities ≃ 0.03 somewhat higher than previously estimated. The masses of the two planets are found to be {M}b={0.885}-0.343+0.374 and {M}c={0.362}-0.016+0.016 Jupiter masses, with M b being determined here from TTVs for the first time. Due to the precession of its orbital plane, Kepler-46c should start transiting its host star a few decades from now.

How providing more or less time to solve a cognitive task interferes with upright stance control; a posturographic analysis on healthy young adults.

PubMed

Rougier, Patrice R; Bonnet, Cédrick T

2016-06-01

Contrasted postural effects have been reported in dual-task protocols associating balance control and cognitive task that could be explained by the nature and the relative difficulty of the cognitive task and the biomechanical significance of the force platform data. To better assess their respective role, eleven healthy young adults were required to stand upright quietly on a force platform while concomitantly solving mental-calculation or mental-navigation cognitive tasks. Various levels of difficulty were applied by adjusting the velocity rate at which the instructions were provided to the subject according to his/her maximal capacities measured beforehand. A condition without any concomitant cognitive task was added to constitute a baseline behavior. Two basic components, the horizontal center-of-gravity movements and the horizontal difference between center-of-gravity and center-of-pressures were computed from the complex center-of-pressure recorded movements. It was hypothesized that increasing the delay should infer less interaction between postural control and task solution. The results indicate that both mental-calculation and mental-navigation tasks induce reduced amplitudes for the center-of-pressure minus center-of-gravity movements, only along the mediolateral axis, whereas center-of-gravity movements were not affected, suggesting that different circuits are involved in the central nervous system to control these two movements. Moreover, increasing the delays task does not infer any effect for both movements. Since center-of-pressure minus center-of-gravity expresses the horizontal acceleration communicated to the center-of-gravity, one may assume that the control of the latter should be facilitated in dual-tasks conditions, inferring reduced center-of-gravity movements, which is not seen in our results. This lack of effect should be thus interpreted as a modification in the control of these center-of-gravity movements. Taken together, these results emphasized how undisturbed upright stance control can be impacted by mental tasks requiring attention, whatever their nature (calculation or navigation) and their relative difficulty. Depending on the provided instructions, i.e. focusing our attention on body movements or on the opposite diverting this attention toward other objectives, the evaluation of upright stance control capacities might be drastically altered. Copyright © 2016. Published by Elsevier B.V.

Persuading and Dissuading by Conditional Argument

ERIC Educational Resources Information Center

Thompson, V.A.; Evans, J.St.B.T.; Handley, S.J.

2005-01-01

Informal reasoning typically draws on a wider range of inferential behaviour than is measured by traditional inference tasks. In this paper, we developed several tasks to study informal reasoning with two novel types of conditional statements: Persuasions (e.g., if the Kyoto accord is ratified, greenhouse gas emissions will be reduced) and…

Plan recognition and generalization in command languages with application to telerobotics

NASA Technical Reports Server (NTRS)

Yared, Wael I.; Sheridan, Thomas B.

1991-01-01

A method for pragmatic inference as a necessary accompaniment to command languages is proposed. The approach taken focuses on the modeling and recognition of the human operator's intent, which relates sequences of domain actions ('plans') to changes in some model of the task environment. The salient feature of this module is that it captures some of the physical and linguistic contextual aspects of an instruction. This provides a basis for generalization and reinterpretation of the instruction in different task environments. The theoretical development is founded on previous work in computational linguistics and some recent models in the theory of action and intention. To illustrate these ideas, an experimental command language to a telerobot is implemented. The program consists of three different components: a robot graphic simulation, the command language itself, and the domain-independent pragmatic inference module. Examples of task instruction processes are provided to demonstrate the benefits of this approach.

The adaptive use of recognition in group decision making.

PubMed

Kämmer, Juliane E; Gaissmaier, Wolfgang; Reimer, Torsten; Schermuly, Carsten C

2014-06-01

Applying the framework of ecological rationality, the authors studied the adaptivity of group decision making. In detail, they investigated whether groups apply decision strategies conditional on their composition in terms of task-relevant features. The authors focused on the recognition heuristic, so the task-relevant features were the validity of the group members' recognition and knowledge, which influenced the potential performance of group strategies. Forty-three three-member groups performed an inference task in which they had to infer which of two German companies had the higher market capitalization. Results based on the choice data support the hypothesis that groups adaptively apply the strategy that leads to the highest theoretically achievable performance. Time constraints had no effect on strategy use but did have an effect on the proportions of different types of arguments. Possible mechanisms underlying the adaptive use of recognition in group decision making are discussed. © 2014 Cognitive Science Society, Inc.

Hemispheric processing of predictive inferences during reading: The influence of negatively emotional valenced stimuli.

PubMed

Virtue, Sandra; Schutzenhofer, Michael; Tomkins, Blaine

2017-07-01

Although a left hemisphere advantage is usually evident during language processing, the right hemisphere is highly involved during the processing of weakly constrained inferences. However, currently little is known about how the emotional valence of environmental stimuli influences the hemispheric processing of these inferences. In the current study, participants read texts promoting either strongly or weakly constrained predictive inferences and performed a lexical decision task to inference-related targets presented to the left visual field-right hemisphere or the right visual field-left hemisphere. While reading these texts, participants either listened to dissonant music (i.e., the music condition) or did not listen to music (i.e., the no music condition). In the no music condition, the left hemisphere showed an advantage for strongly constrained inferences compared to weakly constrained inferences, whereas the right hemisphere showed high facilitation for both strongly and weakly constrained inferences. In the music condition, both hemispheres showed greater facilitation for strongly constrained inferences than for weakly constrained inferences. These results suggest that negatively valenced stimuli (such as dissonant music) selectively influences the right hemisphere's processing of weakly constrained inferences during reading.

Two-Year-Olds Use the Generic/Nongeneric Distinction to Guide Their Inferences about Novel Kinds

ERIC Educational Resources Information Center

Graham, Susan A.; Nayer, Samantha L.; Gelman, Susan A.

2011-01-01

These studies investigated two hundred and forty-four 24- and 30-month-olds' sensitivity to generic versus nongeneric language when acquiring knowledge about novel kinds. Toddlers were administered an inductive inference task, during which they heard a generic noun phrase (e.g., "Blicks drink milk") or a nongeneric noun phrase (e.g., "This blick…

Getting Clued In: Inferential Processing and Comprehension Monitoring in Boys with ADHD

ERIC Educational Resources Information Center

Berthiaume, Kristen S.; Lorch, Elizabeth P.; Milich, Richard

2010-01-01

Objective: The present study examines the ability of children with ADHD to make inferences and monitor ongoing understanding of texts, to shed light on their academic difficulties. Method: A total of 29 boys with ADHD and 41 comparison boys between the ages of 7 and 12 participated. Three tasks measure how boys create and evaluate inferences,…

Probabilistic Inference: Task Dependency and Individual Differences of Probability Weighting Revealed by Hierarchical Bayesian Modeling

PubMed Central

Boos, Moritz; Seer, Caroline; Lange, Florian; Kopp, Bruno

2016-01-01

Cognitive determinants of probabilistic inference were examined using hierarchical Bayesian modeling techniques. A classic urn-ball paradigm served as experimental strategy, involving a factorial two (prior probabilities) by two (likelihoods) design. Five computational models of cognitive processes were compared with the observed behavior. Parameter-free Bayesian posterior probabilities and parameter-free base rate neglect provided inadequate models of probabilistic inference. The introduction of distorted subjective probabilities yielded more robust and generalizable results. A general class of (inverted) S-shaped probability weighting functions had been proposed; however, the possibility of large differences in probability distortions not only across experimental conditions, but also across individuals, seems critical for the model's success. It also seems advantageous to consider individual differences in parameters of probability weighting as being sampled from weakly informative prior distributions of individual parameter values. Thus, the results from hierarchical Bayesian modeling converge with previous results in revealing that probability weighting parameters show considerable task dependency and individual differences. Methodologically, this work exemplifies the usefulness of hierarchical Bayesian modeling techniques for cognitive psychology. Theoretically, human probabilistic inference might be best described as the application of individualized strategic policies for Bayesian belief revision. PMID:27303323

78 FR 66902 - Department of Defense Task Force on the Care, Management, and Transition of Recovering Wounded...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-07

... DEPARTMENT OF DEFENSE Office of the Secretary Department of Defense Task Force on the Care, Management, and Transition of Recovering Wounded, Ill, and Injured Members of the Armed Forces; Notice of... Federal Advisory Committee meeting of the Department of Defense Task Force on the Care, Management, and...

Supervised dictionary learning for inferring concurrent brain networks.

PubMed

Zhao, Shijie; Han, Junwei; Lv, Jinglei; Jiang, Xi; Hu, Xintao; Zhao, Yu; Ge, Bao; Guo, Lei; Liu, Tianming

2015-10-01

Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.

77 FR 4787 - Department of Defense Task Force on the Care, Management, and Transition of Recovering Wounded...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-31

..., Management, and Transition of Recovering Wounded, Ill, and Injured Members of the Armed Forces; Amended... of the Department of Defense Task Force on the Care, Management, and Transition of Recovering Wounded... edition of the Federal Register (77 FR 3454) is amended to reflect changes in the meeting times and agenda...

Semi-Supervised Multi-View Learning for Gene Network Reconstruction

PubMed Central

Ceci, Michelangelo; Pio, Gianvito; Kuzmanovski, Vladimir; Džeroski, Sašo

2015-01-01

The task of gene regulatory network reconstruction from high-throughput data is receiving increasing attention in recent years. As a consequence, many inference methods for solving this task have been proposed in the literature. It has been recently observed, however, that no single inference method performs optimally across all datasets. It has also been shown that the integration of predictions from multiple inference methods is more robust and shows high performance across diverse datasets. Inspired by this research, in this paper, we propose a machine learning solution which learns to combine predictions from multiple inference methods. While this approach adds additional complexity to the inference process, we expect it would also carry substantial benefits. These would come from the automatic adaptation to patterns on the outputs of individual inference methods, so that it is possible to identify regulatory interactions more reliably when these patterns occur. This article demonstrates the benefits (in terms of accuracy of the reconstructed networks) of the proposed method, which exploits an iterative, semi-supervised ensemble-based algorithm. The algorithm learns to combine the interactions predicted by many different inference methods in the multi-view learning setting. The empirical evaluation of the proposed algorithm on a prokaryotic model organism (E. coli) and on a eukaryotic model organism (S. cerevisiae) clearly shows improved performance over the state of the art methods. The results indicate that gene regulatory network reconstruction for the real datasets is more difficult for S. cerevisiae than for E. coli. The software, all the datasets used in the experiments and all the results are available for download at the following link: http://figshare.com/articles/Semi_supervised_Multi_View_Learning_for_Gene_Network_Reconstruction/1604827. PMID:26641091

Spatiotemporal Phase Synchronization in Adaptive Reconfiguration from Action Observation Network to Mentalizing Network for Understanding Other's Action Intention.

PubMed

Zhang, Li; Gan, John Q; Zheng, Wenming; Wang, Haixian

2018-05-01

In action intention understanding, the mirror system is involved in perception-action matching process and the mentalizing system underlies higher-level intention inference. By analyzing the dynamic functional connectivity in α (8-12 Hz) and β (12-30 Hz) frequency bands over a "hand-cup interaction" observation task, this study investigates the topological transition from the action observation network (AON) to the mentalizing network (MZN), and estimates their functional relevance for intention identification from other's different action kinematics. Sequential brain microstates were extracted based on event-related potentials (ERPs), in which significantly differing neuronal responses were found in N170-P200 related to perceptually matching kinematic profiles and P400-700 involved in goal inference. Inter-electrode weighted phase lag index analysis on the ERP microstates revealed a shift of hub centrality salient in α frequency band, from the AON dominated by left-lateral frontal-premotor-temporal and temporal-parietooccipital synchronizations to the MZN consisting of more bilateral frontal-parietal and temporal-parietal synchronizations. As compared with usual actions, intention identification of unintelligible actions induces weaker synchronizations in the AON but dramatically increased connectivity in right frontal-temporal-parietal regions of the MZN, indicating a spatiotemporally complementary effect between the functional network configurations involved in mirror and mentalizing processes. Perceptual processing in observing usual/unintelligible actions decreases/increases requirements for intention inference, which would induce less/greater functional network reorganization on the way to mentalization. From the comparison, our study suggests that the adaptive topological changes from the AON to the MZN indicate implicit causal association between the mirror and mentalizing systems for decoding others' intentionality.

Secondary Analysis of National Longitudinal Transition Study 2 Data

ERIC Educational Resources Information Center

Hicks, Tyler A.; Knollman, Greg A.

2015-01-01

This review examines published secondary analyses of National Longitudinal Transition Study 2 (NLTS2) data, with a primary focus upon statistical objectives, paradigms, inferences, and methods. Its primary purpose was to determine which statistical techniques have been common in secondary analyses of NLTS2 data. The review begins with an…

Inferential reasoning by exclusion in children (Homo sapiens).

PubMed

Hill, Andrew; Collier-Baker, Emma; Suddendorf, Thomas

2012-08-01

The cups task is the most widely adopted forced-choice paradigm for comparative studies of inferential reasoning by exclusion. In this task, subjects are presented with two cups, one of which has been surreptitiously baited. When the empty cup is shaken or its interior shown, it is possible to infer by exclusion that the alternative cup contains the reward. The present study extends the existing body of comparative work to include human children (Homo sapiens). Like chimpanzees (Pan troglodytes) that were tested with the same equipment and near-identical procedures, children aged three to five made apparent inferences using both visual and auditory information, although the youngest children showed the least-developed ability in the auditory modality. However, unlike chimpanzees, children of all ages used causally irrelevant information in a control test designed to examine the possibility that their apparent auditory inferences were the product of contingency learning (the duplicate cups test). Nevertheless, the children's ability to reason by exclusion was corroborated by their performance on a novel verbal disjunctive syllogism test, and we found preliminary evidence consistent with the suggestion that children used their causal-logical understanding to reason by exclusion in the cups task, but subsequently treated the duplicate cups information as symbolic or communicative, rather than causal. Implications for future comparative research are discussed. 2012 APA, all rights reserved

Student Teachers’ Proof Schemes on Proof Tasks Involving Inequality: Deductive or Inductive?

NASA Astrophysics Data System (ADS)

Rosyidi, A. H.; Kohar, A. W.

2018-01-01

Exploring student teachers’ proof ability is crucial as it is important for improving the quality of their learning process and help their future students learn how to construct a proof. Hence, this study aims at exploring at the proof schemes of student teachers in the beginning of their studies. Data were collected from 130 proofs resulted by 65 Indonesian student teachers on two proof tasks involving algebraic inequality. To analyse, the proofs were classified into the refined proof schemes level proposed by Lee (2016) ranging from inductive, which only provides irrelevant inferences, to deductive proofs, which consider addressing formal representation. Findings present several examples of each of Lee’s level on the student teachers’ proofs spanning from irrelevant inferences, novice use of examples or logical reasoning, strategic use examples for reasoning, deductive inferences with major and minor logical coherence, and deductive proof with informal and formal representation. Besides, it was also found that more than half of the students’ proofs coded as inductive schemes, which does not meet the requirement for doing the proof for the proof tasks examined in this study. This study suggests teacher educators in teacher colleges to reform the curriculum regarding proof learning which can accommodate the improvement of student teachers’ proving ability from inductive to deductive proof as well from informal to formal proof.

Advanced Mind-Reading in Adults with Asperger Syndrome

ERIC Educational Resources Information Center

Ponnet, Koen S.; Roeyers, Herbert; Buysse, Ann; De Clercq, Armand; Van Der Heyden, Eva

2004-01-01

This study investigated the mind-reading abilities of 19 adults with Asperger syndrome and 19 typically developing adults. Two static mind-reading tests and a more naturalistic empathic accuracy task were used. In the empathic accuracy task, participants attempted to infer the thoughts and feelings of target persons, while viewing a videotape of…

Automated Discovery of Speech Act Categories in Educational Games

ERIC Educational Resources Information Center

Rus, Vasile; Moldovan, Cristian; Niraula, Nobal; Graesser, Arthur C.

2012-01-01

In this paper we address the important task of automated discovery of speech act categories in dialogue-based, multi-party educational games. Speech acts are important in dialogue-based educational systems because they help infer the student speaker's intentions (the task of speech act classification) which in turn is crucial to providing adequate…

Hysteresis in Mental Workload and Task Performance: The Influence of Demand Transitions and Task Prioritization.

PubMed

Jansen, Reinier J; Sawyer, Ben D; van Egmond, René; de Ridder, Huib; Hancock, Peter A

2016-12-01

We examine how transitions in task demand are manifested in mental workload and performance in a dual-task setting. Hysteresis has been defined as the ongoing influence of demand levels prior to a demand transition. Authors of previous studies predominantly examined hysteretic effects in terms of performance. However, little is known about the temporal development of hysteresis in mental workload. A simulated driving task was combined with an auditory memory task. Participants were instructed to prioritize driving or to prioritize both tasks equally. Three experimental conditions with low, high, and low task demands were constructed by manipulating the frequency of lane changing. Multiple measures of subjective mental workload were taken during experimental conditions. Contrary to our prediction, no hysteretic effects were found after the high- to low-demand transition. However, a hysteretic effect in mental workload was found within the high-demand condition, which degraded toward the end of the high condition. Priority instructions were not reflected in performance. Online assessment of both performance and mental workload demonstrates the transient nature of hysteretic effects. An explanation for the observed hysteretic effect in mental workload is offered in terms of effort regulation. An informed arrival at the scene is important in safety operations, but peaks in mental workload should be avoided to prevent buildup of fatigue. Therefore, communication technologies should incorporate the historical profile of task demand. © 2016, Human Factors and Ergonomics Society.

Portable and Accessible Video Modeling: Teaching a Series of Novel Skills within School and Community Settings

ERIC Educational Resources Information Center

Taber-Doughty, Teresa; Miller, Bridget; Shurr, Jordan; Wiles, Benjamin

2013-01-01

This study examined the effectiveness of self-operated video models on the skill acquisition of a series of novel tasks taught in community-based settings. In addition, the percent of independent task transitions and the duration at which four secondary students with a moderate intellectual disability transitioned between tasks was also examined.…

Adaptive optimal training of animal behavior

NASA Astrophysics Data System (ADS)

Bak, Ji Hyun; Choi, Jung Yoon; Akrami, Athena; Witten, Ilana; Pillow, Jonathan

Neuroscience experiments often require training animals to perform tasks designed to elicit various sensory, cognitive, and motor behaviors. Training typically involves a series of gradual adjustments of stimulus conditions and rewards in order to bring about learning. However, training protocols are usually hand-designed, and often require weeks or months to achieve a desired level of task performance. Here we combine ideas from reinforcement learning and adaptive optimal experimental design to formulate methods for efficient training of animal behavior. Our work addresses two intriguing problems at once: first, it seeks to infer the learning rules underlying an animal's behavioral changes during training; second, it seeks to exploit these rules to select stimuli that will maximize the rate of learning toward a desired objective. We develop and test these methods using data collected from rats during training on a two-interval sensory discrimination task. We show that we can accurately infer the parameters of a learning algorithm that describes how the animal's internal model of the task evolves over the course of training. We also demonstrate by simulation that our method can provide a substantial speedup over standard training methods.

Evaluating data-driven causal inference techniques in noisy physical and ecological systems

NASA Astrophysics Data System (ADS)

Tennant, C.; Larsen, L.

2016-12-01

Causal inference from observational time series challenges traditional approaches for understanding processes and offers exciting opportunities to gain new understanding of complex systems where nonlinearity, delayed forcing, and emergent behavior are common. We present a formal evaluation of the performance of convergent cross-mapping (CCM) and transfer entropy (TE) for data-driven causal inference under real-world conditions. CCM is based on nonlinear state-space reconstruction, and causality is determined by the convergence of prediction skill with an increasing number of observations of the system. TE is the uncertainty reduction based on transition probabilities of a pair of time-lagged variables. With TE, causal inference is based on asymmetry in information flow between the variables. Observational data and numerical simulations from a number of classical physical and ecological systems: atmospheric convection (the Lorenz system), species competition (patch-tournaments), and long-term climate change (Vostok ice core) were used to evaluate the ability of CCM and TE to infer causal-relationships as data series become increasingly corrupted by observational (instrument-driven) or process (model-or -stochastic-driven) noise. While both techniques show promise for causal inference, TE appears to be applicable to a wider range of systems, especially when the data series are of sufficient length to reliably estimate transition probabilities of system components. Both techniques also show a clear effect of observational noise on causal inference. For example, CCM exhibits a negative logarithmic decline in prediction skill as the noise level of the system increases. Changes in TE strongly depend on noise type and which variable the noise was added to. The ability of CCM and TE to detect driving influences suggest that their application to physical and ecological systems could be transformative for understanding driving mechanisms as Earth systems undergo change.

A chain-retrieval model for voluntary task switching.

PubMed

Vandierendonck, André; Demanet, Jelle; Liefooghe, Baptist; Verbruggen, Frederick

2012-09-01

To account for the findings obtained in voluntary task switching, this article describes and tests the chain-retrieval model. This model postulates that voluntary task selection involves retrieval of task information from long-term memory, which is then used to guide task selection and task execution. The model assumes that the retrieved information consists of acquired sequences (or chains) of tasks, that selection may be biased towards chains containing more task repetitions and that bottom-up triggered repetitions may overrule the intended task. To test this model, four experiments are reported. In Studies 1 and 2, sequences of task choices and the corresponding transition sequences (task repetitions or switches) were analyzed with the help of dependency statistics. The free parameters of the chain-retrieval model were estimated on the observed task sequences and these estimates were used to predict autocorrelations of tasks and transitions. In Studies 3 and 4, sequences of hand choices and their transitions were analyzed similarly. In all studies, the chain-retrieval model yielded better fits and predictions than statistical models of event choice. In applications to voluntary task switching (Studies 1 and 2), all three parameters of the model were needed to account for the data. When no task switching was required (Studies 3 and 4), the chain-retrieval model could account for the data with one or two parameters clamped to a neutral value. Implications for our understanding of voluntary task selection and broader theoretical implications are discussed. Copyright © 2012 Elsevier Inc. All rights reserved.

Effective connectivity inferred from fMRI transition dynamics during movie viewing points to a balanced reconfiguration of cortical interactions.

PubMed

Gilson, Matthieu; Deco, Gustavo; Friston, Karl J; Hagmann, Patric; Mantini, Dante; Betti, Viviana; Romani, Gian Luca; Corbetta, Maurizio

2017-10-09

Our behavior entails a flexible and context-sensitive interplay between brain areas to integrate information according to goal-directed requirements. However, the neural mechanisms governing the entrainment of functionally specialized brain areas remain poorly understood. In particular, the question arises whether observed changes in the regional activity for different cognitive conditions are explained by modifications of the inputs to the brain or its connectivity? We observe that transitions of fMRI activity between areas convey information about the tasks performed by 19 subjects, watching a movie versus a black screen (rest). We use a model-based framework that explains this spatiotemporal functional connectivity pattern by the local variability for 66 cortical regions and the network effective connectivity between them. We find that, among the estimated model parameters, movie viewing affects to a larger extent the local activity, which we interpret as extrinsic changes related to the increased stimulus load. However, detailed changes in the effective connectivity preserve a balance in the propagating activity and select specific pathways such that high-level brain regions integrate visual and auditory information, in particular boosting the communication between the two brain hemispheres. These findings speak to a dynamic coordination underlying the functional integration in the brain. Copyright © 2017. Published by Elsevier Inc.

Task switching and response correspondence in the psychological refractory period paradigm

NASA Technical Reports Server (NTRS)

Lien, Mei-Ching; Schweickert, Richard; Proctor, Robert W.

2003-01-01

Three experiments examined the effects of task switching and response correspondence in a psychological refractory period paradigm. A letter task (vowel-consonant) and a digit task (odd-even) were combined to form 4 possible dual-task pairs in each trial: letter-letter, letter-digit, digit-digit, and digit-letter. Foreknowledge of task transition (repeat or switch) and task identity (letter or digit) was varied across experiments: no foreknowledge in Experiment 1, partial foreknowledge (task transition only) in Experiment 2, and full foreknowledge in Experiment 3. For all experiments, the switch cost for Task 2 was additive with stimulus onset asynchrony, and the response-correspondence effect for Task 2 was numerically smaller in the switch condition than in the repeat condition. These outcomes suggest that reconfiguration for Task 2 takes place after the central processing of Task 1 and that the crosstalk correspondence effect is due to response activation by way of stimulus-response associations.

Using Fiction to Assess Mental State Understanding: A New Task for Assessing Theory of Mind in Adults

PubMed Central

Dodell-Feder, David; Lincoln, Sarah Hope; Coulson, Joseph P.; Hooker, Christine I.

2013-01-01

Social functioning depends on the ability to attribute and reason about the mental states of others – an ability known as theory of mind (ToM). Research in this field is limited by the use of tasks in which ceiling effects are ubiquitous, rendering them insensitive to individual differences in ToM ability and instances of subtle ToM impairment. Here, we present data from a new ToM task – the Short Story Task (SST) - intended to improve upon many aspects of existing ToM measures. More specifically, the SST was designed to: (a) assess the full range of individual differences in ToM ability without suffering from ceiling effects; (b) incorporate a range of mental states of differing complexity, including epistemic states, affective states, and intentions to be inferred from a first- and second-order level; (c) use ToM stimuli representative of real-world social interactions; (d) require participants to utilize social context when making mental state inferences; (e) exhibit adequate psychometric properties; and (f) be quick and easy to administer and score. In the task, participants read a short story and were asked questions that assessed explicit mental state reasoning, spontaneous mental state inference, and comprehension of the non-mental aspects of the story. Responses were scored according to a rubric that assigned greater points for accurate mental state attributions that included multiple characters’ mental states. Results demonstrate that the SST is sensitive to variation in ToM ability, can be accurately scored by multiple raters, and exhibits concurrent validity with other social cognitive tasks. The results support the effectiveness of this new measure of ToM in the study of social cognition. The findings are also consistent with studies demonstrating significant relationships among narrative transportation, ToM, and the reading of fiction. Together, the data indicate that reading fiction may be an avenue for improving ToM ability. PMID:24244736

A constructivist connectionist model of transitions on false-belief tasks.

PubMed

Berthiaume, Vincent G; Shultz, Thomas R; Onishi, Kristine H

2013-03-01

How do children come to understand that others have mental representations, e.g., of an object's location? Preschoolers go through two transitions on verbal false-belief tasks, in which they have to predict where an agent will search for an object that was moved in her absence. First, while three-and-a-half-year-olds usually fail at approach tasks, in which the agent wants to find the object, children just under four succeed. Second, only after four do children succeed at tasks in which the agent wants to avoid the object. We present a constructivist connectionist model that autonomously reproduces the two transitions and suggests that the transitions are due to increases in general processing abilities enabling children to (1) overcome a default true-belief attribution by distinguishing false- from true-belief situations, and to (2) predict search in avoidance situations, where there is often more than one correct, empty search location. Constructivist connectionist models are rigorous, flexible and powerful tools that can be analyzed before and after transitions to uncover novel and emergent mechanisms of cognitive development. Copyright © 2012 Elsevier B.V. All rights reserved.

Secure Mass Measurements from Transit Timing: 10 Kepler Exoplanets between 3 and 8 M⊕ with Diverse Densities and Incident Fluxes

NASA Astrophysics Data System (ADS)

Jontof-Hutter, Daniel; Ford, Eric B.; Rowe, Jason F.; Lissauer, Jack J.; Fabrycky, Daniel C.; Van Laerhoven, Christa; Agol, Eric; Deck, Katherine M.; Holczer, Tomer; Mazeh, Tsevi

2016-03-01

We infer dynamical masses in eight multiplanet systems using transit times measured from Kepler's complete data set, including short-cadence data where available. Of the 18 dynamical masses that we infer, 10 pass multiple tests for robustness. These are in systems Kepler-26 (KOI-250), Kepler-29 (KOI-738), Kepler-60 (KOI-2086), Kepler-105 (KOI-115), and Kepler-307 (KOI-1576). Kepler-105 c has a radius of 1.3 R⊕ and a density consistent with an Earth-like composition. Strong transit timing variation (TTV) signals were detected from additional planets, but their inferred masses were sensitive to outliers or consistent solutions could not be found with independently measured transit times, including planets orbiting Kepler-49 (KOI-248), Kepler-57 (KOI-1270), Kepler-105 (KOI-115), and Kepler-177 (KOI-523). Nonetheless, strong upper limits on the mass of Kepler-177 c imply an extremely low density of ˜0.1 g cm-3. In most cases, individual orbital eccentricities were poorly constrained owing to degeneracies in TTV inversion. For five planet pairs in our sample, strong secular interactions imply a moderate to high likelihood of apsidal alignment over a wide range of possible eccentricities. We also find solutions for the three planets known to orbit Kepler-60 in a Laplace-like resonance chain. However, nonlibrating solutions also match the transit timing data. For six systems, we calculate more precise stellar parameters than previously known, enabling useful constraints on planetary densities where we have secure mass measurements. Placing these exoplanets on the mass-radius diagram, we find that a wide range of densities is observed among sub-Neptune-mass planets and that the range in observed densities is anticorrelated with incident flux.

Discovering sparse transcription factor codes for cell states and state transitions during development

PubMed Central

Furchtgott, Leon A; Melton, Samuel; Menon, Vilas; Ramanathan, Sharad

2017-01-01

Computational analysis of gene expression to determine both the sequence of lineage choices made by multipotent cells and to identify the genes influencing these decisions is challenging. Here we discover a pattern in the expression levels of a sparse subset of genes among cell types in B- and T-cell developmental lineages that correlates with developmental topologies. We develop a statistical framework using this pattern to simultaneously infer lineage transitions and the genes that determine these relationships. We use this technique to reconstruct the early hematopoietic and intestinal developmental trees. We extend this framework to analyze single-cell RNA-seq data from early human cortical development, inferring a neocortical-hindbrain split in early progenitor cells and the key genes that could control this lineage decision. Our work allows us to simultaneously infer both the identity and lineage of cell types as well as a small set of key genes whose expression patterns reflect these relationships. DOI: http://dx.doi.org/10.7554/eLife.20488.001 PMID:28296636

Chironomid-based reconstructions of summer air temperature from lake deposits in Lyndon Stream, New Zealand spanning the MIS 3/2 transition

NASA Astrophysics Data System (ADS)

Woodward, C. A.; Shulmeister, J.

2007-01-01

We present chironomid-based temperature reconstructions from lake sediments deposited between ca 26,600 cal yr BP and 24,500 cal yr BP from Lyndon Stream, South Island, New Zealand. Summer (February mean) temperatures averaged 1 °C cooler, with a maximum inferred cooling of 3.7 °C. These estimates corroborate macrofossil and beetle-based temperature inferences from the same site and suggest climate amelioration (an interstadial) at this time. Other records from the New Zealand region also show a large degree of variability during the late Otiran glacial sequence (34,000-18,000 cal yr BP) including a phase of warming at the MIS 2/3 transition and a maximum cooling that did not occur until the global LGM (ca 20,000 cal yr BP). The very moderate cooling identified here at the MIS 2/3 transition confirms and enhances the long-standing discrepancy in New Zealand records between pollen and other proxies. Low abundances (<20%) of canopy tree pollen in records from late MIS 3 to the end of MIS 2 cannot be explained by the minor (<5 °C) cooling inferred from this and other studies unless other environmental parameters are considered. Further work is required to address this critical issue.

Time-varying coupling functions: Dynamical inference and cause of synchronization transitions

NASA Astrophysics Data System (ADS)

Stankovski, Tomislav

2017-02-01

Interactions in nature can be described by their coupling strength, direction of coupling, and coupling function. The coupling strength and directionality are relatively well understood and studied, at least for two interacting systems; however, there can be a complexity in the interactions uniquely dependent on the coupling functions. Such a special case is studied here: synchronization transition occurs only due to the time variability of the coupling functions, while the net coupling strength is constant throughout the observation time. To motivate the investigation, an example is used to present an analysis of cross-frequency coupling functions between delta and alpha brain waves extracted from the electroencephalography recording of a healthy human subject in a free-running resting state. The results indicate that time-varying coupling functions are a reality for biological interactions. A model of phase oscillators is used to demonstrate and detect the synchronization transition caused by the varying coupling functions during an invariant coupling strength. The ability to detect this phenomenon is discussed with the method of dynamical Bayesian inference, which was able to infer the time-varying coupling functions. The form of the coupling function acts as an additional dimension for the interactions, and it should be taken into account when detecting biological or other interactions from data.

Measuring the Number of M Dwarfs per M Dwarf Using Kepler Eclipsing Binaries

NASA Astrophysics Data System (ADS)

Shan, Yutong; Johnson, John A.; Morton, Timothy D.

2015-11-01

We measure the binarity of detached M dwarfs in the Kepler field with orbital periods in the range of 1-90 days. Kepler’s photometric precision and nearly continuous monitoring of stellar targets over time baselines ranging from 3 months to 4 years make its detection efficiency for eclipsing binaries nearly complete over this period range and for all radius ratios. Our investigation employs a statistical framework akin to that used for inferring planetary occurrence rates from planetary transits. The obvious simplification is that eclipsing binaries have a vastly improved detection efficiency that is limited chiefly by their geometric probabilities to eclipse. For the M-dwarf sample observed by the Kepler Mission, the fractional incidence of eclipsing binaries implies that there are {0.11}-0.04+0.02 close stellar companions per apparently single M dwarf. Our measured binarity is higher than previous inferences of the occurrence rate of close binaries via radial velocity techniques, at roughly the 2σ level. This study represents the first use of eclipsing binary detections from a high quality transiting planet mission to infer binary statistics. Application of this statistical framework to the eclipsing binaries discovered by future transit surveys will establish better constraints on short-period M+M binary rate, as well as binarity measurements for stars of other spectral types.

Not Noisy, Just Wrong: The Role of Suboptimal Inference in Behavioral Variability

PubMed Central

Beck, Jeffrey M.; Ma, Wei Ji; Pitkow, Xaq; Latham, Peter E.; Pouget, Alexandre

2015-01-01

Behavior varies from trial to trial even when the stimulus is maintained as constant as possible. In many models, this variability is attributed to noise in the brain. Here, we propose that there is another major source of variability: suboptimal inference. Importantly, we argue that in most tasks of interest, and particularly complex ones, suboptimal inference is likely to be the dominant component of behavioral variability. This perspective explains a variety of intriguing observations, including why variability appears to be larger on the sensory than on the motor side, and why our sensors are sometimes surprisingly unreliable. PMID:22500627

Kepler Transit Depths Contaminated By a Phantom Star

NASA Astrophysics Data System (ADS)

Dalba, Paul A.; Muirhead, Philip S.; Croll, Bryce; Kempton, Eliza M.-R.

2017-02-01

We present ground-based observations from the Discovery Channel Telescope (DCT) of three transits of Kepler-445c—a supposed super-Earth exoplanet with properties resembling GJ 1214b—and demonstrate that the transit depth is ˜50% shallower than the depth previously inferred from Kepler spacecraft data. The resulting decrease in planetary radius significantly alters the interpretation of the exoplanet’s bulk composition. Despite the faintness of the M4 dwarf host star, our ground-based photometry clearly recovers each transit and achieves repeatable 1σ precision of ˜0.2% (2 millimags). The transit parameters estimated from the DCT data are discrepant with those inferred from the Kepler data to at least 17σ confidence. This inconsistency is due to a subtle miscalculation of the stellar crowding metric during the Kepler pre-search data conditioning (PDC). The crowding metric, or CROWDSAP, is contaminated by a non-existent phantom star originating in the USNO-B1 catalog and inherited by the Kepler Input Catalog (KIC). Phantom stars in the KIC are likely rare, but they have the potential to affect statistical studies of Kepler targets that use the PDC transit depths for a large number of exoplanets where an individual follow-up observation of each is not possible. The miscalculation of Kepler-445c’s transit depth emphasizes the importance of stellar crowding in the Kepler data, and provides a cautionary tale for the analysis of data from the Transiting Exoplanet Survey Satellite, which will have even larger pixels than Kepler.

Segmental transition of the first syllables of words in Japanese children who stutter: Comparison between word and sentence production.

PubMed

Matsumoto, Sachiyo; Ito, Tomohiko

2016-01-01

Matsumoto-Shimamori, Ito, Fukuda, & Fukuda (2011) proposed the hypothesis that in Japanese, the transition from the core vowels (i.e. syllable nucleus) of the first syllables of words to the following segments affected the occurrence of stuttering. Moreover, in this transition position, an inter-syllabic transition precipitated more stuttering than an intra-syllabic one (Shimamori & Ito, 2007, 2008). However, these studies have only used word production tasks. The purpose of this study was to investigate whether the same results could be obtained in sentence production tasks. Participants were 28 Japanese school-age children who stutter, ranging in age from 7;3 to 12;7. The frequency of stuttering on words with an inter-syllabic transition was significantly higher than on those having an intra-syllabic transition, not only in isolated words but in the first words of sentences. These results suggested that Matsumoto et al.'s hypothesis could be applicable to the results of sentence production tasks.

Sensitivity to Lateral Information on a Perceptual Word Identification Task in French Third and Fifth Graders

ERIC Educational Resources Information Center

Khelifi, Rachid; Sparrow, Laurent; Casalis, Severine

2012-01-01

This study aimed at examining sensitivity to lateral linguistic and nonlinguistic information in third and fifth grade readers. A word identification task with a threshold was used, and targets were displayed foveally with or without distractors. Sensitivity to lateral information was inferred from the deterioration of the rate of correct word…

On the Locus of Speed-Accuracy Trade-Off in Reaction Time: Inferences From the Lateralized Readiness Potential

ERIC Educational Resources Information Center

Rinkenauer, Gerhard; Osman, Allen; Ulrich, Rolf; Muller-Gethmann, Hiltraut; Mattes, Stefan

2004-01-01

Lateralized readiness potentials (LRPs) were used to determine the stage(s) of reaction time (RT) responsible for speed-accuracy trade-offs (SATs). Speeded decisions based on several types of information were examined in 3 experiments, involving, respectively, a line discrimination task, lexical decisions, and an Erikson flanker task. Three levels…

Spatial Reasoning with External Visualizations: What Matters Is What You See, Not whether You Interact

ERIC Educational Resources Information Center

Keehner, Madeleine; Hegarty, Mary; Cohen, Cheryl; Khooshabeh, Peter; Montello, Daniel R.

2008-01-01

Three experiments examined the effects of interactive visualizations and spatial abilities on a task requiring participants to infer and draw cross sections of a three-dimensional (3D) object. The experiments manipulated whether participants could interactively control a virtual 3D visualization of the object while performing the task, and…

Ventromedial Prefrontal Cortex Is Necessary for Normal Associative Inference and Memory Integration.

PubMed

Spalding, Kelsey N; Schlichting, Margaret L; Zeithamova, Dagmar; Preston, Alison R; Tranel, Daniel; Duff, Melissa C; Warren, David E

2018-04-11

The ability to flexibly combine existing knowledge in response to novel circumstances is highly adaptive. However, the neural correlates of flexible associative inference are not well characterized. Laboratory tests of associative inference have measured memory for overlapping pairs of studied items (e.g., AB, BC) and for nonstudied pairs with common associates (i.e., AC). Findings from functional neuroimaging and neuropsychology suggest the ventromedial prefrontal cortex (vmPFC) may be necessary for associative inference. Here, we used a neuropsychological approach to test the necessity of vmPFC for successful memory-guided associative inference in humans using an overlapping pairs associative memory task. We predicted that individuals with focal vmPFC damage ( n = 5; 3F, 2M) would show impaired inferential memory but intact non-inferential memory. Performance was compared with normal comparison participants ( n = 10; 6F, 4M). Participants studied pairs of visually presented objects including overlapping pairs (AB, BC) and nonoverlapping pairs (XY). Participants later completed a three-alternative forced-choice recognition task for studied pairs (AB, BC, XY) and inference pairs (AC). As predicted, the vmPFC group had intact memory for studied pairs but significantly impaired memory for inferential pairs. These results are consistent with the perspective that the vmPFC is necessary for memory-guided associative inference, indicating that the vmPFC is critical for adaptive abilities that require application of existing knowledge to novel circumstances. Additionally, vmPFC damage was associated with unexpectedly reduced memory for AB pairs post-inference, which could potentially reflect retroactive interference. Together, these results reinforce an emerging understanding of a role for the vmPFC in brain networks supporting associative memory processes. SIGNIFICANCE STATEMENT We live in a constantly changing environment, so the ability to adapt our knowledge to support understanding of new circumstances is essential. One important adaptive ability is associative inference which allows us to extract shared features from distinct experiences and relate them. For example, if we see a woman holding a baby, and later see a man holding the same baby, then we might infer that the two adults are a couple. Despite the importance of associative inference, the brain systems necessary for this ability are not known. Here, we report that damage to human ventromedial prefrontal cortex (vmPFC) disproportionately impairs associative inference. Our findings show the necessity of the vmPFC for normal associative inference and memory integration. Copyright © 2018 the authors 0270-6474/18/383767-09$15.00/0.

Effects of metacognitive exercise on the development of scientific reasoning

NASA Astrophysics Data System (ADS)

Pearsall, Susan Helen

1999-12-01

The essence of scientific reasoning is the coordination of theory and evidence. Kuhn (1998) implicates metacognitive skill in reflecting on one's theories, and metastrategic management of inferential strategies involved in their coordination with evidence, as critical to scientific reasoning. The present study investigates how metacognitive exercise may improve children's performance on scientific reasoning tasks. Two groups composed of 50 fifth- and sixth-grade students engaged in a scientific investigation activity over six weekly sessions. Additionally, one group engaged in an activity involving reflection on others' performance on the task, while the comparison group spent equivalent time on an unrelated task. Children in the experimental condition showed significant improvement (relative to those in the comparison condition) in terms of the sequence identified by Kuhn et al. (1995), from entirely theory-based reasoning to the skilled coordination of theory and evidence. This improvement, however, was confined to the beginning levels in the sequence. Specifically, experimental subjects attended to and employed evidence in their reasoning to a greater extent than did comparison subjects, even though their inferences remained largely invalid. Also, experimental subjects showed a marginally significant trend toward greater use of multiple-instance evidence (which, unlike single instance evidence, allowed them to make comparative inferences). On a delayed posttest, children in the experimental condition continued to use evidence to justify their inferences to a significantly greater extent than the comparison group. In contrast, performance increased only slightly on a transfer task and group differences were largely nonsignificant. At the delayed posttest, experimental children's level of performance on the metacognitive activity was compared to their performance on the main task. Results indicated that when children were not consistent on both tasks, performance was superior on the metacognitive task. This finding points to a utilization deficiency. Future research should focus on helping children close the gap between knowing strategies and using them. Like the main findings documenting the benefits of enhancing metacognitive awareness, identification of this gap points to the critical role of metacognitive awareness and understanding in skilled thinking.

Are there two processes in reasoning? The dimensionality of inductive and deductive inferences.

PubMed

Stephens, Rachel G; Dunn, John C; Hayes, Brett K

2018-03-01

Single-process accounts of reasoning propose that the same cognitive mechanisms underlie inductive and deductive inferences. In contrast, dual-process accounts propose that these inferences depend upon 2 qualitatively different mechanisms. To distinguish between these accounts, we derived a set of single-process and dual-process models based on an overarching signal detection framework. We then used signed difference analysis to test each model against data from an argument evaluation task, in which induction and deduction judgments are elicited for sets of valid and invalid arguments. Three data sets were analyzed: data from Singmann and Klauer (2011), a database of argument evaluation studies, and the results of an experiment designed to test model predictions. Of the large set of testable models, we found that almost all could be rejected, including all 2-dimensional models. The only testable model able to account for all 3 data sets was a model with 1 dimension of argument strength and independent decision criteria for induction and deduction judgments. We conclude that despite the popularity of dual-process accounts, current results from the argument evaluation task are best explained by a single-process account that incorporates separate decision thresholds for inductive and deductive inferences. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Effects of morphine and naloxone on feline colonic transit

DOE Office of Scientific and Technical Information (OSTI.GOV)

Krevsky, B.; Libster, B.; Maurer, A.H.

1989-01-01

The effects of endogenous and exogenous opioid substances on feline colonic transit were evaluated using colonic transit scintigraphy. Naloxone accelerated emptying of the cecum and ascending colon, and filling of the transverse colon. Endogenous opioid peptides thus appear to play a significant role in the regulation of colonic transit. At a moderate dose of morphine cecum and ascending colon transit was accelerated, while at a larger dose morphine had no effect. Since naloxone, a relatively nonspecific opioid antagonist, and morphine, a principally mu opioid receptor agonist, both accelerate proximal colonic transit, a decelerating role for at least one of themore » other opioid receptors is inferred.« less

Integrated Design of a Telerobotic Workstation

NASA Technical Reports Server (NTRS)

Rochlis, Jennifer L.; Clarke, John-Paul

2001-01-01

The experiments described in this paper are part of a larger joint MIT/NASA research effort that focuses on the development of a methodology for designing and evaluating integrated interfaces for highly dexterous and multi-functional telerobots. Specifically, a telerobotic workstation is being designed for an Extravehicular Activity (EVA) anthropomorphic space station telerobot. Previous researchers have designed telerobotic workstations based upon performance of discrete subsets of tasks (for example, peg-in-hole, tracking, etc.) without regard for transitions that operators go through between tasks performed sequentially in the context of larger integrated tasks. The exploratory research experiments presented here took an integrated approach and assessed how subjects operating a full-immersion telerobot perform during the transitions between sub-tasks of two common EVA tasks. Preliminary results show that up to 30% of total task time is spent gaining and maintaining Situation Awareness (SA) of their task space and environment during transitions. Although task performance improves over the two trial days, the percentage of time spent on SA remains the same. This method identifies areas where workstation displays and feedback mechanisms are most needed to increase operator performance and decrease operator workload - areas that previous research methods have not been able to address.

Different Patterns of Theory of Mind Impairment in Mild Cognitive Impairment.

PubMed

Moreau, Noémie; Rauzy, Stéphane; Bonnefoi, Bernadette; Renié, Laurent; Martinez-Almoyna, Laurent; Viallet, François; Champagne-Lavau, Maud

2015-01-01

Theory of Mind refers to the ability to infer other’s mental states, their beliefs, intentions, or knowledge. To date, only two studies have reported the presence of Theory of Mind impairment in mild cognitive impairment (MCI). In the present study,we evaluated 20 MCI patients and compared them with 25 healthy control participants using two Theory of Mind tasks. The first task was a false belief paradigm as frequently used in the literature, and the second one was a referential communication task,assessing Theory of Mind in a real situation of interaction and which had never been used before in this population. The results showed that MCI patients presented difficulties inferring another person’s beliefs about reality and attributing knowledge to them in a situation of real-life interaction. Two different patterns of Theory of Mind emerged among the patients. In comparison with the control group, some MCI patients demonstrated impairment only in the interaction task and presented isolated episodicmemory impairment, while others were impaired in both Theory of Mind tasks and presented cognitive impairment impacting both episodic memory and executive functioning. Theory of Mind is thus altered in the very early stages of cognitive impairment even in real social interaction, which could impact precociously relationships in daily life.

Pitting intuitive and analytical thinking against each other: the case of transitivity.

PubMed

Rusou, Zohar; Zakay, Dan; Usher, Marius

2013-06-01

Identifying which thinking mode, intuitive or analytical, yields better decisions has been a major subject of inquiry by decision-making researchers. Yet studies show contradictory results. One possibility is that the ambiguity is due to the variability in experimental conditions across studies. Our hypothesis is that decision quality depends critically on the level of compatibility between the thinking mode employed in the decision and the nature of the decision-making task. In two experiments, we pitted intuition and analytical thinking against each other on tasks that were either mainly intuitive or mainly analytical. Thinking modes, as well as task characteristics, were manipulated in a factorial design, with choice transitivity as the dependent measure. Results showed higher choice consistency (transitivity) when thinking mode and the characteristics of the decision task were compatible.

Mental State Inferences Abilities Contribution to Verbal Irony Comprehension in Older Adults with Mild Cognitive Impairment

PubMed Central

Gaudreau, G.; Monetta, L.; Macoir, J.; Poulin, S.; Laforce, R. Jr.; Hudon, C.

2015-01-01

Objective. The present study examined mentalizing capacities as well as the relative implication of mentalizing in the comprehension of ironic and sincere assertions among 30 older adults with mild cognitive impairment (MCI) and 30 healthy control (HC) subjects. Method. Subjects were administered a task evaluating mentalizing by means of short stories. A verbal irony comprehension task, in which participants had to identify ironic or sincere statements within short stories, was also administered; the design of the task allowed uniform implication of mentalizing across the conditions. Results. Findings indicated that participants with MCI have second-order mentalizing difficulties compared to HC subjects. Moreover, MCI participants were impaired compared to the HC group in identifying ironic or sincere stories, both requiring mental inference capacities. Conclusion. This study suggests that, in individuals with MCI, difficulties in the comprehension of ironic and sincere assertions are closely related to second-order mentalizing deficits. These findings support previous data suggesting a strong relationship between irony comprehension and mentalizing. PMID:26199459

Integrating Conceptual Knowledge Within and Across Representational Modalities

PubMed Central

McNorgan, Chris; Reid, Jackie; McRae, Ken

2011-01-01

Research suggests that concepts are distributed across brain regions specialized for processing information from different sensorimotor modalities. Multimodal semantic models fall into one of two broad classes differentiated by the assumed hierarchy of convergence zones over which information is integrated. In shallow models, communication within- and between-modality is accomplished using either direct connectivity, or a central semantic hub. In deep models, modalities are connected via cascading integration sites with successively wider receptive fields. Four experiments provide the first direct behavioral tests of these models using speeded tasks involving feature inference and concept activation. Shallow models predict no within-modal versus cross-modal difference in either task, whereas deep models predict a within-modal advantage for feature inference, but a cross-modal advantage for concept activation. Experiments 1 and 2 used relatedness judgments to tap participants’ knowledge of relations for within- and cross-modal feature pairs. Experiments 3 and 4 used a dual feature verification task. The pattern of decision latencies across Experiments 1 to 4 is consistent with a deep integration hierarchy. PMID:21093853

[Social exchange and inference: an experimental study with the Wason selection task].

PubMed

Hayashi, N

2001-04-01

Social contract theory (Cosmides, 1989) posits that the human mind was equipped with inference faculty specialized for cheater detection. Cosmides (1989) conducted a series of experiments employing the Wason selection task to demonstrate that her social contract theory could account for the content effects reported in the literature. The purpose of this study was to investigate the possibility that the results were due to experimental artifacts. In the current experiment, the subject was given two versions of the Wason task that contained no social exchange context, but included an instruction implying him/her to look for something, together with the cassava root and the abstract versions used by Cosmides (1989). Results showed that the two versions with no social exchange context produced the same response pattern observed in the original study. It may be concluded that the subject's perception of the rule as a social contract was not necessary to obtain the original results, and that an instruction implying that he/she should look for something was sufficient.

The cerebellum and decision making under uncertainty.

PubMed

Blackwood, Nigel; Ffytche, Dominic; Simmons, Andrew; Bentall, Richard; Murray, Robin; Howard, Robert

2004-06-01

This study aimed to identify the neural basis of probabilistic reasoning, a type of inductive inference that aids decision making under conditions of uncertainty. Eight normal subjects performed two separate two-alternative-choice tasks (the balls in a bottle and personality survey tasks) while undergoing functional magnetic resonance imaging (fMRI). The experimental conditions within each task were chosen so that they differed only in their requirement to make a decision under conditions of uncertainty (probabilistic reasoning and frequency determination required) or under conditions of certainty (frequency determination required). The same visual stimuli and motor responses were used in the experimental conditions. We provide evidence that the neo-cerebellum, in conjunction with the premotor cortex, inferior parietal lobule and medial occipital cortex, mediates the probabilistic inferences that guide decision making under uncertainty. We hypothesise that the neo-cerebellum constructs internal working models of uncertain events in the external world, and that such probabilistic models subserve the predictive capacity central to induction. Copyright 2004 Elsevier B.V.

Bayesian Action–Perception Computational Model: Interaction of Production and Recognition of Cursive Letters

PubMed Central

Gilet, Estelle; Diard, Julien; Bessière, Pierre

2011-01-01

In this paper, we study the collaboration of perception and action representations involved in cursive letter recognition and production. We propose a mathematical formulation for the whole perception–action loop, based on probabilistic modeling and Bayesian inference, which we call the Bayesian Action–Perception (BAP) model. Being a model of both perception and action processes, the purpose of this model is to study the interaction of these processes. More precisely, the model includes a feedback loop from motor production, which implements an internal simulation of movement. Motor knowledge can therefore be involved during perception tasks. In this paper, we formally define the BAP model and show how it solves the following six varied cognitive tasks using Bayesian inference: i) letter recognition (purely sensory), ii) writer recognition, iii) letter production (with different effectors), iv) copying of trajectories, v) copying of letters, and vi) letter recognition (with internal simulation of movements). We present computer simulations of each of these cognitive tasks, and discuss experimental predictions and theoretical developments. PMID:21674043

Cerebellarlike corrective model inference engine for manipulation tasks.

PubMed

Luque, Niceto Rafael; Garrido, Jesús Alberto; Carrillo, Richard Rafael; Coenen, Olivier J-M D; Ros, Eduardo

2011-10-01

This paper presents how a simple cerebellumlike architecture can infer corrective models in the framework of a control task when manipulating objects that significantly affect the dynamics model of the system. The main motivation of this paper is to evaluate a simplified bio-mimetic approach in the framework of a manipulation task. More concretely, the paper focuses on how the model inference process takes place within a feedforward control loop based on the cerebellar structure and on how these internal models are built up by means of biologically plausible synaptic adaptation mechanisms. This kind of investigation may provide clues on how biology achieves accurate control of non-stiff-joint robot with low-power actuators which involve controlling systems with high inertial components. This paper studies how a basic temporal-correlation kernel including long-term depression (LTD) and a constant long-term potentiation (LTP) at parallel fiber-Purkinje cell synapses can effectively infer corrective models. We evaluate how this spike-timing-dependent plasticity correlates sensorimotor activity arriving through the parallel fibers with teaching signals (dependent on error estimates) arriving through the climbing fibers from the inferior olive. This paper addresses the study of how these LTD and LTP components need to be well balanced with each other to achieve accurate learning. This is of interest to evaluate the relevant role of homeostatic mechanisms in biological systems where adaptation occurs in a distributed manner. Furthermore, we illustrate how the temporal-correlation kernel can also work in the presence of transmission delays in sensorimotor pathways. We use a cerebellumlike spiking neural network which stores the corrective models as well-structured weight patterns distributed among the parallel fibers to Purkinje cell connections.

NIMEFI: gene regulatory network inference using multiple ensemble feature importance algorithms.

PubMed

Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

2014-01-01

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available.

On the inherent competition between valid and spurious inductive inferences in Boolean data

NASA Astrophysics Data System (ADS)

Andrecut, M.

Inductive inference is the process of extracting general rules from specific observations. This problem also arises in the analysis of biological networks, such as genetic regulatory networks, where the interactions are complex and the observations are incomplete. A typical task in these problems is to extract general interaction rules as combinations of Boolean covariates, that explain a measured response variable. The inductive inference process can be considered as an incompletely specified Boolean function synthesis problem. This incompleteness of the problem will also generate spurious inferences, which are a serious threat to valid inductive inference rules. Using random Boolean data as a null model, here we attempt to measure the competition between valid and spurious inductive inference rules from a given data set. We formulate two greedy search algorithms, which synthesize a given Boolean response variable in a sparse disjunct normal form, and respectively a sparse generalized algebraic normal form of the variables from the observation data, and we evaluate numerically their performance.

Experimental evidence for circular inference in schizophrenia

PubMed Central

Jardri, Renaud; Duverne, Sandrine; Litvinova, Alexandra S; Denève, Sophie

2017-01-01

Schizophrenia (SCZ) is a complex mental disorder that may result in some combination of hallucinations, delusions and disorganized thinking. Here SCZ patients and healthy controls (CTLs) report their level of confidence on a forced-choice task that manipulated the strength of sensory evidence and prior information. Neither group's responses can be explained by simple Bayesian inference. Rather, individual responses are best captured by a model with different degrees of circular inference. Circular inference refers to a corruption of sensory data by prior information and vice versa, leading us to ‘see what we expect' (through descending loops), to ‘expect what we see' (through ascending loops) or both. Ascending loops are stronger for SCZ than CTLs and correlate with the severity of positive symptoms. Descending loops correlate with the severity of negative symptoms. Both loops correlate with disorganized symptoms. The findings suggest that circular inference might mediate the clinical manifestations of SCZ. PMID:28139642

Experimental evidence for circular inference in schizophrenia.

PubMed

Jardri, Renaud; Duverne, Sandrine; Litvinova, Alexandra S; Denève, Sophie

2017-01-31

Schizophrenia (SCZ) is a complex mental disorder that may result in some combination of hallucinations, delusions and disorganized thinking. Here SCZ patients and healthy controls (CTLs) report their level of confidence on a forced-choice task that manipulated the strength of sensory evidence and prior information. Neither group's responses can be explained by simple Bayesian inference. Rather, individual responses are best captured by a model with different degrees of circular inference. Circular inference refers to a corruption of sensory data by prior information and vice versa, leading us to 'see what we expect' (through descending loops), to 'expect what we see' (through ascending loops) or both. Ascending loops are stronger for SCZ than CTLs and correlate with the severity of positive symptoms. Descending loops correlate with the severity of negative symptoms. Both loops correlate with disorganized symptoms. The findings suggest that circular inference might mediate the clinical manifestations of SCZ.

Experimental evidence for circular inference in schizophrenia

NASA Astrophysics Data System (ADS)

Jardri, Renaud; Duverne, Sandrine; Litvinova, Alexandra S.; Denève, Sophie

2017-01-01

Schizophrenia (SCZ) is a complex mental disorder that may result in some combination of hallucinations, delusions and disorganized thinking. Here SCZ patients and healthy controls (CTLs) report their level of confidence on a forced-choice task that manipulated the strength of sensory evidence and prior information. Neither group's responses can be explained by simple Bayesian inference. Rather, individual responses are best captured by a model with different degrees of circular inference. Circular inference refers to a corruption of sensory data by prior information and vice versa, leading us to `see what we expect' (through descending loops), to `expect what we see' (through ascending loops) or both. Ascending loops are stronger for SCZ than CTLs and correlate with the severity of positive symptoms. Descending loops correlate with the severity of negative symptoms. Both loops correlate with disorganized symptoms. The findings suggest that circular inference might mediate the clinical manifestations of SCZ.

Classification versus inference learning contrasted with real-world categories.

PubMed

Jones, Erin L; Ross, Brian H

2011-07-01

Categories are learned and used in a variety of ways, but the research focus has been on classification learning. Recent work contrasting classification with inference learning of categories found important later differences in category performance. However, theoretical accounts differ on whether this is due to an inherent difference between the tasks or to the implementation decisions. The inherent-difference explanation argues that inference learners focus on the internal structure of the categories--what each category is like--while classification learners focus on diagnostic information to predict category membership. In two experiments, using real-world categories and controlling for earlier methodological differences, inference learners learned more about what each category was like than did classification learners, as evidenced by higher performance on a novel classification test. These results suggest that there is an inherent difference between learning new categories by classifying an item versus inferring a feature.

78 FR 31542 - FCC Technology Transitions Policy Task Force Seeks Comment on Potential Trials

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-24

... communications alternatives to customers living in particularly high-cost areas, including its Mobile Premises... FEDERAL COMMUNICATIONS COMMISSION [GN Docket No. 13-5; DA 13-1016] FCC Technology Transitions Policy Task Force Seeks Comment on Potential Trials AGENCY: Federal Communications Commission. ACTION...

Unbounding the mental number line—new evidence on children's spatial representation of numbers

PubMed Central

Link, Tanja; Huber, Stefan; Nuerk, Hans-Christoph; Moeller, Korbinian

2014-01-01

Number line estimation (i.e., indicating the position of a given number on a physical line) is a standard assessment of children's spatial representation of number magnitude. Importantly, there is an ongoing debate on the question in how far the bounded task version with start and endpoint given (e.g., 0 and 100) might induce specific estimation strategies and thus may not allow for unbiased inferences on the underlying representation. Recently, a new unbounded version of the task was suggested with only the start point and a unit fixed (e.g., the distance from 0 to 1). In adults this task provided a less biased index of the spatial representation of number magnitude. Yet, so far there are no children data available for the unbounded number line estimation task. Therefore, we conducted a cross-sectional study on primary school children performing both, the bounded and the unbounded version of the task. We observed clear evidence for systematic strategic influences (i.e., the consideration of reference points) in the bounded number line estimation task for children older than grade two whereas there were no such indications for the unbounded version for any one of the age groups. In summary, the current data corroborate the unbounded number line estimation task to be a valuable tool for assessing children's spatial representation of number magnitude in a systematic and unbiased manner. Yet, similar results for the bounded and the unbounded version of the task for first- and second-graders may indicate that both versions of the task might assess the same underlying representation for relatively younger children—at least in number ranges familiar to the children assessed. This is of particular importance for inferences about the nature and development of children's magnitude representation. PMID:24478734

Inference of Evolutionary Jumps in Large Phylogenies using Lévy Processes

PubMed Central

Duchen, Pablo; Leuenberger, Christoph; Szilágyi, Sándor M.; Harmon, Luke; Eastman, Jonathan; Schweizer, Manuel

2017-01-01

Abstract Although it is now widely accepted that the rate of phenotypic evolution may not necessarily be constant across large phylogenies, the frequency and phylogenetic position of periods of rapid evolution remain unclear. In his highly influential view of evolution, G. G. Simpson supposed that such evolutionary jumps occur when organisms transition into so-called new adaptive zones, for instance after dispersal into a new geographic area, after rapid climatic changes, or following the appearance of an evolutionary novelty. Only recently, large, accurate and well calibrated phylogenies have become available that allow testing this hypothesis directly, yet inferring evolutionary jumps remains computationally very challenging. Here, we develop a computationally highly efficient algorithm to accurately infer the rate and strength of evolutionary jumps as well as their phylogenetic location. Following previous work we model evolutionary jumps as a compound process, but introduce a novel approach to sample jump configurations that does not require matrix inversions and thus naturally scales to large trees. We then make use of this development to infer evolutionary jumps in Anolis lizards and Loriinii parrots where we find strong signal for such jumps at the basis of clades that transitioned into new adaptive zones, just as postulated by Simpson’s hypothesis. [evolutionary jump; Lévy process; phenotypic evolution; punctuated equilibrium; quantitative traits. PMID:28204787

The Case for a Dual-Process Theory of Transitive Reasoning

ERIC Educational Resources Information Center

Wright, Barlow C.

2012-01-01

Ever since its popularisation by Piaget around 60 years ago, transitive reasoning (deductively-inferring A greater than C from premises A greater than B and B greater than C) has been of psychological interest both as a mental phenomenon and as a tool in areas of psychological discourse. However, the focus of interest in it has shifted…

What Does Ipsilateral Delay Activity Reflect? Inferences from Slow Potentials in a Lateralized Visual Working Memory Task

ERIC Educational Resources Information Center

Arend, Anna M.; Zimmer, Hubert D.

2011-01-01

In the lateralized change detection task, two item arrays are presented, one on each side of the display. Participants have to remember the items in the relevant hemifield and ignore the items in the irrelevant hemifield. A difference wave between contralateral and ipsilateral slow potentials with respect to the relevant items, the contralateral…

The Development of Categorization: Effects of Classification and Inference Training on Category Representation

PubMed Central

Deng, Wei (Sophia); Sloutsky, Vladimir M.

2015-01-01

Does category representation change in the course of development? And if so, how and why? The current study attempted to answer these questions by examining category learning and category representation. In Experiment 1, 4-year-olds, 6-year-olds, and adults were trained with either a classification task or an inference task and their categorization performance and memory for items were tested. Adults and 6-year-olds exhibited an important asymmetry: they relied on a single deterministic feature during classification training, but not during inference training. In contrast, regardless of the training condition, 4-year-olds relied on multiple probabilistic features. In Experiment 2, 4-year-olds were presented with classification training and their attention was explicitly directed to the deterministic feature. Under this condition, their categorization performance was similar to that of older participants in Experiment 1, yet their memory performance pointed to a similarity-based representation, which was similar to that of 4-year-olds in Experiment 1. These results are discussed in relation to theories of categorization and the role of selective attention in the development of category learning. PMID:25602938

Potential development of an intercity passenger transit system in Texas : report on tasks 1-5.

DOT National Transportation Integrated Search

2009-11-01

This report summarizes the results of Tasks 1 through 5 of TxDOT Research Project 0-5930: Potential : Development of an Intercity Passenger Transit System in Texas. Rather than focus on any regional : commuter or light rail systems within or radiatin...

Noise Impact Inventory of Elevated Structures in U.S. Urban Rail Rapid Transit Systems

DOT National Transportation Integrated Search

1980-09-01

This report presents the results of the third task of a five-task program dealing with the reduction of noise from elevated structures in use in U.S. rail rapid transit systems. This report is an inventory and impact assessment of the noise radiated ...

Hebbian Wiring Plasticity Generates Efficient Network Structures for Robust Inference with Synaptic Weight Plasticity

PubMed Central

Hiratani, Naoki; Fukai, Tomoki

2016-01-01

In the adult mammalian cortex, a small fraction of spines are created and eliminated every day, and the resultant synaptic connection structure is highly nonrandom, even in local circuits. However, it remains unknown whether a particular synaptic connection structure is functionally advantageous in local circuits, and why creation and elimination of synaptic connections is necessary in addition to rich synaptic weight plasticity. To answer these questions, we studied an inference task model through theoretical and numerical analyses. We demonstrate that a robustly beneficial network structure naturally emerges by combining Hebbian-type synaptic weight plasticity and wiring plasticity. Especially in a sparsely connected network, wiring plasticity achieves reliable computation by enabling efficient information transmission. Furthermore, the proposed rule reproduces experimental observed correlation between spine dynamics and task performance. PMID:27303271

Infants use compression information to infer objects’ weights: Examining cognition, exploration, and prospective action in a preferential-reaching task

PubMed Central

Hauf, Petra; Paulus, Markus; Baillargeon, Renée

2012-01-01

The present research used a preferential-reaching task to examine whether 9- and 11-month-olds (n = 144) could infer the relative weights of two objects resting on a soft, compressible platform. Experiment 1 established that infants reached preferentially for the lighter of two boxes. In Experiments 2 to 4, infants saw two boxes identical except in weight resting on a cotton wool platform. Infants reached prospectively for the lighter box, but only when their initial exploratory activities provided critical information. At 11 months, infants succeeded as long as they first determined that the platform was compressible; at 9 months, infants succeeded only if they also explored the boxes and thus had advance knowledge that they differed in weight. PMID:22861050

Gesture as a window on children's beginning understanding of false belief.

PubMed

Carlson, Stephanie M; Wong, Antoinette; Lemke, Margaret; Cosser, Caron

2005-01-01

Given that gestures may provide access to transitions in cognitive development, preschoolers' performance on standard tasks was compared with their performance on a new gesture false belief task. Experiment 1 confirmed that children (N=45, M age=54 months) responded consistently on two gesture tasks and that there is dramatic improvement on both the gesture false belief task and a standard task from ages 3 to 5. In 2 subsequent experiments focusing on children in transition with respect to understanding false beliefs (Ns=34 and 70, M age=48 months), there was a significant advantage of gesture over standard and novel verbal-response tasks. Iconic gesture may facilitate reasoning about opaque mental states in children who are rapidly developing concepts of mind.

Accounting for Movement between Childcare Classrooms: Does it Change Teacher Effects Interpretations?

PubMed Central

Messan Setodji, Claude; Le, Vi-Nhuan; Schaack, Diana

2012-01-01

Child care studies that have examined links between teachers' qualifications and children's outcomes often ignore teachers’ and children’s transitions between classrooms at a center throughout the day and only take into account head teacher qualifications. The objective of this investigation was to examine these traditional assumptions and to compare inferences made from these traditional models to methods accounting for transitions between classrooms and multiple teachers in a classroom. The study examined the receptive language, letter-word identification, and passage comprehension skills of 307 children enrolled in 49 community-based childcare centers serving primarily low-income families in Colorado. Results suggest that nearly one-third of children and over 80% of teachers moved daily between classrooms. Findings also reveal that failure to account for daily transitions between classrooms can affect interpretations of the relationship between teacher qualifications and child outcomes, with the model accounting for movement providing significant improvements in model fit and inference. PMID:22389546

Artificial emotion triggered stochastic behavior transitions with motivational gain effects for multi-objective robot tasks

NASA Astrophysics Data System (ADS)

Dağlarli, Evren; Temeltaş, Hakan

2007-04-01

This paper presents artificial emotional system based autonomous robot control architecture. Hidden Markov model developed as mathematical background for stochastic emotional and behavior transitions. Motivation module of architecture considered as behavioral gain effect generator for achieving multi-objective robot tasks. According to emotional and behavioral state transition probabilities, artificial emotions determine sequences of behaviors. Also motivational gain effects of proposed architecture can be observed on the executing behaviors during simulation.

Data Imputation in Epistatic MAPs by Network-Guided Matrix Completion

PubMed Central

Žitnik, Marinka; Zupan, Blaž

2015-01-01

Abstract Epistatic miniarray profile (E-MAP) is a popular large-scale genetic interaction discovery platform. E-MAPs benefit from quantitative output, which makes it possible to detect subtle interactions with greater precision. However, due to the limits of biotechnology, E-MAP studies fail to measure genetic interactions for up to 40% of gene pairs in an assay. Missing measurements can be recovered by computational techniques for data imputation, in this way completing the interaction profiles and enabling downstream analysis algorithms that could otherwise be sensitive to missing data values. We introduce a new interaction data imputation method called network-guided matrix completion (NG-MC). The core part of NG-MC is low-rank probabilistic matrix completion that incorporates prior knowledge presented as a collection of gene networks. NG-MC assumes that interactions are transitive, such that latent gene interaction profiles inferred by NG-MC depend on the profiles of their direct neighbors in gene networks. As the NG-MC inference algorithm progresses, it propagates latent interaction profiles through each of the networks and updates gene network weights toward improved prediction. In a study with four different E-MAP data assays and considered protein–protein interaction and gene ontology similarity networks, NG-MC significantly surpassed existing alternative techniques. Inclusion of information from gene networks also allowed NG-MC to predict interactions for genes that were not included in original E-MAP assays, a task that could not be considered by current imputation approaches. PMID:25658751

Probabilistic Low-Rank Multitask Learning.

PubMed

Kong, Yu; Shao, Ming; Li, Kang; Fu, Yun

2018-03-01

In this paper, we consider the problem of learning multiple related tasks simultaneously with the goal of improving the generalization performance of individual tasks. The key challenge is to effectively exploit the shared information across multiple tasks as well as preserve the discriminative information for each individual task. To address this, we propose a novel probabilistic model for multitask learning (MTL) that can automatically balance between low-rank and sparsity constraints. The former assumes a low-rank structure of the underlying predictive hypothesis space to explicitly capture the relationship of different tasks and the latter learns the incoherent sparse patterns private to each task. We derive and perform inference via variational Bayesian methods. Experimental results on both regression and classification tasks on real-world applications demonstrate the effectiveness of the proposed method in dealing with the MTL problems.

Human Judgment and the Logic of Evidence: A Critical Examination of Research Methods in Special Education Transition Literature.

ERIC Educational Resources Information Center

Harnisch, Delwyn L.; And Others

This paper describes several common types of research studies in special education transition literature and the threats to their validity. It then describes how the evidential base may be broadened, how diverse sources of evidence can be combined to strengthen causal inferences, and the role of judgment within quasi-experimentation. The paper…

Salient and Emerging Developmental Tasks in the Transition to Adulthood

ERIC Educational Resources Information Center

Roisman, Glenn I.; Masten, Ann S.; Coatsworth, J. Douglas; Tellegen, Auke

2004-01-01

Drawing on data from a normative sample of 205 children tracked into adulthood, this study examined the predictive links from 3 salient (friendship, academic, conduct) and 2 emerging (work, romantic) developmental tasks during the transition years around age 20 to adult adaptation 10 years later. Results (a) confirm the utility of salient…

The transition to increased automaticity during finger sequence learning in adult males who stutter.

PubMed

Smits-Bandstra, Sarah; De Nil, Luc; Rochon, Elizabeth

2006-01-01

The present study compared the automaticity levels of persons who stutter (PWS) and persons who do not stutter (PNS) on a practiced finger sequencing task under dual task conditions. Automaticity was defined as the amount of attention required for task performance. Twelve PWS and 12 control subjects practiced finger tapping sequences under single and then dual task conditions. Control subjects performed the sequencing task significantly faster and less variably under single versus dual task conditions while PWS' performance was consistently slow and variable (comparable to the dual task performance of control subjects) under both conditions. Control subjects were significantly more accurate on a colour recognition distracter task than PWS under dual task conditions. These results suggested that control subjects transitioned to quick, accurate and increasingly automatic performance on the sequencing task after practice, while PWS did not. Because most stuttering treatment programs for adults include practice and automatization of new motor speech skills, findings of this finger sequencing study and future studies of speech sequence learning may have important implications for how to maximize stuttering treatment effectiveness. As a result of this activity, the participant will be able to: (1) Define automaticity and explain the importance of dual task paradigms to investigate automaticity; (2) Relate the proposed relationship between motor learning and automaticity as stated by the authors; (3) Summarize the reviewed literature concerning the performance of PWS on dual tasks; and (4) Explain why the ability to transition to automaticity during motor learning may have important clinical implications for stuttering treatment effectiveness.

Task conflict and relationship conflict in top management teams: the pivotal role of intragroup trust.

PubMed

Simons, T L; Peterson, R S

2000-02-01

Task conflict is usually associated with effective decisions, and relationship conflict is associated with poor decisions. The 2 conflict types are typically correlated in ongoing groups, however, which creates a prescriptive dilemma. Three explanations might account for this relationship--misattribution of task conflict as relationship conflict, harsh task conflict tactics triggering relationship conflict, and misattribution of relationship conflict as task conflict. The authors found that intragroup trust moderates the relationship between task conflict and relationship conflict in 70 top management teams. This result supports the "misattribution of task conflict" explanation. The authors also found a weak effect that is consistent with the argument that tactical choices drive the association between the 2 conflict types. We infer that trust is a key to gaining the benefits of task conflict without suffering the costs of relationship conflict.

A Reaction Time Advantage for Calculating Beliefs over Public Representations Signals Domain Specificity for "Theory of Mind"

ERIC Educational Resources Information Center

Cohen, Adam S.; German, Tamsin C.

2010-01-01

In a task where participants' overt task was to track the location of an object across a sequence of events, reaction times to unpredictable probes requiring an inference about a social agent's beliefs about the location of that object were obtained. Reaction times to false belief situations were faster than responses about the (false) contents of…

Acquiring concepts and features of novel words by two types of learning: direct mapping and inference.

PubMed

Chen, Shuang; Wang, Lin; Yang, Yufang

2014-04-01

This study examined the semantic representation of novel words learnt in two conditions: directly mapping a novel word to a concept (Direct mapping: DM) and inferring the concept from provided features (Inferred learning: IF). A condition where no definite concept could be inferred (No basic-level meaning: NM) served as a baseline. The semantic representation of the novel word was assessed via a semantic-relatedness judgment task. In this task, the learned novel word served as a prime, while the corresponding concept, an unlearned feature of the concept, and an unrelated word served as targets. ERP responses to the targets, primed by the novel words in the three learning conditions, were compared. For the corresponding concept, smaller N400s were elicited in the DM and IF conditions than in the NM condition, indicating that the concept could be obtained in both learning conditions. However, for the unlearned feature, the targets in the IF condition produced an N400 effect while in the DM condition elicited an LPC effect relative to the NM learning condition. No ERP difference was observed among the three learning conditions for the unrelated words. The results indicate that conditions of learning affect the semantic representation of novel word, and that the unlearned feature was only activated by the novel word in the IF learning condition. Copyright © 2014 Elsevier Ltd. All rights reserved.

General mental ability and two types of adaptation to unforeseen change: applying discontinuous growth models to the task-change paradigm.

PubMed

Lang, Jonas W B; Bliese, Paul D

2009-03-01

The present research provides new insights into the relationship between general mental ability (GMA) and adaptive performance by applying a discontinuous growth modeling framework to a study of unforeseen change on a complex decision-making task. The proposed framework provides a way to distinguish 2 types of adaptation (transition adaptation and reacquisition adaptation) from 2 common performance components (skill acquisition and basal task performance). Transition adaptation refers to an immediate loss of performance following a change, whereas reacquisition adaptation refers to the ability to relearn a changed task over time. Analyses revealed that GMA was negatively related to transition adaptation and found no evidence for a relationship between GMA and reacquisition adaptation. The results are integrated within the context of adaptability research, and implications of using the described discontinuous growth modeling framework to study adaptability are discussed. (c) 2009 APA, all rights reserved.

Right-hemispheric dominance for processing extended non-linguistic frequency transitions.

PubMed

McKibbin, Katherine; Elias, Lorin J; Saucier, Deborah M; Engebregston, Delaine

2003-11-01

The left hemisphere is specialized for most linguistic tasks and the right hemisphere is specialized for many non-linguistic tasks, but the cause of these functional asymmetries is unknown. One of the stimulus factors that appears to influence these asymmetries is the rate at which stimuli change. In the present experiment, 41 participants completed the Fused Dichotic Words Test (FDWT) and a non-linguistic Frequency Transition Task (FTT) wherein the Frequency Transitions (FTs) were either rapid (40 ms) or relatively slow (200 ms). There was a right hemisphere advantage for slow FTs when the change was at the front of the stimulus, but no corresponding left hemisphere advantage for the rapid FTs. There was no relationship between either FTT and the left hemisphere advantage exhibited on the FDWT. This finding provides support for the position that the right hemisphere dominates tasks that require temporal processing over relatively long periods of time.

Narrative comprehension in 4-7-year-old children with autism: testing the Weak Central Coherence account.

PubMed

Nuske, Heather Joy; Bavin, Edith L

2011-01-01

Despite somewhat spared structural language development in high-functioning autism, communicative comprehension deficits persist. Comprehension involves the integration of meaning: global processing is required. The Weak Central Coherence theory suggests that individuals with autism are biased to process information locally. This cognitive style may impair comprehension, particularly if inferencing is required. However, task performance may be facilitated by this cognitive style if local processing is required. The current study was designed to examine the extent to which the 'weak central coherence' cognitive style affects comprehension and inferential processing of spoken narratives. The children with autism were expected to perform comparatively poorer on inferences relating to event scripts and comparatively better on inferences requiring deductive reasoning. Fourteen high-functioning children with autism were recruited from databases of various autism organizations (mean age = 6:7, 13 males, one female) and were matched on a receptive vocabulary and a picture-completion task with 14 typically developing children recruited from a local childcare centre (mean age = 4:10, seven males, seven females). The children were read short stories and asked questions about the stories. Results indicated that the children with autism were less able to make inferences based on event scripts, but the groups did not differ significantly on inferences requiring deductive logical reasoning. Despite similar group performance on questions relating to the main idea of the stories, only for the typically developing group was good performance on extracting the main idea of the narratives significantly correlated with performance on all other comprehension tasks. Findings provide some support for the Weak Central Coherence theory and demonstrate that young children with autism do not spontaneously integrate information in order to make script inferences, as do typically developing children. These findings may help to explain communicative problems of young children with autism and can be applied to intervention programme development. More research on the link between a 'weak central coherence' cognitive style and communicative comprehension in autism will be valuable in understanding the comprehension deficits associated with autism. © 2010 Royal College of Speech & Language Therapists.

An expert system shell for inferring vegetation characteristics: Implementation of additional techniques (task E)

NASA Technical Reports Server (NTRS)

Harrison, P. Ann

1992-01-01

The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. The VEG subgoal PROPORTION.GROUND.COVER has been completed and a number of additional techniques that infer the proportion ground cover of a sample have been implemented. Some techniques operate on sample data at a single wavelength. The techniques previously incorporated in VEG for other subgoals operated on data at a single wavelength so implementing the additional single wavelength techniques required no changes to the structure of VEG. Two techniques which use data at multiple wavelengths to infer proportion ground cover were also implemented. This work involved modifying the structure of VEG so that multiple wavelength techniques could be incorporated. All the new techniques were tested using both the VEG 'Research Mode' and the 'Automatic Mode.'

Children's selective trust decisions: rational competence and limiting performance factors.

PubMed

Hermes, Jonas; Behne, Tanya; Bich, Anna Elisa; Thielert, Christa; Rakoczy, Hannes

2018-03-01

Recent research has amply documented that even preschoolers learn selectively from others, preferring, for example, reliable over unreliable and competent over incompetent models. It remains unclear, however, what the cognitive foundations of such selective learning are, in particular, whether it builds on rational inferences or on less sophisticated processes. The current study, therefore, was designed to test directly the possibility that children are in principle capable of selective learning based on rational inference, yet revert to simpler strategies such as global impression formation under certain circumstances. Preschoolers (N = 75) were shown pairs of models that either differed in their degree of competence within one domain (strong vs. weak or knowledgeable vs. ignorant) or were both highly competent, but in different domains (e.g., strong vs. knowledgeable model). In the test trials, children chose between the models for strength- or knowledge-related tasks. The results suggest that, in fact, children are capable of rational inference-based selective trust: when both models were highly competent, children preferred the model with the competence most predictive and relevant for a given task. However, when choosing between two models that differed in competence on one dimension, children reverted to halo-style wide generalizations and preferred the competent models for both relevant and irrelevant tasks. These findings suggest that the rational strategies for selective learning, that children master in principle, can get masked by various performance factors. © 2017 John Wiley & Sons Ltd.

Feature-to-Feature Inference Under Conditions of Cue Restriction and Dimensional Correlation.

PubMed

Lancaster, Matthew E; Homa, Donald

2017-01-01

The present study explored feature-to-feature and label-to-feature inference in a category task for different category structures. In the correlated condition, each of the 4 dimensions comprising the category was positively correlated to each other and to the category label. In the uncorrelated condition, no correlation existed between the 4 dimensions comprising the category, although the dimension to category label correlation matched that of the correlated condition. After learning, participants made inference judgments of a missing feature, given 1, 2, or 3 feature cues; on half the trials, the category label was also included as a cue. The results showed superior inference of features following training on the correlated structure, with accurate inference when only a single feature was presented. In contrast, a single-feature cue resulted in chance levels of inference for the uncorrelated structure. Feature inference systematically improved with number of cues after training on the correlated structure. Surprisingly, a similar outcome was obtained for the uncorrelated structure, an outcome that must have reflected mediation via the category label. A descriptive model is briefly introduced to explain the results, with a suggestion that this paradigm might be profitably extended to hierarchical structures where the levels of feature-to-feature inference might vary with the depth of the hierarchy.

Quantitative assessment of protein activity in orphan tissues and single cells using the metaVIPER algorithm. | Office of Cancer Genomics

Cancer.gov

We and others have shown that transition and maintenance of biological states is controlled by master regulator proteins, which can be inferred by interrogating tissue-specific regulatory models (interactomes) with transcriptional signatures, using the VIPER algorithm. Yet, some tissues may lack molecular profiles necessary for interactome inference (orphan tissues), or, as for single cells isolated from heterogeneous samples, their tissue context may be undetermined.

Causal inferences about others’ behavior among the Wampar, Papua New Guinea – and why they are hard to elicit

PubMed Central

Beer, Bettina; Bender, Andrea

2015-01-01

As social beings, people need to be able to interact intelligently with others in their social environment. Accordingly, people spend much time conversing with one another in order to understand the broad and fine aspects of the relations that link them. They are especially interested in the interactive behaviors that constitute social relations, such as mutual aid, gift giving and exchange, sharing, informal socializing, or deception. The evaluations of these behaviors are embedded in social relationships and charged with values and emotions. We developed tasks to probe how people in an unfamiliar socio-cultural setting understand and account for the behavior of others conditional upon their category membership – by trying to elicit the basic categories, stereotypes, and models that inform the causal perceptions, inferences and reasoning people use in understanding others’ interactive behaviors – and we tested these tasks among the Wampar in Papua New Guinea. The results show changes in the relevance of social categories among the Wampar but also, and perhaps more important, limitations in the translation and applicability of cognitive tasks. PMID:25806007

Integrating conceptual knowledge within and across representational modalities.

PubMed

McNorgan, Chris; Reid, Jackie; McRae, Ken

2011-02-01

Research suggests that concepts are distributed across brain regions specialized for processing information from different sensorimotor modalities. Multimodal semantic models fall into one of two broad classes differentiated by the assumed hierarchy of convergence zones over which information is integrated. In shallow models, communication within- and between-modality is accomplished using either direct connectivity, or a central semantic hub. In deep models, modalities are connected via cascading integration sites with successively wider receptive fields. Four experiments provide the first direct behavioral tests of these models using speeded tasks involving feature inference and concept activation. Shallow models predict no within-modal versus cross-modal difference in either task, whereas deep models predict a within-modal advantage for feature inference, but a cross-modal advantage for concept activation. Experiments 1 and 2 used relatedness judgments to tap participants' knowledge of relations for within- and cross-modal feature pairs. Experiments 3 and 4 used a dual-feature verification task. The pattern of decision latencies across Experiments 1-4 is consistent with a deep integration hierarchy. Copyright © 2010 Elsevier B.V. All rights reserved.

Hippocampal Structure Predicts Statistical Learning and Associative Inference Abilities during Development.

PubMed

Schlichting, Margaret L; Guarino, Katharine F; Schapiro, Anna C; Turk-Browne, Nicholas B; Preston, Alison R

2017-01-01

Despite the importance of learning and remembering across the lifespan, little is known about how the episodic memory system develops to support the extraction of associative structure from the environment. Here, we relate individual differences in volumes along the hippocampal long axis to performance on statistical learning and associative inference tasks-both of which require encoding associations that span multiple episodes-in a developmental sample ranging from ages 6 to 30 years. Relating age to volume, we found dissociable patterns across the hippocampal long axis, with opposite nonlinear volume changes in the head and body. These structural differences were paralleled by performance gains across the age range on both tasks, suggesting improvements in the cross-episode binding ability from childhood to adulthood. Controlling for age, we also found that smaller hippocampal heads were associated with superior behavioral performance on both tasks, consistent with this region's hypothesized role in forming generalized codes spanning events. Collectively, these results highlight the importance of examining hippocampal development as a function of position along the hippocampal axis and suggest that the hippocampal head is particularly important in encoding associative structure across development.

Inference of Evolutionary Jumps in Large Phylogenies using Lévy Processes.

PubMed

Duchen, Pablo; Leuenberger, Christoph; Szilágyi, Sándor M; Harmon, Luke; Eastman, Jonathan; Schweizer, Manuel; Wegmann, Daniel

2017-11-01

Although it is now widely accepted that the rate of phenotypic evolution may not necessarily be constant across large phylogenies, the frequency and phylogenetic position of periods of rapid evolution remain unclear. In his highly influential view of evolution, G. G. Simpson supposed that such evolutionary jumps occur when organisms transition into so-called new adaptive zones, for instance after dispersal into a new geographic area, after rapid climatic changes, or following the appearance of an evolutionary novelty. Only recently, large, accurate and well calibrated phylogenies have become available that allow testing this hypothesis directly, yet inferring evolutionary jumps remains computationally very challenging. Here, we develop a computationally highly efficient algorithm to accurately infer the rate and strength of evolutionary jumps as well as their phylogenetic location. Following previous work we model evolutionary jumps as a compound process, but introduce a novel approach to sample jump configurations that does not require matrix inversions and thus naturally scales to large trees. We then make use of this development to infer evolutionary jumps in Anolis lizards and Loriinii parrots where we find strong signal for such jumps at the basis of clades that transitioned into new adaptive zones, just as postulated by Simpson's hypothesis. [evolutionary jump; Lévy process; phenotypic evolution; punctuated equilibrium; quantitative traits. The Author(s) 2017. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.

The Institute of American Indian Arts Background Information (Task One of the Transition Evaluation). Background Paper.

ERIC Educational Resources Information Center

Tippeconnic, John W., Jr.

The paper, prepared as Task One of the Institute of American Indian Arts Transition Evaluation, provides pertinent background information about the Institute of American Indian Arts in Santa Fe, New Mexico. A brief history of the Institute is given, with information about its philosophy and purpose; objectives; organization and administration; the…

Bilingual Phonological Awareness: Multilevel Construct Validation among Spanish-Speaking Kindergarteners in Transitional Bilingual Education Classrooms

ERIC Educational Resources Information Center

Branum-Martin, Lee; Mehta, Paras D.; Fletcher, Jack M.; Carlson, Coleen D.; Ortiz, Alba; Carlo, Maria; Francis, David J.

2006-01-01

The construct validity of English and Spanish phonological awareness (PA) tasks was examined with a sample of 812 kindergarten children from 71 transitional bilingual education program classrooms located in 3 different types of geographic regions in California and Texas. Tasks of PA, including blending nonwords, segmenting words, and phoneme…

The Family Life Cycle and Critical Transitions: Utilizing Cinematherapy to Facilitate Understanding and Increase Communication

ERIC Educational Resources Information Center

Ballard, Mary B.

2012-01-01

Transitioning successfully from one stage of development to the next in the family life cycle requires the accomplishment of certain developmental tasks. Couples and families who fail to accomplish these tasks often become "stuck" and unable to move forward. This impasse frequently leads to heightened stress reactions and crippled channels of…

Toward a process-level view of distributed healthcare tasks: Medication management as a case study.

PubMed

Werner, Nicole E; Malkana, Seema; Gurses, Ayse P; Leff, Bruce; Arbaje, Alicia I

2017-11-01

We aim to highlight the importance of using a process-level view in analyzing distributed healthcare tasks through a case study analysis of medication management (MM). MM during older adults' hospital-to-skilled-home-healthcare (SHHC) transitions is a healthcare process with tasks distributed across people, organizations, and time. MM has typically been studied at the task level, but a process-level is needed to fully understand and improve MM during transitions. A process-level view allows for a broader investigation of how tasks are distributed throughout the work system through an investigation of interactions and the resultant emergent properties. We studied MM during older adults' hospital-to-SHHC transitions through interviews and observations with 60 older adults, their 33 caregivers, and 79 SHHC providers at 5 sites associated with 3 SHHC agencies. Study findings identified key cross-system characteristics not observable at the task-level: (1) identification of emergent properties (e.g., role ambiguity, loosely-coupled teams performing MM) and associated barriers; and (2) examination of barrier propagation across system boundaries. Findings highlight the importance of a process-level view of healthcare delivery occurring across system boundaries. Copyright © 2017 Elsevier Ltd. All rights reserved.

How Children with Autism Reason about Other's Intentions: False-Belief and Counterfactual Inferences.

PubMed

Rasga, Célia; Quelhas, Ana Cristina; Byrne, Ruth M J

2017-06-01

We examine false belief and counterfactual reasoning in children with autism with a new change-of-intentions task. Children listened to stories, for example, Anne is picking up toys and John hears her say she wants to find her ball. John goes away and the reason for Anne's action changes-Anne's mother tells her to tidy her bedroom. We asked, 'What will John believe is the reason that Anne is picking up toys?' which requires a false-belief inference, and 'If Anne's mother hadn't asked Anne to tidy her room, what would have been the reason she was picking up toys?' which requires a counterfactual inference. We tested children aged 6, 8 and 10 years. Children with autism made fewer correct inferences than typically developing children at 8 years, but by 10 years there was no difference. Children with autism made fewer correct false-belief than counterfactual inferences, just like typically developing children.

Affective expressions in groups and inferences about members' relational well-being: The effects of socially engaging and disengaging emotions.

PubMed

Rothman, Naomi B; Magee, Joe C

2016-01-01

Our findings draw attention to the interpersonal communication function of a relatively unexplored dimension of emotions-the level of social engagement versus disengagement. In four experiments, regardless of valence and target group gender, observers infer greater relational well-being (more cohesiveness and less conflict) between group members from socially engaging (sadness and appreciation) versus disengaging (anger and pride) emotion expressions. Supporting our argument that social (dis)engagement is a critical dimension communicated by these emotions, we demonstrate (1) that inferences about group members' self-interest mediate the effect of socially engaging emotions on cohesiveness and (2) that the influence of socially disengaging emotion expressions on inferences of conflict is attenuated when groups have collectivistic norms (i.e., members value a high level of social engagement). Furthermore, we show an important downstream consequence of these inferences of relational well-being: Groups that seem less cohesive because of their members' proud (versus appreciative) expressions are also expected to have worse task performance.

Win-Stay, Lose-Sample: a simple sequential algorithm for approximating Bayesian inference.

PubMed

Bonawitz, Elizabeth; Denison, Stephanie; Gopnik, Alison; Griffiths, Thomas L

2014-11-01

People can behave in a way that is consistent with Bayesian models of cognition, despite the fact that performing exact Bayesian inference is computationally challenging. What algorithms could people be using to make this possible? We show that a simple sequential algorithm "Win-Stay, Lose-Sample", inspired by the Win-Stay, Lose-Shift (WSLS) principle, can be used to approximate Bayesian inference. We investigate the behavior of adults and preschoolers on two causal learning tasks to test whether people might use a similar algorithm. These studies use a "mini-microgenetic method", investigating how people sequentially update their beliefs as they encounter new evidence. Experiment 1 investigates a deterministic causal learning scenario and Experiments 2 and 3 examine how people make inferences in a stochastic scenario. The behavior of adults and preschoolers in these experiments is consistent with our Bayesian version of the WSLS principle. This algorithm provides both a practical method for performing Bayesian inference and a new way to understand people's judgments. Copyright © 2014 Elsevier Inc. All rights reserved.

Task-Dependent Behavioral Dynamics Make the Case for Temporal Integration in Multiple Strategies during Odor Processing

PubMed Central

Brown, Austin; Mehta, Nisarg; Vujovic, Mark; Amina, Tasneem; Fixsen, Bethany

2017-01-01

Differing results in olfactory-based decision-making research regarding the amount of time that rats and mice use to identify odors have led to some disagreements about odor-processing mechanics, including whether or not rodents use temporal integration (i.e., sniffing longer to identify odors better). Reported differences in behavioral strategies may be due to the different types of tasks used in different laboratories. Some researchers have reported that animals performing two-alternative choice (TAC) tasks need only 1–2 sniffs and do not increase performance with longer sampling. Others have reported that animals performing go/no-go (GNG) tasks increase sampling times and performance for difficult discriminations, arguing for temporal integration. We present results from four experiments comparing GNG and TAC tasks over several behavioral variables (e.g., performance, sampling duration). When rats know only one task, they perform better in GNG than in TAC. However, performance was not statistically different when rats learned and were tested in both tasks. Rats sample odors longer in GNG than in TAC, even when they know both tasks and perform them in the same or different sessions. Longer sampling is associated with better performance for both tasks in difficult discriminations, which supports the case for temporal integration over ≥2–6 sniffs in both tasks. These results illustrate that generalizations from a single task about behavioral or cognitive abilities (e.g., processing, perception) do not capture the full range of complexity and can significantly impact inferences about general abilities in sensory perception. SIGNIFICANCE STATEMENT Behavioral tasks and training and testing history affect measured outcomes in cognitive tests. Rats sample odors longer in a go/no-go (GNG) than in a two-alternative choice (TAC) task, performing better in GNG unless they know both tasks. Odor-sampling time is extended in both tasks when the odors to be discriminated are very similar. Rats may extend sampling time to integrate odor information up to ∼0.5 s (2–6 sniffs). Such factors as task, task parameters, and training history affect decision times and performance, making it important to use multiple tasks when making inferences about sensory or cognitive processing. PMID:28336570

Inferring Planet Occurrence Rates With a Q1-Q17 Kepler Planet Candidate Catalog Produced by a Machine Learning Classifier

NASA Astrophysics Data System (ADS)

Catanzarite, Joseph; Jenkins, Jon Michael; McCauliff, Sean D.; Burke, Christopher; Bryson, Steve; Batalha, Natalie; Coughlin, Jeffrey; Rowe, Jason; mullally, fergal; thompson, susan; Seader, Shawn; Twicken, Joseph; Li, Jie; morris, robert; smith, jeffrey; haas, michael; christiansen, jessie; Clarke, Bruce

2015-08-01

NASA’s Kepler Space Telescope monitored the photometric variations of over 170,000 stars, at half-hour cadence, over its four-year prime mission. The Kepler pipeline calibrates the pixels of the target apertures for each star, produces light curves with simple aperture photometry, corrects for systematic error, and detects threshold-crossing events (TCEs) that may be due to transiting planets. The pipeline estimates planet parameters for all TCEs and computes diagnostics used by the Threshold Crossing Event Review Team (TCERT) to produce a catalog of objects that are deemed either likely transiting planet candidates or false positives.We created a training set from the Q1-Q12 and Q1-Q16 TCERT catalogs and an ensemble of synthetic transiting planets that were injected at the pixel level into all 17 quarters of data, and used it to train a random forest classifier. The classifier uniformly and consistently applies diagnostics developed by the Transiting Planet Search and Data Validation pipeline components and by TCERT to produce a robust catalog of planet candidates.The characteristics of the planet candidates detected by Kepler (planet radius and period) do not reflect the intrinsic planet population. Detection efficiency is a function of SNR, so the set of detected planet candidates is incomplete. Transit detection preferentially finds close-in planets with nearly edge-on orbits and misses planets whose orbital geometry precludes transits. Reliability of the planet candidates must also be considered, as they may be false positives. Errors in detected planet radius and in assumed star properties can also bias inference of intrinsic planet population characteristics.In this work we infer the intrinsic planet population, starting with the catalog of detected planet candidates produced by our random forest classifier, and accounting for detection biases and reliabilities as well as for radius errors in the detected population.Kepler was selected as the 10th mission of the Discovery Program. Funding for this mission is provided by NASA, Science Mission Directorate.

Differentiable cortical networks for inferences concerning people’s intentions versus physical causality

PubMed Central

Mason, Robert A.; Just, Marcel Adam

2010-01-01

Cortical activity associated with generating an inference was measured using fMRI. Participants read three-sentence passages that differed in whether or not an inference needed to be drawn to understand them. The inference was based on either a protagonist’s intention or a physical consequence of a character’s action. Activation was expected in Theory of Mind brain regions for the passages based on protagonists’ intentions but not for the physical consequence passages. The activation measured in the right temporo-parietal junction was greater in the intentional passages than in the consequence passages, consistent with predictions from a Theory of Mind perspective. In contrast, there was increased occipital activation in the physical inference passages. For both types of passage, the cortical activity related to the reading of the critical inference sentence demonstrated a recruitment of a common inference cortical network. This general inference-related activation appeared bilaterally in the language processing areas (the inferior frontal gyrus, the temporal gyrus, and the angular gyrus), as well as in the medial to superior frontal gyrus, which has been found to be active in Theory of Mind tasks. These findings are consistent with the hypothesis that component areas of the discourse processing network are recruited as needed based on the nature of the inference. A Protagonist monitoring and synthesis network is proposed as a more accurate account for Theory of Mind activation during narrative comprehension. PMID:21229617

The transition from the core vowels to the following segments in Japanese children who stutter: the second, third and fourth syllables.

PubMed

Matsumoto-Shimamori, Sachiyo; Ito, Tomohiko; Fukuda, Suzy E; Fukuda, Shinji

2011-09-01

Shimamori and Ito (2007 , Syllable weight and phonological encoding in Japanese children who stutter. Japanese Journal of Special Education, 44, 451-462; 2008, Syllable weight and frequency of stuttering: Comparison between children who stutter with and without a family history of stuttering. Japanese Journal of Special Education, 45, 437-445; 2009, Difference in frequency of stuttering between light and heavy syllables in the production of monosyllables: From the viewpoint of phonetic transition. The Japanese Journal of Logopedics and Phoniatrics, 50, 116-122 (in Japanese)) proposed the hypothesis that in Japanese the transition from the core vowels (CVs) to the following segments affected the occurrence of stuttering. However, the transition we investigated was in the first syllables only, and the effect of the transition in second, third and fourth syllables was not addressed. The purpose of this study was to investigate whether the transition from the CVs in the second, third and fourth syllables affected the occurrence of stuttering. The participants were 21 Japanese children. A non-word naming task and a non-word reading task were used. The frequency of stuttering was not significantly different where the number of transitions from the CVs differed on either task. These results suggest that the transition from the CVs in the second, third and fourth syllables does not have a significant effect on the occurrence of stuttering in Japanese.

Bayesian Inference and Online Learning in Poisson Neuronal Networks.

PubMed

Huang, Yanping; Rao, Rajesh P N

2016-08-01

Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.

Qualitative Assessment of the Acoustic Disturbance Environment in the NASA LaRC 20-Inch MACH 6 Wind Tunnel

NASA Technical Reports Server (NTRS)

Horvath, Thomas J.; Berry, Scott A.; Hamilton, H. Harris

2001-01-01

An experimental investigation was conducted on a 5-degree-half-angle cone with a flare in a conventional Mach 6 wind tunnel to examine the effect of facility noise on boundary layer transition. The effect of tunnel noise was inferred by comparing transition onset locations determined from the present test to that previously obtained in a Mach 6 quiet tunnel. Together, the two sets of experiments are believed to represent the first direct comparison of transition onset between a conventional and a quiet hypersonic wind tunnel using a common test model. In the present conventional hypersonic tunnel experiment, adiabatic wall temperatures were measured and heat transfer distributions were inferred on the cone flare model at zero degree angle of attack over a range of length Reynolds numbers (2 x 10(exp 6) to 10 x 10(exp 6)) which resulted in laminar and turbulent flow. Wall-to-total temperature ratio for the transient heating measurements and the adiabatic wall temperature measurements were 0.69 and 0.86, respectively. The cone flare nosetip radius was varied from 0.0001 to 0.125-inch to examine the effects of bluntness on transition onset. At comparable freestream conditions the transition onset Reynolds number obtained on the cone flare model in the conventional "noisy" tunnel was approximately 25% lower than that measured in the low disturbance tunnel.

Impact of induced joy on literacy in children: does the nature of the task make a difference?

PubMed

Tornare, Elise; Cuisinier, Frédérique; Czajkowski, Nikolai O; Pons, Francisco

2017-04-01

This research examined whether induced joy influences fifth graders' performance in literacy tasks. Children were asked to recall a joyful experience, used as a joy induction, before completing either a grammar (Study 1) or textual comprehension task (Study 2). The grammar task involved understanding at the surface level and retrieval of appropriate declarative and procedural knowledge, but limited elaboration unlike the textual comprehension task, which tackled inference generation. By differentiating tasks based on depth of processing required for completion we aimed at testing the validity of two concurrent hypotheses: that of a facilitating effect and that of a detrimental effect of induced joy. Compared to controls, joy induced children showed better performance on the grammar task - specifically children with lower language ability. No differences across groups emerged as a function of joy induction on the text comprehension task. Results are discussed with respect to emotion effects on cognition.

Information access in a dual-task context: testing a model of optimal strategy selection.

PubMed

Wickens, C D; Seidler, K S

1997-09-01

Pilots were required to access information from a hierarchical aviation database by navigating under single-task conditions (Experiment 1) and when this task was time-shared with an altitude-monitoring task of varying bandwidth and priority (Experiment 2). In dual-task conditions, pilots had 2 viewports available, 1 always used for the information task and the other to be allocated to either task. Dual-task strategy, inferred from the decision of which task to allocate to the 2nd viewport, revealed that allocation was generally biased in favor of the monitoring task and was only partly sensitive to the difficulty of the 2 tasks and their relative priorities. Some dominant sources of navigational difficulties failed to adaptively influence selection strategy. The implications of the results are to provide tools for jumping to the top of the database, to provide 2 viewports into the common database, and to provide training as to the optimum viewport management strategy in a multitask environment.

Information access in a dual-task context: testing a model of optimal strategy selection

NASA Technical Reports Server (NTRS)

Wickens, C. D.; Seidler, K. S.

1997-01-01

Pilots were required to access information from a hierarchical aviation database by navigating under single-task conditions (Experiment 1) and when this task was time-shared with an altitude-monitoring task of varying bandwidth and priority (Experiment 2). In dual-task conditions, pilots had 2 viewports available, 1 always used for the information task and the other to be allocated to either task. Dual-task strategy, inferred from the decision of which task to allocate to the 2nd viewport, revealed that allocation was generally biased in favor of the monitoring task and was only partly sensitive to the difficulty of the 2 tasks and their relative priorities. Some dominant sources of navigational difficulties failed to adaptively influence selection strategy. The implications of the results are to provide tools for jumping to the top of the database, to provide 2 viewports into the common database, and to provide training as to the optimum viewport management strategy in a multitask environment.

Computational Phenotyping in Psychiatry: A Worked Example

PubMed Central

2016-01-01

Abstract Computational psychiatry is a rapidly emerging field that uses model-based quantities to infer the behavioral and neuronal abnormalities that underlie psychopathology. If successful, this approach promises key insights into (pathological) brain function as well as a more mechanistic and quantitative approach to psychiatric nosology—structuring therapeutic interventions and predicting response and relapse. The basic procedure in computational psychiatry is to build a computational model that formalizes a behavioral or neuronal process. Measured behavioral (or neuronal) responses are then used to infer the model parameters of a single subject or a group of subjects. Here, we provide an illustrative overview over this process, starting from the modeling of choice behavior in a specific task, simulating data, and then inverting that model to estimate group effects. Finally, we illustrate cross-validation to assess whether between-subject variables (e.g., diagnosis) can be recovered successfully. Our worked example uses a simple two-step maze task and a model of choice behavior based on (active) inference and Markov decision processes. The procedural steps and routines we illustrate are not restricted to a specific field of research or particular computational model but can, in principle, be applied in many domains of computational psychiatry. PMID:27517087

The Utility of Cognitive Plausibility in Language Acquisition Modeling: Evidence From Word Segmentation.

PubMed

Phillips, Lawrence; Pearl, Lisa

2015-11-01

The informativity of a computational model of language acquisition is directly related to how closely it approximates the actual acquisition task, sometimes referred to as the model's cognitive plausibility. We suggest that though every computational model necessarily idealizes the modeled task, an informative language acquisition model can aim to be cognitively plausible in multiple ways. We discuss these cognitive plausibility checkpoints generally and then apply them to a case study in word segmentation, investigating a promising Bayesian segmentation strategy. We incorporate cognitive plausibility by using an age-appropriate unit of perceptual representation, evaluating the model output in terms of its utility, and incorporating cognitive constraints into the inference process. Our more cognitively plausible model shows a beneficial effect of cognitive constraints on segmentation performance. One interpretation of this effect is as a synergy between the naive theories of language structure that infants may have and the cognitive constraints that limit the fidelity of their inference processes, where less accurate inference approximations are better when the underlying assumptions about how words are generated are less accurate. More generally, these results highlight the utility of incorporating cognitive plausibility more fully into computational models of language acquisition. Copyright © 2015 Cognitive Science Society, Inc.

Computational Phenotyping in Psychiatry: A Worked Example.

PubMed

Schwartenbeck, Philipp; Friston, Karl

2016-01-01

Computational psychiatry is a rapidly emerging field that uses model-based quantities to infer the behavioral and neuronal abnormalities that underlie psychopathology. If successful, this approach promises key insights into (pathological) brain function as well as a more mechanistic and quantitative approach to psychiatric nosology-structuring therapeutic interventions and predicting response and relapse. The basic procedure in computational psychiatry is to build a computational model that formalizes a behavioral or neuronal process. Measured behavioral (or neuronal) responses are then used to infer the model parameters of a single subject or a group of subjects. Here, we provide an illustrative overview over this process, starting from the modeling of choice behavior in a specific task, simulating data, and then inverting that model to estimate group effects. Finally, we illustrate cross-validation to assess whether between-subject variables (e.g., diagnosis) can be recovered successfully. Our worked example uses a simple two-step maze task and a model of choice behavior based on (active) inference and Markov decision processes. The procedural steps and routines we illustrate are not restricted to a specific field of research or particular computational model but can, in principle, be applied in many domains of computational psychiatry.

Quantum Inference on Bayesian Networks

NASA Astrophysics Data System (ADS)

Yoder, Theodore; Low, Guang Hao; Chuang, Isaac

2014-03-01

Because quantum physics is naturally probabilistic, it seems reasonable to expect physical systems to describe probabilities and their evolution in a natural fashion. Here, we use quantum computation to speedup sampling from a graphical probability model, the Bayesian network. A specialization of this sampling problem is approximate Bayesian inference, where the distribution on query variables is sampled given the values e of evidence variables. Inference is a key part of modern machine learning and artificial intelligence tasks, but is known to be NP-hard. Classically, a single unbiased sample is obtained from a Bayesian network on n variables with at most m parents per node in time (nmP(e) - 1 / 2) , depending critically on P(e) , the probability the evidence might occur in the first place. However, by implementing a quantum version of rejection sampling, we obtain a square-root speedup, taking (n2m P(e) -1/2) time per sample. The speedup is the result of amplitude amplification, which is proving to be broadly applicable in sampling and machine learning tasks. In particular, we provide an explicit and efficient circuit construction that implements the algorithm without the need for oracle access.

The development of categorization: effects of classification and inference training on category representation.

PubMed

Deng, Wei Sophia; Sloutsky, Vladimir M

2015-03-01

Does category representation change in the course of development? And if so, how and why? The current study attempted to answer these questions by examining category learning and category representation. In Experiment 1, 4-year-olds, 6-year-olds, and adults were trained with either a classification task or an inference task and their categorization performance and memory for items were tested. Adults and 6-year-olds exhibited an important asymmetry: they relied on a single deterministic feature during classification training, but not during inference training. In contrast, regardless of the training condition, 4-year-olds relied on multiple probabilistic features. In Experiment 2, 4-year-olds were presented with classification training and their attention was explicitly directed to the deterministic feature. Under this condition, their categorization performance was similar to that of older participants in Experiment 1, yet their memory performance pointed to a similarity-based representation, which was similar to that of 4-year-olds in Experiment 1. These results are discussed in relation to theories of categorization and the role of selective attention in the development of category learning. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Inferring energy dissipation from violation of the fluctuation-dissipation theorem

NASA Astrophysics Data System (ADS)

Wang, Shou-Wen

2018-05-01

The Harada-Sasa equality elegantly connects the energy dissipation rate of a moving object with its measurable violation of the Fluctuation-Dissipation Theorem (FDT). Although proven for Langevin processes, its validity remains unclear for discrete Markov systems whose forward and backward transition rates respond asymmetrically to external perturbation. A typical example is a motor protein called kinesin. Here we show generally that the FDT violation persists surprisingly in the high-frequency limit due to the asymmetry, resulting in a divergent FDT violation integral and thus a complete breakdown of the Harada-Sasa equality. A renormalized FDT violation integral still well predicts the dissipation rate when each discrete transition produces a small entropy in the environment. Our study also suggests a way to infer this perturbation asymmetry based on the measurable high-frequency-limit FDT violation.

Signature of inverse Compton emission from blazars

NASA Astrophysics Data System (ADS)

Gaur, Haritma; Mohan, Prashanth; Wierzcholska, Alicja; Gu, Minfeng

2018-01-01

Blazars are classified into high-, intermediate- and low-energy-peaked sources based on the location of their synchrotron peak. This lies in infra-red/optical to ultra-violet bands for low- and intermediate-peaked blazars. The transition from synchrotron to inverse Compton emission falls in the X-ray bands for such sources. We present the spectral and timing analysis of 14 low- and intermediate-energy-peaked blazars observed with XMM-Newton spanning 31 epochs. Parametric fits to X-ray spectra help constrain the possible location of transition from the high-energy end of the synchrotron to the low-energy end of the inverse Compton emission. In seven sources in our sample, we infer such a transition and constrain the break energy in the range 0.6-10 keV. The Lomb-Scargle periodogram is used to estimate the power spectral density (PSD) shape. It is well described by a power law in a majority of light curves, the index being flatter compared to general expectation from active galactic nuclei, ranging here between 0.01 and 1.12, possibly due to short observation durations resulting in an absence of long-term trends. A toy model involving synchrotron self-Compton and external Compton (EC; disc, broad line region, torus) mechanisms are used to estimate magnetic field strength ≤0.03-0.88 G in sources displaying the energy break and infer a prominent EC contribution. The time-scale for variability being shorter than synchrotron cooling implies steeper PSD slopes which are inferred in these sources.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heng, Kevin, E-mail: kevin.heng@csh.unibe.ch

We present a dimensionless index that quantifies the degree of cloudiness of the atmosphere of a transiting exoplanet. Our cloudiness index is based on measuring the transit radii associated with the line center and wing of the sodium or potassium line. In deriving this index, we revisited the algebraic formulae for inferring the isothermal pressure scale height from transit measurements. We demonstrate that the formulae of Lecavelier et al. and Benneke and Seager are identical: the former is inferring the temperature while assuming a value for the mean molecular mass and the latter is inferring the mean molecular mass whilemore » assuming a value for the temperature. More importantly, these formulae cannot be used to distinguish between cloudy and cloud-free atmospheres. We derive values of our cloudiness index for a small sample of seven hot Saturns/Jupiters taken from Sing et al. We show that WASP-17b, WASP-31b, and HAT-P-1b are nearly cloud-free at visible wavelengths. We find the tentative trend that more irradiated atmospheres tend to have fewer clouds consisting of sub-micron-sized particles. We also derive absolute sodium and/or potassium abundances ∼10{sup 2} cm{sup −3} for WASP-17b, WASP-31b, and HAT-P-1b (and upper limits for the other objects). Higher-resolution measurements of both the sodium and potassium lines, for a larger sample of exoplanetary atmospheres, are needed to confirm or refute this trend.« less

Compositional clustering in task structure learning

PubMed Central

Frank, Michael J.

2018-01-01

Humans are remarkably adept at generalizing knowledge between experiences in a way that can be difficult for computers. Often, this entails generalizing constituent pieces of experiences that do not fully overlap, but nonetheless share useful similarities with, previously acquired knowledge. However, it is often unclear how knowledge gained in one context should generalize to another. Previous computational models and data suggest that rather than learning about each individual context, humans build latent abstract structures and learn to link these structures to arbitrary contexts, facilitating generalization. In these models, task structures that are more popular across contexts are more likely to be revisited in new contexts. However, these models can only re-use policies as a whole and are unable to transfer knowledge about the transition structure of the environment even if only the goal has changed (or vice-versa). This contrasts with ecological settings, where some aspects of task structure, such as the transition function, will be shared between context separately from other aspects, such as the reward function. Here, we develop a novel non-parametric Bayesian agent that forms independent latent clusters for transition and reward functions, affording separable transfer of their constituent parts across contexts. We show that the relative performance of this agent compared to an agent that jointly clusters reward and transition functions depends environmental task statistics: the mutual information between transition and reward functions and the stochasticity of the observations. We formalize our analysis through an information theoretic account of the priors, and propose a meta learning agent that dynamically arbitrates between strategies across task domains to optimize a statistical tradeoff. PMID:29672581

NIMEFI: Gene Regulatory Network Inference using Multiple Ensemble Feature Importance Algorithms

PubMed Central

Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

2014-01-01

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available. PMID:24667482

A Constructivist Connectionist Model of Transitions on False-Belief Tasks

ERIC Educational Resources Information Center

Berthiaume, Vincent G.; Shultz, Thomas R.; Onishi, Kristine H.

2013-01-01

How do children come to understand that others have mental representations, e.g., of an object's location? Preschoolers go through two transitions on verbal false-belief tasks, in which they have to predict where an agent will search for an object that was moved in her absence. First, while three-and-a-half-year-olds usually fail at approach…

A Connectionist Model of a Continuous Developmental Transition in the Balance Scale Task

ERIC Educational Resources Information Center

Schapiro, Anna C.; McClelland, James L.

2009-01-01

A connectionist model of the balance scale task is presented which exhibits developmental transitions between "Rule I" and "Rule II" behavior [Siegler, R. S. (1976). Three aspects of cognitive development. "Cognitive Psychology," 8, 481-520.] as well as the "catastrophe flags" seen in data from Jansen and van der Maas [Jansen, B. R. J., & van der…

Increased Alpha (8-12 Hz) Activity during Slow Wave Sleep as a Marker for the Transition from Implicit Knowledge to Explicit Insight

ERIC Educational Resources Information Center

Yordanova, Juliana; Kolev, Vasil; Wagner, Ullrich; Born, Jan; Verleger, Rolf

2012-01-01

The number reduction task (NRT) allows us to study the transition from implicit knowledge of hidden task regularities to explicit insight into these regularities. To identify sleep-associated neurophysiological indicators of this restructuring of knowledge representations, we measured frequency-specific power of EEG while participants slept during…

Generating probabilistic Boolean networks from a prescribed transition probability matrix.

PubMed

Ching, W-K; Chen, X; Tsing, N-K

2009-11-01

Probabilistic Boolean networks (PBNs) have received much attention in modeling genetic regulatory networks. A PBN can be regarded as a Markov chain process and is characterised by a transition probability matrix. In this study, the authors propose efficient algorithms for constructing a PBN when its transition probability matrix is given. The complexities of the algorithms are also analysed. This is an interesting inverse problem in network inference using steady-state data. The problem is important as most microarray data sets are assumed to be obtained from sampling the steady-state.

Inferring rules of lineage commitment in haematopoiesis.

PubMed

Pina, Cristina; Fugazza, Cristina; Tipping, Alex J; Brown, John; Soneji, Shamit; Teles, Jose; Peterson, Carsten; Enver, Tariq

2012-02-19

How the molecular programs of differentiated cells develop as cells transit from multipotency through lineage commitment remains unexplored. This reflects the inability to access cells undergoing commitment or located in the immediate vicinity of commitment boundaries. It remains unclear whether commitment constitutes a gradual process, or else represents a discrete transition. Analyses of in vitro self-renewing multipotent systems have revealed cellular heterogeneity with individual cells transiently exhibiting distinct biases for lineage commitment. Such systems can be used to molecularly interrogate early stages of lineage affiliation and infer rules of lineage commitment. In haematopoiesis, population-based studies have indicated that lineage choice is governed by global transcriptional noise, with self-renewing multipotent cells reversibly activating transcriptome-wide lineage-affiliated programs. We examine this hypothesis through functional and molecular analysis of individual blood cells captured from self-renewal cultures, during cytokine-driven differentiation and from primary stem and progenitor bone marrow compartments. We show dissociation between self-renewal potential and transcriptome-wide activation of lineage programs, and instead suggest that multipotent cells experience independent activation of individual regulators resulting in a low probability of transition to the committed state.

Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo

PubMed Central

Golightly, Andrew; Wilkinson, Darren J.

2011-01-01

Computational systems biology is concerned with the development of detailed mechanistic models of biological processes. Such models are often stochastic and analytically intractable, containing uncertain parameters that must be estimated from time course data. In this article, we consider the task of inferring the parameters of a stochastic kinetic model defined as a Markov (jump) process. Inference for the parameters of complex nonlinear multivariate stochastic process models is a challenging problem, but we find here that algorithms based on particle Markov chain Monte Carlo turn out to be a very effective computationally intensive approach to the problem. Approximations to the inferential model based on stochastic differential equations (SDEs) are considered, as well as improvements to the inference scheme that exploit the SDE structure. We apply the methodology to a Lotka–Volterra system and a prokaryotic auto-regulatory network. PMID:23226583

Kepler and Ground-Based Transits of the exo-Neptune HAT-P-11b

NASA Technical Reports Server (NTRS)

Deming, Drake; Sada, Pedro V.; Jackson, Brian; Peterson, Steven W.; Agol, Eric; Knutson, Heather A.; Jennings, Donald E.; Haase, Plynn; Bays, Kevin

2011-01-01

We analyze 26 archival Kepler transits of the exo-Neptune HAT-P-11b, supplemented by ground-based transits observed in the blue (B band) and near-IR (J band). Both the planet and host star are smaller than previously believed; our analysis yields Rp = 4.31 R xor 0.06 R xor and Rs = 0.683 R solar mass 0.009 R solar mass, both about 3 sigma smaller than the discovery values. Our ground-based transit data at wavelengths bracketing the Kepler bandpass serve to check the wavelength dependence of stellar limb darkening, and the J-band transit provides a precise and independent constraint on the transit duration. Both the limb darkening and transit duration from our ground-based data are consistent with the new Kepler values for the system parameters. Our smaller radius for the planet implies that its gaseous envelope can be less extensive than previously believed, being very similar to the H-He envelope of GJ 436b and Kepler-4b. HAT-P-11 is an active star, and signatures of star spot crossings are ubiquitous in the Kepler transit data. We develop and apply a methodology to correct the planetary radius for the presence of both crossed and uncrossed star spots. Star spot crossings are concentrated at phases 0.002 and +0.006. This is consistent with inferences from Rossiter-McLaughlin measurements that the planet transits nearly perpendicular to the stellar equator. We identify the dominant phases of star spot crossings with active latitudes on the star, and infer that the stellar rotational pole is inclined at about 12 deg 5 deg to the plane of the sky. We point out that precise transit measurements over long durations could in principle allow us to construct a stellar Butterfly diagram to probe the cyclic evolution of magnetic activity on this active K-dwarf star.

Suburban transit opportunities study.

DOT National Transportation Integrated Search

2005-01-01

Providing successful suburban transit service is not an easy task. With the vast majority of work and non-work related trips being made by automobiles and land use policies that generally do not support conventional transit service, providing alterna...

Analysis of Florida transit bus accidents

DOT National Transportation Integrated Search

2001-06-01

Through its National Center for Transit Research, and under contract with the Florida Department of Transportation, the Center for Urban Transportation Research was tasked with reviewing a sample of transit bus crash occurrence data from selected Flo...

Analysis of Florida transit bus crashes

DOT National Transportation Integrated Search

2001-06-01

Through its National Center for Transit Research, and under contract with the Florida Department of Transportation, the Center for Urban Transportation Research was tasked with reviewing a sample of transit bus crash occurrence data from selected Flo...

When and why do old adults outsource control to the environment?

PubMed

Mayr, Ulrich; Spieler, Daniel H; Hutcheon, Thomas G

2015-09-01

Old adults' tendency to rely on information present in the environment rather than internal representations has been frequently noted, but is not well understood. The fade-out paradigm provides a useful model situation to study this internal-to-external shift across the life span: Subjects need to transition from an initial, cued task-switching phase to a fade-out phase where only 1 task remains relevant. Old adults exhibit large response-time "fade-out costs," mainly because they continue to consult the task cues. Here we show that age differences in fade-out costs remain very large even when we insert between the task-switching and the fade-out phase 20 single-task trials without task cues (during which even old adults' performance becomes highly fluent; Experiment 1), but costs in old adults are eliminated when presenting an on-screen instruction to focus on the 1 remaining task at the transition point between the task-switching and fade-out phase (Experiment 2). Furthermore, old adults, but not young adults, also exhibited "fade-in costs" when they were instructed to perform an initial single-task phase that would be followed by the cued task-switching phase (Experiment 3). Combined, these results show that old adults' tendency to overutilize external support is not a problem of perseverating earlier-relevant control settings. Instead, old adults seem less likely to initiate the necessary reconfiguration process when transitioning from 1 phase to the next because they use underspecified task models that lack the higher-level distinction between those contexts that do and that do not require external support. (c) 2015 APA, all rights reserved).

DeepInfer: open-source deep learning deployment toolkit for image-guided therapy

NASA Astrophysics Data System (ADS)

Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A.; Kapur, Tina; Wells, William M.; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

2017-03-01

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research work ows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose "DeepInfer" - an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

DeepInfer: Open-Source Deep Learning Deployment Toolkit for Image-Guided Therapy.

PubMed

Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A; Kapur, Tina; Wells, William M; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

2017-02-11

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research workflows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose "DeepInfer" - an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

The development of adaptive decision making: Recognition-based inference in children and adolescents.

PubMed

Horn, Sebastian S; Ruggeri, Azzurra; Pachur, Thorsten

2016-09-01

Judgments about objects in the world are often based on probabilistic information (or cues). A frugal judgment strategy that utilizes memory (i.e., the ability to discriminate between known and unknown objects) as a cue for inference is the recognition heuristic (RH). The usefulness of the RH depends on the structure of the environment, particularly the predictive power (validity) of recognition. Little is known about developmental differences in use of the RH. In this study, the authors examined (a) to what extent children and adolescents recruit the RH when making judgments, and (b) around what age adaptive use of the RH emerges. Primary schoolchildren (M = 9 years), younger adolescents (M = 12 years), and older adolescents (M = 17 years) made comparative judgments in task environments with either high or low recognition validity. Reliance on the RH was measured with a hierarchical multinomial model. Results indicated that primary schoolchildren already made systematic use of the RH. However, only older adolescents adaptively adjusted their strategy use between environments and were better able to discriminate between situations in which the RH led to correct versus incorrect inferences. These findings suggest that the use of simple heuristics does not progress unidirectionally across development but strongly depends on the task environment, in line with the perspective of ecological rationality. Moreover, adaptive heuristic inference seems to require experience and a developed base of domain knowledge. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Representing metarepresentations: is there theory of mind-specific cognition?

PubMed

Egeth, Marc; Kurzban, Robert

2009-03-01

What cognitive mechanisms underlie Theory of Mind? Some infer domain-specific Theory of Mind cognition based the pattern of children diagnosed with autism failing the False Belief test but passing the False Photograph test. However, we argue that the False Belief test entails various task demands the False Photograph task does not, including the necessity to represent a higher-order representation (a metarepresentation), thus confounding the inference of domain-specificity. Instead, a general difficulty that affects representations of metarepresentations might account for the seeming domain-specific failure. Here we find that False-Belief failing False-Photograph passing children fail the Meta Photograph test, a new photograph-domain test that requires subjects to represent a metarepresentation. We conclude that people who fail the False Belief test but pass the False Photograph test do not necessarily have a content-specific Theory of Mind deficit. Instead, the general ability to represent representations and metarepresentations might underlie Theory of Mind.

Cardinal rules: Visual orientation perception reflects knowledge of environmental statistics

PubMed Central

Girshick, Ahna R.; Landy, Michael S.; Simoncelli, Eero P.

2011-01-01

Humans are remarkably good at performing visual tasks, but experimental measurements reveal substantial biases in the perception of basic visual attributes. An appealing hypothesis is that these biases arise through a process of statistical inference, in which information from noisy measurements is fused with a probabilistic model of the environment. But such inference is optimal only if the observer’s internal model matches the environment. Here, we provide evidence that this is the case. We measured performance in an orientation-estimation task, demonstrating the well-known fact that orientation judgements are more accurate at cardinal (horizontal and vertical) orientations, along with a new observation that judgements made under conditions of uncertainty are strongly biased toward cardinal orientations. We estimate observers’ internal models for orientation and find that they match the local orientation distribution measured in photographs. We also show how a neural population could embed probabilistic information responsible for such biases. PMID:21642976

Mixed-initiative control of intelligent systems

NASA Technical Reports Server (NTRS)

Borchardt, G. C.

1987-01-01

Mixed-initiative user interfaces provide a means by which a human operator and an intelligent system may collectively share the task of deciding what to do next. Such interfaces are important to the effective utilization of real-time expert systems as assistants in the execution of critical tasks. Presented here is the Incremental Inference algorithm, a symbolic reasoning mechanism based on propositional logic and suited to the construction of mixed-initiative interfaces. The algorithm is similar in some respects to the Truth Maintenance System, but replaces the notion of 'justifications' with a notion of recency, allowing newer values to override older values yet permitting various interested parties to refresh these values as they become older and thus more vulnerable to change. A simple example is given of the use of the Incremental Inference algorithm plus an overview of the integration of this mechanism within the SPECTRUM expert system for geological interpretation of imaging spectrometer data.

Interactive information retrieval systems with minimalist representation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Domeshek, E.; Kedar, S.; Gordon, A.

Almost any information you might want is becoming available on-line. The problem is how to find what you need. One strategy to improve access to existing information sources, is intelligent information agents - an approach based on extensive representation and inference. Another alternative is to simply concentrate on better information organization and indexing. Our systems use a form of conceptual indexing sensitive to users` task-specific information needs. We aim for minimalist representation, coding only select aspects of stored items. Rather than supporting reliable automated inference, the primary purpose of our representations is to provide sufficient discrimination and guidance to amore » user for a given domain and task. This paper argues, using case studies, that minimal representations can make strong contributions to the usefulness and usability of interactive information systems, while minimizing knowledge engineering effort. We demonstrate this approach in several broad spectrum applications including video retrieval and advisory systems.« less

Reverse inference of memory retrieval processes underlying metacognitive monitoring of learning using multivariate pattern analysis.

PubMed

Stiers, Peter; Falbo, Luciana; Goulas, Alexandros; van Gog, Tamara; de Bruin, Anique

2016-05-15

Monitoring of learning is only accurate at some time after learning. It is thought that immediate monitoring is based on working memory, whereas later monitoring requires re-activation of stored items, yielding accurate judgements. Such interpretations are difficult to test because they require reverse inference, which presupposes specificity of brain activity for the hidden cognitive processes. We investigated whether multivariate pattern classification can provide this specificity. We used a word recall task to create single trial examples of immediate and long term retrieval and trained a learning algorithm to discriminate them. Next, participants performed a similar task involving monitoring instead of recall. The recall-trained classifier recognized the retrieval patterns underlying immediate and long term monitoring and classified delayed monitoring examples as long-term retrieval. This result demonstrates the feasibility of decoding cognitive processes, instead of their content. Copyright © 2016 Elsevier Inc. All rights reserved.

Explosive diversification following a benthic to pelagic shift in freshwater fishes.

PubMed

Hollingsworth, Phillip R; Simons, Andrew M; Fordyce, James A; Hulsey, C Darrin

2013-12-17

Interspecific divergence along a benthic to pelagic habitat axis is ubiquitous in freshwater fishes inhabiting lentic environments. In this study, we examined the influence of this habitat axis on the macroevolution of a diverse, lotic radiation using mtDNA and nDNA phylogenies for eastern North America's most species-rich freshwater fish clade, the open posterior myodome (OPM) cyprinids. We used ancestral state reconstruction to identify the earliest benthic to pelagic transition in this group and generated fossil-calibrated estimates of when this shift occurred. This transition could have represented evolution into a novel adaptive zone, and therefore, we tested for a period of accelerated lineage accumulation after this historical habitat shift. Ancestral state reconstructions inferred a similar and concordant region of our mtDNA and nDNA based gene trees as representing the shift from benthic to pelagic habitats in the OPM clade. Two independent tests conducted on each gene tree suggested an increased diversification rate after this inferred habitat transition. Furthermore, lineage through time analyses indicated rapid early cladogenesis in the clade arising after the benthic to pelagic shift. A burst of diversification followed the earliest benthic to pelagic transition during the radiation of OPM cyprinids in eastern North America. As such, the benthic/pelagic habitat axis has likely influenced the generation of biodiversity across disparate freshwater ecosystems.

A reconstruction of sexual modes throughout animal evolution.

PubMed

Sasson, Daniel A; Ryan, Joseph F

2017-12-06

Although most extant animals have separate sexes, simultaneous hermaphrodites can be found in lineages throughout the animal kingdom. However, the sexual modes of key ancestral nodes including the last common ancestor (LCA) of all animals remain unclear. Without these data, it is difficult to infer the reproductive-state transitions that occurred early in animal evolution, and thus a broad understanding of the evolution of animal reproduction remains elusive. In this study, we use a composite phylogeny from four previously published studies, two alternative topologies (ctenophores or sponges as sister to the rest of animals), and multiple phylogenetic approaches to conduct the most extensive analysis to date of the evolution of animal sexual modes. Our analyses clarify the sexual mode of many ancestral animal nodes and allow for sound inferences of modal transitions that have occurred in animal history. Our results also indicate that the transition from separate sexes to hermaphroditism has been more common in animal history than the reverse. These results provide the most complete view of the evolution of animal sexual modes to date and provide a framework for future inquiries into the correlation of these transitions with genes, behaviors, and physiology. These results also suggest that mutations promoting hermaphroditism have historically been more likely to invade gonochoristic populations than vice versa.

Resident learning across the full range of core competencies through a transitions of care curriculum.

PubMed

Pavon, Juliessa M; Pinheiro, Sandro O; Buhr, Gwendolen T

2018-01-01

The authors developed a Transitions of Care (TOC) curriculum to teach and measure learner competence in performing TOC tasks for older adults. Internal medicine interns at an academic residency program received the curriculum, which consisted of experiential learning, self-study, and small group discussion. Interns completed retrospective pre/post surveys rating their confidence in performing five TOC tasks, qualitative open-ended survey questions, and a self-reflection essay. A subset of interns also completed follow-up assessments. For all five TOC tasks, the interns' confidence improved following completion of the TOC curriculum. Self-confidence persisted for up to 3 months later for some but not all tasks. According to the qualitative responses, the TOC curriculum provided interns with learning experiences and skills integral to performing safe care transitions. The TOC curriculum and a mixed-method assessment approach effectively teaches and measures learner competency in TOC across all six Accreditation Council for Graduate Medical Education competency domains.

The Effect of Time on Mediation Deficiency in Children and Adults

ERIC Educational Resources Information Center

Peterson, Candida C.

1973-01-01

Results support the inference that symbolic mediation is a measurably time-consuming process and that temporal factors are involved in the ontogenetic transition from single-link to symbolically mediated behavior. (Author/CB)

Project FARE task III report : urban mass transportation industry reporting system design : interim task III report for November 1972-June 1973 period. Part I - Task summary.

DOT National Transportation Integrated Search

1973-06-01

This report contains a description of the proposed uniform reporting system for the urban mass transit industry. It is presented in four volumes: Part I - Task Summary contains a description of how Task III was accomplished and the conclusions and re...

Does It Really Matter Where You Look When Walking on Stairs? Insights from a Dual-Task Study

PubMed Central

Miyasike-daSilva, Veronica; McIlroy, William E.

2012-01-01

Although the visual system is known to provide relevant information to guide stair locomotion, there is less understanding of the specific contributions of foveal and peripheral visual field information. The present study investigated the specific role of foveal vision during stair locomotion and ground-stairs transitions by using a dual-task paradigm to influence the ability to rely on foveal vision. Fifteen healthy adults (26.9±3.3 years; 8 females) ascended a 7-step staircase under four conditions: no secondary tasks (CONTROL); gaze fixation on a fixed target located at the end of the pathway (TARGET); visual reaction time task (VRT); and auditory reaction time task (ART). Gaze fixations towards stair features were significantly reduced in TARGET and VRT compared to CONTROL and ART. Despite the reduced fixations, participants were able to successfully ascend stairs and rarely used the handrail. Step time was increased during VRT compared to CONTROL in most stair steps. Navigating on the transition steps did not require more gaze fixations than the middle steps. However, reaction time tended to increase during locomotion on transitions suggesting additional executive demands during this phase. These findings suggest that foveal vision may not be an essential source of visual information regarding stair features to guide stair walking, despite the unique control challenges at transition phases as highlighted by phase-specific challenges in dual-tasking. Instead, the tendency to look at the steps in usual conditions likely provides a stable reference frame for extraction of visual information regarding step features from the entire visual field. PMID:22970297

A Prize-Collecting Steiner Tree Approach for Transduction Network Inference

NASA Astrophysics Data System (ADS)

Bailly-Bechet, Marc; Braunstein, Alfredo; Zecchina, Riccardo

Into the cell, information from the environment is mainly propagated via signaling pathways which form a transduction network. Here we propose a new algorithm to infer transduction networks from heterogeneous data, using both the protein interaction network and expression datasets. We formulate the inference problem as an optimization task, and develop a message-passing, probabilistic and distributed formalism to solve it. We apply our algorithm to the pheromone response in the baker’s yeast S. cerevisiae. We are able to find the backbone of the known structure of the MAPK cascade of pheromone response, validating our algorithm. More importantly, we make biological predictions about some proteins whose role could be at the interface between pheromone response and other cellular functions.

Mining Top K Spread Sources for a Specific Topic and a Given Node.

PubMed

Liu, Weiwei; Deng, Zhi-Hong; Cao, Longbing; Xu, Xiaoran; Liu, He; Gong, Xiuwen

2015-11-01

In social networks, nodes (or users) interested in specific topics are often influenced by others. The influence is usually associated with a set of nodes rather than a single one. An interesting but challenging task for any given topic and node is to find the set of nodes that represents the source or trigger for the topic and thus identify those nodes that have the greatest influence on the given node as the topic spreads. We find that it is an NP-hard problem. This paper proposes an effective framework to deal with this problem. First, the topic propagation is represented as the Bayesian network. We then construct the propagation model by a variant of the voter model. The probability transition matrix (PTM) algorithm is presented to conduct the probability inference with the complexity O(θ(3)log2θ), while θ is the number nodes in the given graph. To evaluate the PTM algorithm, we conduct extensive experiments on real datasets. The experimental results show that the PTM algorithm is both effective and efficient.

Modelling Chemical Reasoning to Predict and Invent Reactions.

PubMed

Segler, Marwin H S; Waller, Mark P

2017-05-02

The ability to reason beyond established knowledge allows organic chemists to solve synthetic problems and invent novel transformations. Herein, we propose a model that mimics chemical reasoning, and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outperforms a rule-based expert system in the reaction prediction task for 180 000 randomly selected binary reactions. The data-driven model generalises even beyond known reaction types, and is thus capable of effectively (re-)discovering novel transformations (even including transition metal-catalysed reactions). Our model enables computers to infer hypotheses about reactivity and reactions by only considering the intrinsic local structure of the graph and because each single reaction prediction is typically achieved in a sub-second time frame, the model can be used as a high-throughput generator of reaction hypotheses for reaction discovery. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Abnormal agency experiences in schizophrenia patients: Examining the role of psychotic symptoms and familial risk.

PubMed

Prikken, Merel; van der Weiden, Anouk; Renes, Robert A; Koevoets, Martijn G J C; Heering, Henriette D; Kahn, René S; Aarts, Henk; van Haren, Neeltje E M

2017-04-01

Experiencing self-agency over one's own action outcomes is essential for social functioning. Recent research revealed that patients with schizophrenia do not use implicitly available information about their action-outcomes (i.e., prime-based agency inference) to arrive at self-agency experiences. Here, we examined whether this is related to symptoms and/or familial risk to develop the disease. Fifty-four patients, 54 controls, and 19 unaffected (and unrelated) siblings performed an agency inference task, in which experienced agency was measured over action-outcomes that matched or mismatched outcome-primes that were presented before action performance. The Positive and Negative Syndrome Scale (PANSS) and Comprehensive Assessment of Symptoms and History (CASH) were administered to assess psychopathology. Impairments in prime-based inferences did not differ between patients with symptoms of over- and underattribution. However, patients with agency underattribution symptoms reported significantly lower overall self-agency experiences. Siblings displayed stronger prime-based agency inferences than patients, but weaker prime-based inferences than healthy controls. However, these differences were not statistically significant. Findings suggest that impairments in prime-based agency inferences may be a trait characteristic of schizophrenia. Moreover, this study may stimulate further research on the familial basis and the clinical relevance of impairments in implicit agency inferences. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Roughness induced transition and heat transfer augmentation in hypersonic environments

NASA Astrophysics Data System (ADS)

Wassel, A. T.; Shih, W. C. L.; Courtney, J. F.

Boundary layer transition and surface heating distributions on graphite, fine weave carbon-carbon, and metallic nosetip materials were derived from surface temperature responses measured in nitrogen environments during both free-flight and track-guided testing in hypersonic environments. Innovative test procedures were developed, and heat transfer results were validated against established theory through experiments using a super-smooth tungsten model. Quantitative definitions of mean transition front locations were established by deriving heat flux distributions from measured temperatures, and comparisons made with existing nosetip transition correlations. Qualitative transition locations were inferred directly from temperature distributions to investigate preferred orientations on fine weave nosetips. Levels of roughness augmented heat transfer were generally shown to be below values predicted by state-of-the-art methods.

Relational learning and transitive expression in aging and amnesia

PubMed Central

D'Angelo, Maria C.; Kamino, Daphne; Ostreicher, Melanie; Moses, Sandra N.; Rosenbaum, R. Shayna

2016-01-01

ABSTRACT Aging has been associated with a decline in relational memory, which is critically supported by the hippocampus. By adapting the transitivity paradigm (Bunsey and Eichenbaum (1996) Nature 379:255‐257), which traditionally has been used in nonhuman animal research, this work examined the extent to which aging is accompanied by deficits in relational learning and flexible expression of relational information. Older adults' performance was additionally contrasted with that of amnesic case DA to understand the critical contributions of the medial temporal lobe, and specifically, the hippocampus, which endures structural and functional changes in healthy aging. Participants were required to select the correct choice item (B versus Y) based on the presented sample item (e.g., A). Pairwise relations must be learned (A‐>B, B‐>C, C‐>D) so that ultimately, the correct relations can be inferred when presented with a novel probe item (A‐>C?Z?). Participants completed four conditions of transitivity that varied in terms of the degree to which the stimuli and the relations among them were known pre‐experimentally. Younger adults, older adults, and DA performed similarly when the condition employed all pre‐experimentally known, semantic, relations. Older adults and DA were less accurate than younger adults when all to‐be‐learned relations were arbitrary. However, accuracy improved for older adults when they could use pre‐experimentally known pairwise relations to express understanding of arbitrary relations as indexed through inference judgments. DA could not learn arbitrary relations nor use existing knowledge to support novel inferences. These results suggest that while aging has often been associated with an emerging decline in hippocampal function, prior knowledge can be used to support novel inferences. However, in case DA, significant damage to the hippocampus likely impaired his ability to learn novel relations, while additional damage to ventromedial prefrontal and anterior temporal regions may have resulted in an inability to use prior knowledge to flexibly express indirect relational knowledge. © 2015 The Authors Hippocampus Published by Wiley Periodicals, Inc. PMID:26234960

It's Time to Transition to Production, Now What?

NASA Technical Reports Server (NTRS)

Jansma, P. A.; Montgomery, Marc; Werntz, David; Payne, Michael

1999-01-01

When it's time to transition to production, it's easy to be too focused on the application itself and to overlook some areas crucial to your success. Learn about the 10 transition tasks that will ensure a smooth transition, and will prepare your organization to operate and use your system effectively.

Use of Internal Consistency Coefficients for Estimating Reliability of Experimental Tasks Scores

PubMed Central

Green, Samuel B.; Yang, Yanyun; Alt, Mary; Brinkley, Shara; Gray, Shelley; Hogan, Tiffany; Cowan, Nelson

2017-01-01

Reliabilities of scores for experimental tasks are likely to differ from one study to another to the extent that the task stimuli change, the number of trials varies, the type of individuals taking the task changes, the administration conditions are altered, or the focal task variable differs. Given reliabilities vary as a function of the design of these tasks and the characteristics of the individuals taking them, making inferences about the reliability of scores in an ongoing study based on reliability estimates from prior studies is precarious. Thus, it would be advantageous to estimate reliability based on data from the ongoing study. We argue that internal consistency estimates of reliability are underutilized for experimental task data and in many applications could provide this information using a single administration of a task. We discuss different methods for computing internal consistency estimates with a generalized coefficient alpha and the conditions under which these estimates are accurate. We illustrate use of these coefficients using data for three different tasks. PMID:26546100

Testing the distinctiveness of visual imagery and motor imagery in a reach paradigm.

PubMed

Gabbard, Carl; Ammar, Diala; Cordova, Alberto

2009-01-01

We examined the distinctiveness of motor imagery (MI) and visual imagery (VI) in the context of perceived reachability. The aim was to explore the notion that the two visual modes have distinctive processing properties tied to the two-visual-system hypothesis. The experiment included an interference tactic whereby participants completed two tasks at the same time: a visual or motor-interference task combined with a MI or VI-reaching task. We expected increased error would occur when the imaged task and the interference task were matched (e.g., MI with the motor task), suggesting an association based on the assumption that the two tasks were in competition for space on the same processing pathway. Alternatively, if there were no differences, dissociation could be inferred. Significant increases in the number of errors were found when the modalities for the imaged (both MI and VI) task and the interference task were matched. Therefore, it appears that MI and VI in the context of perceived reachability recruit different processing mechanisms.

Differentiable cortical networks for inferences concerning people's intentions versus physical causality.

PubMed

Mason, Robert A; Just, Marcel Adam

2011-02-01

Cortical activity associated with generating an inference was measured using fMRI. Participants read three-sentence passages that differed in whether or not an inference needed to be drawn to understand them. The inference was based on either a protagonist's intention or a physical consequence of a character's action. Activation was expected in Theory of Mind brain regions for the passages based on protagonists' intentions but not for the physical consequence passages. The activation measured in the right temporo-parietal junction was greater in the intentional passages than in the consequence passages, consistent with predictions from a Theory of Mind perspective. In contrast, there was increased occipital activation in the physical inference passages. For both types of passage, the cortical activity related to the reading of the critical inference sentence demonstrated a recruitment of a common inference cortical network. This general inference-related activation appeared bilaterally in the language processing areas (the inferior frontal gyrus, the temporal gyrus, and the angular gyrus), as well as in the medial to superior frontal gyrus, which has been found to be active in Theory of Mind tasks. These findings are consistent with the hypothesis that component areas of the discourse processing network are recruited as needed based on the nature of the inference. A Protagonist monitoring and synthesis network is proposed as a more accurate account for Theory of Mind activation during narrative comprehension. Copyright © 2010 Wiley-Liss, Inc.

KOI-2700b—A Planet Candidate with Dusty Effluents on a 22 hr Orbit

NASA Astrophysics Data System (ADS)

Rappaport, Saul; Barclay, Thomas; DeVore, John; Rowe, Jason; Sanchis-Ojeda, Roberto; Still, Martin

2014-03-01

Kepler planet candidate KOI-2700b (KIC 8639908b), with an orbital period of 21.84 hr, exhibits a distinctly asymmetric transit profile, likely indicative of the emission of dusty effluents, and reminiscent of KIC 1255b. The host star has T eff = 4435 K, M ~= 0.63 M ⊙, and R ~= 0.57 R ⊙, comparable to the parameters ascribed to KIC 12557548. The transit egress can be followed for ~25% of the orbital period and, if interpreted as extinction from a dusty comet-like tail, indicates a long lifetime for the dust grains of more than a day. We present a semiphysical model for the dust tail attenuation and fit for the physical parameters contained in that expression. The transit is not sufficiently deep to allow for a study of the transit-to-transit variations, as is the case for KIC 1255b however, it is clear that the transit depth is slowly monotonically decreasing by a factor of ~2 over the duration of the Kepler mission. We infer a mass-loss rate in dust from the planet of ~2 lunar masses per Gyr. The existence of a second star hosting a planet with a dusty comet-like tail would help to show that such objects may be more common and less exotic than originally thought. According to current models, only quite small planets with Mp <~ 0.03 M ⊕ are likely to release a detectable quantity of dust. Thus, any "normal-looking" transit that is inferred to arise from a rocky planet of radius greater than ~1/2 R ⊕ should not exhibit any hint of a dusty tail. Conversely, if one detects an asymmetric transit due to a dusty tail, then it will be very difficult to detect the hard body of the planet within the transit because, by necessity, the planet must be quite small (i.e., <~ 0.3 R ⊕).

Pressure and temperature induced elastic properties of Am and Cf monobismuthides

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jain, S., E-mail: sanjay-rjain@rediffmail.com; Sushila Devi Bansal College of Engineering, Rau, Indore 452001; Shriya, S.

2016-05-23

The pressure and temperature dependent mechanical properties as melting temperature, hardness and brittle nature of XBi (X = Am and Cf) are studied. The rare earth actinides pnictides showed a structural phase transition (B1–B2) at a transition pressure (P{sub T}) of 14.3 GPa (AmBi) and 10.8 GPa (CfBi). Pressure dependence of melting temperature (T{sub m}) discerns an increase inferring the hardening or stiffening of the lattice as a consequence of bond compression and bond strengthening. Suppressed T{sub M} as functions of temperature infers the weakening of the lattice results in bond weakening in XBi (X = Am, Cf). Vickers Hardnessmore » (H{sub V}), Poisson’s and Pugh ratio of XBi (X = Am and Cf) demonstrates that XBi (X = Am and Cf) is mechanically stiffened, thermally softened and brittle on applied pressure and temperature.« less

Perceptual and Conceptual Priming of Cue Encoding in Task Switching

ERIC Educational Resources Information Center

Schneider, Darryl W.

2016-01-01

Transition effects in task-cuing experiments can be partitioned into task switching and cue repetition effects by using multiple cues per task. In the present study, the author shows that cue repetition effects can be partitioned into perceptual and conceptual priming effects. In 2 experiments, letters or numbers in their uppercase/lowercase or…

The Role of Response Repetition in Task Switching

ERIC Educational Resources Information Center

Cooper, Stephen; Mari-Beffa, Paloma

2008-01-01

When switching between tasks, participants are sometimes required to use different response sets for each task. Thus, task switch and response set switch are confounded. In 5 experiments, the authors examined transitions of response within a linear 4-finger arrangement. A random baseline condition was compared with the cuing of specific response…

From Neutron Star Observables to the Equation of State. II. Bayesian Inference of Equation of State Pressures

NASA Astrophysics Data System (ADS)

Raithel, Carolyn A.; Özel, Feryal; Psaltis, Dimitrios

2017-08-01

One of the key goals of observing neutron stars is to infer the equation of state (EoS) of the cold, ultradense matter in their interiors. Here, we present a Bayesian statistical method of inferring the pressures at five fixed densities, from a sample of mock neutron star masses and radii. We show that while five polytropic segments are needed for maximum flexibility in the absence of any prior knowledge of the EoS, regularizers are also necessary to ensure that simple underlying EoS are not over-parameterized. For ideal data with small measurement uncertainties, we show that the pressure at roughly twice the nuclear saturation density, {ρ }{sat}, can be inferred to within 0.3 dex for many realizations of potential sources of uncertainties. The pressures of more complicated EoS with significant phase transitions can also be inferred to within ˜30%. We also find that marginalizing the multi-dimensional parameter space of pressure to infer a mass-radius relation can lead to biases of nearly 1 km in radius, toward larger radii. Using the full, five-dimensional posterior likelihoods avoids this bias.

Analysis of the Structure of Surgical Activity for a Suturing and Knot-Tying Task

PubMed Central

Vedula, S. Swaroop; Malpani, Anand O.; Tao, Lingling; Chen, George; Gao, Yixin; Poddar, Piyush; Ahmidi, Narges; Paxton, Christopher; Vidal, Rene; Khudanpur, Sanjeev; Hager, Gregory D.; Chen, Chi Chiung Grace

2016-01-01

Background Surgical tasks are performed in a sequence of steps, and technical skill evaluation includes assessing task flow efficiency. Our objective was to describe differences in task flow for expert and novice surgeons for a basic surgical task. Methods We used a hierarchical semantic vocabulary to decompose and annotate maneuvers and gestures for 135 instances of a surgeon’s knot performed by 18 surgeons. We compared counts of maneuvers and gestures, and analyzed task flow by skill level. Results Experts used fewer gestures to perform the task (26.29; 95% CI = 25.21 to 27.38 for experts vs. 31.30; 95% CI = 29.05 to 33.55 for novices) and made fewer errors in gestures than novices (1.00; 95% CI = 0.61 to 1.39 vs. 2.84; 95% CI = 2.3 to 3.37). Transitions among maneuvers, and among gestures within each maneuver for expert trials were more predictable than novice trials. Conclusions Activity segments and state flow transitions within a basic surgical task differ by surgical skill level, and can be used to provide targeted feedback to surgical trainees. PMID:26950551

The capacity to generate alternative ideas is more important than inhibition for logical reasoning in preschool-age children.

PubMed

de Chantal, Pier-Luc; Markovits, Henry

2017-02-01

There is little consensus about the nature of logical reasoning and, equally important, about how it develops. To address this, we looked at the early origins of deductive reasoning in preschool children. We examined the contribution of two factors to the reasoning ability of very young children: inhibitory capacity and the capacity to generate alternative ideas. In a first study, a total of 32 preschool children were all given generation, inhibition, and logical reasoning measures. Logical reasoning was measured using knowledge-based premises such as "All dogs have legs," and two different inferences: modus ponens and affirmation of the consequent. Results revealed that correctly reasoning with both inferences is not related to the measure of inhibition, but is rather related to the capacity to generate alternative ideas. In a second study, 32 preschool children were given either the generation or the inhibition task before the logical reasoning measure. Results showed that receiving the generation task beforehand significantly improved logical reasoning compared to the inhibition task given beforehand. Overall, these results provide evidence for the greater importance of idea generation in the early development of logical reasoning.

Interaction without intent: the shape of the social world in Huntington’s disease

PubMed Central

Rickards, Hugh E.

2015-01-01

Huntington’s disease (HD) is an inherited neurodegenerative condition. Patients with this movement disorder can exhibit deficits on tasks involving Theory of Mind (ToM): the ability to understand mental states such as beliefs and emotions. We investigated mental state inference in HD in response to ambiguous animations involving geometric shapes, while exploring the impact of symptoms within cognitive, emotional and motor domains. Forty patients with HD and twenty healthy controls described the events in videos showing random movements of two triangles (i.e. floating), simple interactions (e.g. following) and more complex interactions prompting the inference of mental states (e.g. one triangle encouraging the other). Relationships were explored between animation interpretation and measures of executive functioning, alexithymia and motor symptoms. Individuals with HD exhibited alexithymia and a reduced tendency to spontaneously attribute intentions to interacting triangles on the animations task. Attribution of intentions on the animations task correlated with motor symptoms and burden of pathology. Importantly, patients without motor symptoms showed similar ToM deficits despite intact executive functions. Subtle changes in ToM that are unrelated to executive dysfunction could therefore feature in basal ganglia disorders prior to motor onset. PMID:25680992

Don’t Ask, Don’t Tell: Failing in Strategic Leadership

DTIC Science & Technology

2007-03-30

another example of where the results might be useful. Even if 7 one accepts the fact that task cohesion overrides social cohesion as a major...it can be inferred that social cohesion is still an important factor in combat. At this point the important question becomes whether openly gay...Gateway, 1993), 57. 17 39 Social cohesion refers to emotional bonds and friendship, and is distinguished from task cohesion which refers to a group’s

Finding Waldo: Learning about Users from their Interactions.

PubMed

Brown, Eli T; Ottley, Alvitta; Zhao, Helen; Quan Lin; Souvenir, Richard; Endert, Alex; Chang, Remco

2014-12-01

Visual analytics is inherently a collaboration between human and computer. However, in current visual analytics systems, the computer has limited means of knowing about its users and their analysis processes. While existing research has shown that a user's interactions with a system reflect a large amount of the user's reasoning process, there has been limited advancement in developing automated, real-time techniques that mine interactions to learn about the user. In this paper, we demonstrate that we can accurately predict a user's task performance and infer some user personality traits by using machine learning techniques to analyze interaction data. Specifically, we conduct an experiment in which participants perform a visual search task, and apply well-known machine learning algorithms to three encodings of the users' interaction data. We achieve, depending on algorithm and encoding, between 62% and 83% accuracy at predicting whether each user will be fast or slow at completing the task. Beyond predicting performance, we demonstrate that using the same techniques, we can infer aspects of the user's personality factors, including locus of control, extraversion, and neuroticism. Further analyses show that strong results can be attained with limited observation time: in one case 95% of the final accuracy is gained after a quarter of the average task completion time. Overall, our findings show that interactions can provide information to the computer about its human collaborator, and establish a foundation for realizing mixed-initiative visual analytics systems.

The Social Attribution Task - Multiple Choice (SAT-MC): Psychometric comparison with social cognitive measures for schizophrenia research.

PubMed

Johannesen, Jason K; Fiszdon, Joanna M; Weinstein, Andrea; Ciosek, David; Bell, Morris D

2018-04-01

The Social Attribution Task-Multiple Choice (SAT-MC) tests the ability to extract social themes from viewed object motion. This form of animacy perception is thought to aid the development of social inference, but appears impaired in schizophrenia. The current study was undertaken to examine psychometric equivalence of two forms of the SAT-MC and to compare their performance against social cognitive tests recommended for schizophrenia research. Thirty-two schizophrenia (SZ) and 30 substance use disorder (SUD) participants completed both SAT-MC forms, the Bell-Lysaker Emotion Recognition Task (BLERT), Hinting Task, The Awareness of Social Inference Test (TASIT), Ambiguous Intentions and Hostility Questionnaire (AIHQ) and questionnaire measures of interpersonal function. Test sensitivity, construct and external validity, test-retest reliability, and internal consistency were evaluated. SZ scored significantly lower than SUD on both SAT-MC forms, each classifying ~60% of SZ as impaired, compared with ~30% of SUD. SAT-MC forms demonstrated good test-retest and parallel form reliability, minimal practice effect, high internal consistency, and similar patterns of correlation with social cognitive and external validity measures. The SAT-MC compared favorably to recommended social cognitive tests across psychometric features and, with exception of TASIT, was most sensitive to impairment in schizophrenia when compared to a chronic substance use sample. Published by Elsevier B.V.

Self-Associations Influence Task-Performance through Bayesian Inference

PubMed Central

Bengtsson, Sara L.; Penny, Will D.

2013-01-01

The way we think about ourselves impacts greatly on our behavior. This paper describes a behavioral study and a computational model that shed new light on this important area. Participants were primed “clever” and “stupid” using a scrambled sentence task, and we measured the effect on response time and error-rate on a rule-association task. First, we observed a confirmation bias effect in that associations to being “stupid” led to a gradual decrease in performance, whereas associations to being “clever” did not. Second, we observed that the activated self-concepts selectively modified attention toward one’s performance. There was an early to late double dissociation in RTs in that primed “clever” resulted in RT increase following error responses, whereas primed “stupid” resulted in RT increase following correct responses. We propose a computational model of subjects’ behavior based on the logic of the experimental task that involves two processes; memory for rules and the integration of rules with subsequent visual cues. The model incorporates an adaptive decision threshold based on Bayes rule, whereby decision thresholds are increased if integration was inferred to be faulty. Fitting the computational model to experimental data confirmed our hypothesis that priming affects the memory process. This model explains both the confirmation bias and double dissociation effects and demonstrates that Bayesian inferential principles can be used to study the effect of self-concepts on behavior. PMID:23966937

Increasing On-Task Behavior in Every Student in a Second-Grade Classroom during Transitions: Validating the Color Wheel System

ERIC Educational Resources Information Center

Fudge, Daniel L.; Skinner, Christopher H.; Williams, Jacqueline L.; Cowden, Dan; Clark, Janice; Bliss, Stacy L.

2008-01-01

A single-case (B-C-B-C) experimental design was used to evaluate the effects of the Color Wheel classroom management system (CWS) on on-task (OT) behavior in an intact, general-education, 2nd-grade classroom during transitions. The CWS included three sets of rules, posted cues to indicate the rules students are expected to be following at that…

fastBMA: scalable network inference and transitive reduction.

PubMed

Hung, Ling-Hong; Shi, Kaiyuan; Wu, Migao; Young, William Chad; Raftery, Adrian E; Yeung, Ka Yee

2017-10-01

Inferring genetic networks from genome-wide expression data is extremely demanding computationally. We have developed fastBMA, a distributed, parallel, and scalable implementation of Bayesian model averaging (BMA) for this purpose. fastBMA also includes a computationally efficient module for eliminating redundant indirect edges in the network by mapping the transitive reduction to an easily solved shortest-path problem. We evaluated the performance of fastBMA on synthetic data and experimental genome-wide time series yeast and human datasets. When using a single CPU core, fastBMA is up to 100 times faster than the next fastest method, LASSO, with increased accuracy. It is a memory-efficient, parallel, and distributed application that scales to human genome-wide expression data. A 10 000-gene regulation network can be obtained in a matter of hours using a 32-core cloud cluster (2 nodes of 16 cores). fastBMA is a significant improvement over its predecessor ScanBMA. It is more accurate and orders of magnitude faster than other fast network inference methods such as the 1 based on LASSO. The improved scalability allows it to calculate networks from genome scale data in a reasonable time frame. The transitive reduction method can improve accuracy in denser networks. fastBMA is available as code (M.I.T. license) from GitHub (https://github.com/lhhunghimself/fastBMA), as part of the updated networkBMA Bioconductor package (https://www.bioconductor.org/packages/release/bioc/html/networkBMA.html) and as ready-to-deploy Docker images (https://hub.docker.com/r/biodepot/fastbma/). © The Authors 2017. Published by Oxford University Press.

The person trade-off method and the transitivity principle: an example from preferences over age weighting.

PubMed

Dolan, Paul; Tsuchiya, Aki

2003-06-01

The person trade-off (PTO) is increasingly being used to elicit preferences in health. This paper explores the measurement properties of the PTO method in the context of a study about how members of the public prioritise between patients of different ages. In particular, it considers whether PTO responses satisfy the transitivity principle; that is, whether one PTO response can be inferred from two other PTO responses. The results suggest that very few responses to PTO questions satisfy cardinal transitivity condition. However, this study has produced results that suggest that cardinal transitivity will hold, on average, when respondents who fail to satisfy the ordinal transitivity condition have been excluded from the analysis. This suggests that future PTO studies should build in checks for ordinal transitivity. Copyright 2002 John Wiley & Sons, Ltd.

Catch and Release: Orbital Symmetry Guided Reaction Dynamics from a Freed “Tension Trapped Transition State”

DOE PAGES

Wang, Junpeng; Ong, Mitchell T.; Kouznetsova, Tatiana B.; ...

2015-08-31

The dynamics of reactions at or in the immediate vicinity of transition states are critical to reaction rates and product distributions, but direct experimental probes of those dynamics are rare. In this paper, s-trans, s-trans 1,3-diradicaloid transition states are trapped by tension along the backbone of purely cis-substituted gem-difluorocyclopropanated polybutadiene using the extensional forces generated by pulsed sonication of dilute polymer solutions. Once released, the branching ratio between symmetry-allowed disrotatory ring closing (of which the trapped diradicaloid structure is the transition state) and symmetry-forbidden conrotatory ring closing (whose transition state is nearby) can be inferred. Finally, net conrotatory ring closingmore » occurred in 5.0 ± 0.5% of the released transition states, in excellent agreement with ab initio molecular dynamics simulations.« less

Modeling the Evolution of Beliefs Using an Attentional Focus Mechanism

PubMed Central

Marković, Dimitrije; Gläscher, Jan; Bossaerts, Peter; O’Doherty, John; Kiebel, Stefan J.

2015-01-01

For making decisions in everyday life we often have first to infer the set of environmental features that are relevant for the current task. Here we investigated the computational mechanisms underlying the evolution of beliefs about the relevance of environmental features in a dynamical and noisy environment. For this purpose we designed a probabilistic Wisconsin card sorting task (WCST) with belief solicitation, in which subjects were presented with stimuli composed of multiple visual features. At each moment in time a particular feature was relevant for obtaining reward, and participants had to infer which feature was relevant and report their beliefs accordingly. To test the hypothesis that attentional focus modulates the belief update process, we derived and fitted several probabilistic and non-probabilistic behavioral models, which either incorporate a dynamical model of attentional focus, in the form of a hierarchical winner-take-all neuronal network, or a diffusive model, without attention-like features. We used Bayesian model selection to identify the most likely generative model of subjects’ behavior and found that attention-like features in the behavioral model are essential for explaining subjects’ responses. Furthermore, we demonstrate a method for integrating both connectionist and Bayesian models of decision making within a single framework that allowed us to infer hidden belief processes of human subjects. PMID:26495984

Evaluation of a noninvasive command scheme for upper-limb prostheses in a virtual reality reach and grasp task.

PubMed

Kaliki, Rahul R; Davoodi, Rahman; Loeb, Gerald E

2013-03-01

C5/C6 tetraplegic patients and transhumeral amputees may be able to use voluntary shoulder motion as command signals for a functional electrical stimulation system or transhumeral prosthesis. Stereotyped relationships, termed "postural synergies," among the shoulder, forearm, and wrist joints emerge during goal-oriented reaching and transport movements as performed by able-bodied subjects. Thus, the posture of the shoulder can potentially be used to infer the desired posture of the elbow and forearm joints during reaching and transporting movements. We investigated how well able-bodied subjects could learn to use a noninvasive command scheme based on inferences from these postural synergies to control a simulated transhumeral prosthesis in a virtual reality task. We compared the performance of subjects using the inferential command scheme (ICS) with subjects operating the simulated prosthesis in virtual reality according to complete motion tracking of their actual arm and hand movements. Initially, subjects performed poorly with the ICS but improved rapidly with modest amounts of practice, eventually achieving performance only slightly less than subjects using complete motion tracking. Thus, inferring the desired movement of distal joints from voluntary shoulder movements appears to be an intuitive and noninvasive approach for obtaining command signals for prostheses to restore reaching and grasping functions.

Functional mechanisms of probabilistic inference in feature- and space-based attentional systems.

PubMed

Dombert, Pascasie L; Kuhns, Anna; Mengotti, Paola; Fink, Gereon R; Vossel, Simone

2016-11-15

Humans flexibly attend to features or locations and these processes are influenced by the probability of sensory events. We combined computational modeling of response times with fMRI to compare the functional correlates of (re-)orienting, and the modulation by probabilistic inference in spatial and feature-based attention systems. Twenty-four volunteers performed two task versions with spatial or color cues. Percentage of cue validity changed unpredictably. A hierarchical Bayesian model was used to derive trial-wise estimates of probability-dependent attention, entering the fMRI analysis as parametric regressors. Attentional orienting activated a dorsal frontoparietal network in both tasks, without significant parametric modulation. Spatially invalid trials activated a bilateral frontoparietal network and the precuneus, while invalid feature trials activated the left intraparietal sulcus (IPS). Probability-dependent attention modulated activity in the precuneus, left posterior IPS, middle occipital gyrus, and right temporoparietal junction for spatial attention, and in the left anterior IPS for feature-based and spatial attention. These findings provide novel insights into the generality and specificity of the functional basis of attentional control. They suggest that probabilistic inference can distinctively affect each attentional subsystem, but that there is an overlap in the left IPS, which responds to both spatial and feature-based expectancy violations. Copyright © 2016 Elsevier Inc. All rights reserved.

The Neural Basis of Event Simulation: An fMRI Study

PubMed Central

Yomogida, Yukihito; Sugiura, Motoaki; Akimoto, Yoritaka; Miyauchi, Carlos Makoto; Kawashima, Ryuta

2014-01-01

Event simulation (ES) is the situational inference process in which perceived event features such as objects, agents, and actions are associated in the brain to represent the whole situation. ES provides a common basis for various cognitive processes, such as perceptual prediction, situational understanding/prediction, and social cognition (such as mentalizing/trait inference). Here, functional magnetic resonance imaging was used to elucidate the neural substrates underlying important subdivisions within ES. First, the study investigated whether ES depends on different neural substrates when it is conducted explicitly and implicitly. Second, the existence of neural substrates specific to the future-prediction component of ES was assessed. Subjects were shown contextually related object pictures implying a situation and performed several picture–word-matching tasks. By varying task goals, subjects were made to infer the implied situation implicitly/explicitly or predict the future consequence of that situation. The results indicate that, whereas implicit ES activated the lateral prefrontal cortex and medial/lateral parietal cortex, explicit ES activated the medial prefrontal cortex, posterior cingulate cortex, and medial/lateral temporal cortex. Additionally, the left temporoparietal junction plays an important role in the future-prediction component of ES. These findings enrich our understanding of the neural substrates of the implicit/explicit/predictive aspects of ES-related cognitive processes. PMID:24789353

Motivational orientations and task autonomy fit: effects on organizational attraction.

PubMed

Wu, Yu-Chi

2012-02-01

The main purpose of this study was to investigate whether there is congruence between applicant needs (i.e., motivational orientations) and what is available (i.e., task autonomy) from an organizational perspective based on the fit between needs and supply. The fit between work motivation and task autonomy was examined to see whether it was associated with organizational attraction. This experimental study included two phases. Phase 1 participants consisted of 446 undergraduate students, of whom 228 were recruited to participate in Phase 2. The fit relations between task autonomy and intrinsic motivation and between task control and extrinsic motivation were characterized. Findings indicated that the fit between work motivation and task autonomy was positively associated with organizational attraction. Based on these results, it may be inferred that employers should emphasize job characteristics such as autonomy or control orientations to attract individuals, and focus on the most suitable work motivations for their organizations.

Finding user personal interests by tweet-mining using advanced machine learning algorithm in R

NASA Astrophysics Data System (ADS)

Krithika, L. B.; Roy, P.; Asha Jerlin, M.

2017-11-01

The social-media plays a key role in every individual’s life by anyone’s personal views about their liking-ness/disliking-ness. This methodology is a sharp departure from the traditional techniques of inferring interests of a user from the tweets that he/she posts or receives. It is showed that the topics of interest inferred by the proposed methodology are far superior than the topics extracted by state-of-the-art techniques such as using topic models (Labelled LDA) on tweets. Based upon the proposed methodology, a system has been built, “Who is interested in what”, which can infer the interests of millions of Twitter users. A novel mechanism is proposed to infer topics of interest of individual users in the twitter social network. It has been observed that in twitter, a user generally follows experts on various topics of his/her interest in order to acquire information on those topics. A methodology based on social annotations is used to first deduce the topical expertise of popular twitter users and then transitively infer the interests of the users who follow them.

The design and application of a Transportable Inference Engine (TIE1)

NASA Technical Reports Server (NTRS)

Mclean, David R.

1986-01-01

A Transportable Inference Engine (TIE1) system has been developed by the author as part of the Interactive Experimenter Planning System (IEPS) task which is involved with developing expert systems in support of the Spacecraft Control Programs Branch at Goddard Space Flight Center in Greenbelt, Maryland. Unlike traditional inference engines, TIE1 is written in the C programming language. In the TIE1 system, knowledge is represented by a hierarchical network of objects which have rule frames. The TIE1 search algorithm uses a set of strategies, including backward chaining, to obtain the values of goals. The application of TIE1 to a spacecraft scheduling problem is described. This application involves the development of a strategies interpreter which uses TIE1 to do constraint checking.

Neuronal integration of dynamic sources: Bayesian learning and Bayesian inference.

PubMed

Siegelmann, Hava T; Holzman, Lars E

2010-09-01

One of the brain's most basic functions is integrating sensory data from diverse sources. This ability causes us to question whether the neural system is computationally capable of intelligently integrating data, not only when sources have known, fixed relative dependencies but also when it must determine such relative weightings based on dynamic conditions, and then use these learned weightings to accurately infer information about the world. We suggest that the brain is, in fact, fully capable of computing this parallel task in a single network and describe a neural inspired circuit with this property. Our implementation suggests the possibility that evidence learning requires a more complex organization of the network than was previously assumed, where neurons have different specialties, whose emergence brings the desired adaptivity seen in human online inference.

Diagnostic causal reasoning with verbal information.

PubMed

Meder, Björn; Mayrhofer, Ralf

2017-08-01

In diagnostic causal reasoning, the goal is to infer the probability of causes from one or multiple observed effects. Typically, studies investigating such tasks provide subjects with precise quantitative information regarding the strength of the relations between causes and effects or sample data from which the relevant quantities can be learned. By contrast, we sought to examine people's inferences when causal information is communicated through qualitative, rather vague verbal expressions (e.g., "X occasionally causes A"). We conducted three experiments using a sequential diagnostic inference task, where multiple pieces of evidence were obtained one after the other. Quantitative predictions of different probabilistic models were derived using the numerical equivalents of the verbal terms, taken from an unrelated study with different subjects. We present a novel Bayesian model that allows for incorporating the temporal weighting of information in sequential diagnostic reasoning, which can be used to model both primacy and recency effects. On the basis of 19,848 judgments from 292 subjects, we found a remarkably close correspondence between the diagnostic inferences made by subjects who received only verbal information and those of a matched control group to whom information was presented numerically. Whether information was conveyed through verbal terms or numerical estimates, diagnostic judgments closely resembled the posterior probabilities entailed by the causes' prior probabilities and the effects' likelihoods. We observed interindividual differences regarding the temporal weighting of evidence in sequential diagnostic reasoning. Our work provides pathways for investigating judgment and decision making with verbal information within a computational modeling framework. Copyright © 2017 Elsevier Inc. All rights reserved.

Aspect-object alignment with Integer Linear Programming in opinion mining.

PubMed

Zhao, Yanyan; Qin, Bing; Liu, Ting; Yang, Wei

2015-01-01

Target extraction is an important task in opinion mining. In this task, a complete target consists of an aspect and its corresponding object. However, previous work has always simply regarded the aspect as the target itself and has ignored the important "object" element. Thus, these studies have addressed incomplete targets, which are of limited use for practical applications. This paper proposes a novel and important sentiment analysis task, termed aspect-object alignment, to solve the "object neglect" problem. The objective of this task is to obtain the correct corresponding object for each aspect. We design a two-step framework for this task. We first provide an aspect-object alignment classifier that incorporates three sets of features, namely, the basic, relational, and special target features. However, the objects that are assigned to aspects in a sentence often contradict each other and possess many complicated features that are difficult to incorporate into a classifier. To resolve these conflicts, we impose two types of constraints in the second step: intra-sentence constraints and inter-sentence constraints. These constraints are encoded as linear formulations, and Integer Linear Programming (ILP) is used as an inference procedure to obtain a final global decision that is consistent with the constraints. Experiments on a corpus in the camera domain demonstrate that the three feature sets used in the aspect-object alignment classifier are effective in improving its performance. Moreover, the classifier with ILP inference performs better than the classifier without it, thereby illustrating that the two types of constraints that we impose are beneficial.

Mixed Initiative Visual Analytics Using Task-Driven Recommendations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cook, Kristin A.; Cramer, Nicholas O.; Israel, David

2015-12-07

Visual data analysis is composed of a collection of cognitive actions and tasks to decompose, internalize, and recombine data to produce knowledge and insight. Visual analytic tools provide interactive visual interfaces to data to support tasks involved in discovery and sensemaking, including forming hypotheses, asking questions, and evaluating and organizing evidence. Myriad analytic models can be incorporated into visual analytic systems, at the cost of increasing complexity in the analytic discourse between user and system. Techniques exist to increase the usability of interacting with such analytic models, such as inferring data models from user interactions to steer the underlying modelsmore » of the system via semantic interaction, shielding users from having to do so explicitly. Such approaches are often also referred to as mixed-initiative systems. Researchers studying the sensemaking process have called for development of tools that facilitate analytic sensemaking through a combination of human and automated activities. However, design guidelines do not exist for mixed-initiative visual analytic systems to support iterative sensemaking. In this paper, we present a candidate set of design guidelines and introduce the Active Data Environment (ADE) prototype, a spatial workspace supporting the analytic process via task recommendations invoked by inferences on user interactions within the workspace. ADE recommends data and relationships based on a task model, enabling users to co-reason with the system about their data in a single, spatial workspace. This paper provides an illustrative use case, a technical description of ADE, and a discussion of the strengths and limitations of the approach.« less

A novel task assessing intention and emotion attribution: Italian standardization and normative data of the Story-based Empathy Task.

PubMed

Dodich, Alessandra; Cerami, Chiara; Canessa, Nicola; Crespi, Chiara; Iannaccone, Sandro; Marcone, Alessandra; Realmuto, Sabrina; Lettieri, Giada; Perani, Daniela; Cappa, Stefano F

2015-10-01

Theory of Mind (ToM), the process by which an individual imputes mental states to himself and others, is presently considered as a multidimensional cognitive domain, with two main facets (i.e., cognitive and affective ToM) accounting, respectively, for the ability to understand others' intention (intention attribution-IA) and emotions (emotion attribution-EA). Despite the large amount of literature investigating the behavioural and neural bases of mentalizing abilities in neurological conditions, there is still a lack of validated neuropsychological tools specifically designed to assess such skills. Here, we report the normative data of the Story-Based Empathy Task (SET), a non-verbal test developed for the assessment of intention and emotion attribution in the neurodegenerative conditions characterized by the impairment of social-emotional abilities. It is an easy-to-administer task including 18 stimuli, sub-grouped into two experimental conditions assessing, respectively, the ability to infer others' intentions (SET-IA) and emotions (SET-EA), compared to a control condition of causal inference (SET-CI). Normative data were collected in 136 Italian subjects pooled across subgroups homogenous for age (range 20-79 years), sex, and education (at least 5 years). The results show a detrimental effect of age and a beneficial effect of education on both the global score and each subscale, for which we provide correction grids. This new task could be a useful tool to investigate both affective and cognitive aspects of ToM in the course of disorders of socio-emotional behaviour, such as the fronto-temporal dementia spectrum.

From episodic to habitual prospective memory: ERP-evidence for a linear transition

PubMed Central

Meier, Beat; Matter, Sibylle; Baumann, Brigitta; Walter, Stefan; Koenig, Thomas

2014-01-01

Performing a prospective memory task repeatedly changes the nature of the task from episodic to habitual. The goal of the present study was to investigate the neural basis of this transition. In two experiments, we contrasted event-related potentials (ERPs) evoked by correct responses to prospective memory targets in the first, more episodic part of the experiment with those of the second, more habitual part of the experiment. Specifically, we tested whether the early, middle, or late ERP-components, which are thought to reflect cue detection, retrieval of the intention, and post-retrieval processes, respectively, would be changed by routinely performing the prospective memory task. The results showed a differential ERP effect in the middle time window (450–650 ms post-stimulus). Source localization using low resolution brain electromagnetic tomography analysis suggests that the transition was accompanied by an increase of activation in the posterior parietal and occipital cortex. These findings indicate that habitual prospective memory involves retrieval processes guided more strongly by parietal brain structures. In brief, the study demonstrates that episodic and habitual prospective memory tasks recruit different brain areas. PMID:25071519

A Tree-Ring Chronology and Paleoclimate Record for the Younger Dryas-Early Holocene Transition from Northeastern North America

NASA Technical Reports Server (NTRS)

Griggs, Carol; Peteet, Dorothy; Kromer, Bernd; Grote, Todd; Southon, John

2017-01-01

Spruce and tamarack logs dating from the Younger Dryas and Early Holocene (YDEH; approx. 12.9 - 11.3k cal a BP) were found at Bell Creek in the Lake Ontario lowlands of the Great Lakes region, North America. A 211-year tree-ring chronology dates to approx. 11 755 -11 545 cal a BP, across the YDEH transition. A 23-year period of higher year-to-year ring-width variability dates to around 11 650 cal a BP, infers strong regional climatic perturbations and may represent the end of the YD. Tamarack and spruce were dominant species throughout the YD - EH interval at the site, indicating that boreal conditions persisted into the EH, in contrast to geographical regions immediately south and east of the lowlands, but consistent with the Great Lakes interior lowlands. This infers that Bell Creek was at the eastern boundary of a boreal ecotone, perhaps a result of its lower elevation and the non-analog dynamics of the Laurentide Ice Sheet. This finding suggests that the ecotone boundary extended farther east during the YD - EH transition than previously thought.

Imprints from genetic drift and mutation imply relative divergence times across marine transition zones in a pan-European small pelagic fish (Sprattus sprattus).

PubMed

Limborg, M T; Hanel, R; Debes, P V; Ring, A K; André, C; Tsigenopoulos, C S; Bekkevold, D

2012-08-01

Geographic distributions of most temperate marine fishes are affected by postglacial recolonisation events, which have left complex genetic imprints on populations of marine species. This study investigated population structure and demographic history of European sprat (Sprattus sprattus L.) by combining inference from both mtDNA and microsatellite genetic markers throughout the species' distribution. We compared effects from genetic drift and mutation for both genetic markers in shaping genetic differentiation across four transition zones. Microsatellite markers revealed significant isolation by distance and a complex population structure across the species' distribution (overall θ(ST)=0.038, P<0.01). Across transition zones markers indicated larger effects of genetic drift over mutations in the northern distribution of sprat contrasting a stronger relative impact of mutation in the species' southern distribution in the Mediterranean region. These results were interpreted to reflect more recent divergence times between northern populations in accordance with previous findings. This study demonstrates the usefulness of comparing inference from different markers and estimators of divergence for phylogeographic and population genetic studies in species with weak genetic structure, as is the case in many marine species.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deming, Drake; Jackson, Brian; Jennings, Donald E.

We analyze 26 archival Kepler transits of the exo-Neptune HAT-P-11b, supplemented by ground-based transits observed in the blue (B band) and near-IR (J band). Both the planet and host star are smaller than previously believed; our analysis yields R{sub p} = 4.31 R{sub +} {+-} 0.06 R{sub +} and R{sub s} = 0.683 R{sub sun} {+-} 0.009 R{sub sun}, both about 3{sigma} smaller than the discovery values. Our ground-based transit data at wavelengths bracketing the Kepler bandpass serve to check the wavelength dependence of stellar limb darkening, and the J-band transit provides a precise and independent constraint on the transitmore » duration. Both the limb darkening and transit duration from our ground-based data are consistent with the new Kepler values for the system parameters. Our smaller radius for the planet implies that its gaseous envelope can be less extensive than previously believed, being very similar to the H-He envelope of GJ 436b and Kepler-4b. HAT-P-11 is an active star, and signatures of star spot crossings are ubiquitous in the Kepler transit data. We develop and apply a methodology to correct the planetary radius for the presence of both crossed and uncrossed star spots. Star spot crossings are concentrated at phases -0.002 and +0.006. This is consistent with inferences from Rossiter-McLaughlin measurements that the planet transits nearly perpendicular to the stellar equator. We identify the dominant phases of star spot crossings with active latitudes on the star, and infer that the stellar rotational pole is inclined at about 12{sup 0} {+-} 5{sup 0} to the plane of the sky. We point out that precise transit measurements over long durations could in principle allow us to construct a stellar Butterfly diagram to probe the cyclic evolution of magnetic activity on this active K-dwarf star.« less

Transitions in Care from Pediatric to Adult Health Care Providers: Ongoing Challenges and Opportunities for Young Persons with Diabetes.

PubMed

Garvey, Katharine; Laffel, Lori

2018-01-01

Adolescence and young adulthood are times of multiple developmental changes, including physiological, social, emotional, cognitive, and behavioral transformations. The adolescent or young adult living with type 1 or type 2 diabetes must navigate the vicissitudes of these developmental stages while managing the rigors and self-care demands of these conditions. Diabetes in children is managed by adults, mainly by parents. As the child matures, diabetes management tasks transition from parents to the developing teen. This transition in care is a process that generally begins in early adolescence and culminates when the older teen successfully accepts and manages diabetes self-care tasks. Along with the transitions in diabetes management tasks, older teens and young adults must be prepared for transfer from the pediatric diabetes care team to an adult-focused health care team. Numerous publications have described the challenges associated with both the process of transition and the act of transfer. Lack of preparation during transition followed by unsuccessful transfer often results in gaps in diabetes care exceeding 6 months, deterioration in glycemic control, increase in emergency room use and hospitalization, and emergence of diabetes complications among older teens and young adults. There is need for ongoing research internationally to address these deficiencies in order to improve the short- and long-term health of young persons with diabetes. © 2018 S. Karger AG, Basel.

Studies of Transitional Flow, Unsteady Separation Phenomena and Particle Induced Augmentation Heating on Ablated Nose Tips.

DTIC Science & Technology

1975-10-01

63 29 Variation of Profile Shape with Time for Axisyinmetric Camphor Models 63 30 The Development of Ablated Nose Shapes Over Which Flow...ablation tests using camphor models and inferred from downrange observation of full scale flight missions. Regions of gross instability on nose...been verified in wind tunnel tests of camphor models where shapes similar to those shown on Figure 29 can be developed under transitional conditions

Inference for Transition Network Grammars,

DTIC Science & Technology

1976-01-01

If the arc Is followed. language L(G) is said to be structurally complete if The power of an augmented transition network (Am) is each rewriting rule ...Clearly, a context-sensitive grammar can be represented as a context—free grarmar plus a set of transformationDbbbbb Eabbbbbb Dbb~~bb Ebbbbbb rules ...are the foun— as a CFG (base) and a set of transformationa l rules . datIons of grammars of different complexities. The The CSL Is obtained by appl

Explosive diversification following a benthic to pelagic shift in freshwater fishes

PubMed Central

2013-01-01

Background Interspecific divergence along a benthic to pelagic habitat axis is ubiquitous in freshwater fishes inhabiting lentic environments. In this study, we examined the influence of this habitat axis on the macroevolution of a diverse, lotic radiation using mtDNA and nDNA phylogenies for eastern North America’s most species-rich freshwater fish clade, the open posterior myodome (OPM) cyprinids. We used ancestral state reconstruction to identify the earliest benthic to pelagic transition in this group and generated fossil-calibrated estimates of when this shift occurred. This transition could have represented evolution into a novel adaptive zone, and therefore, we tested for a period of accelerated lineage accumulation after this historical habitat shift. Results Ancestral state reconstructions inferred a similar and concordant region of our mtDNA and nDNA based gene trees as representing the shift from benthic to pelagic habitats in the OPM clade. Two independent tests conducted on each gene tree suggested an increased diversification rate after this inferred habitat transition. Furthermore, lineage through time analyses indicated rapid early cladogenesis in the clade arising after the benthic to pelagic shift. Conclusions A burst of diversification followed the earliest benthic to pelagic transition during the radiation of OPM cyprinids in eastern North America. As such, the benthic/pelagic habitat axis has likely influenced the generation of biodiversity across disparate freshwater ecosystems. PMID:24341464

Automated medication reconciliation and complexity of care transitions.

PubMed

Silva, Pamela A Bozzo; Bernstam, Elmer V; Markowitz, Eliz; Johnson, Todd R; Zhang, Jiajie; Herskovic, Jorge R

2011-01-01

Medication reconciliation is a National Patient Safety Goal (NPSG) from The Joint Commission (TJC) that entails reviewing all medications a patient takes after a health care transition. Medication reconciliation is a resource-intensive, error-prone task, and the resources to accomplish it may not be routinely available. Computer-based methods have the potential to overcome these barriers. We designed and explored a rule-based medication reconciliation algorithm to accomplish this task across different healthcare transitions. We tested our algorithm on a random sample of 94 transitions from the Clinical Data Warehouse at the University of Texas Health Science Center at Houston. We found that the algorithm reconciled, on average, 23.4% of the potentially reconcilable medications. Our study did not have sufficient statistical power to establish whether the kind of transition affects reconcilability. We conclude that automated reconciliation is possible and will help accomplish the NPSG.

Algorithm Optimally Orders Forward-Chaining Inference Rules

NASA Technical Reports Server (NTRS)

James, Mark

2008-01-01

People typically develop knowledge bases in a somewhat ad hoc manner by incrementally adding rules with no specific organization. This often results in a very inefficient execution of those rules since they are so often order sensitive. This is relevant to tasks like Deep Space Network in that it allows the knowledge base to be incrementally developed and have it automatically ordered for efficiency. Although data flow analysis was first developed for use in compilers for producing optimal code sequences, its usefulness is now recognized in many software systems including knowledge-based systems. However, this approach for exhaustively computing data-flow information cannot directly be applied to inference systems because of the ubiquitous execution of the rules. An algorithm is presented that efficiently performs a complete producer/consumer analysis for each antecedent and consequence clause in a knowledge base to optimally order the rules to minimize inference cycles. An algorithm was developed that optimally orders a knowledge base composed of forwarding chaining inference rules such that independent inference cycle executions are minimized, thus, resulting in significantly faster execution. This algorithm was integrated into the JPL tool Spacecraft Health Inference Engine (SHINE) for verification and it resulted in a significant reduction in inference cycles for what was previously considered an ordered knowledge base. For a knowledge base that is completely unordered, then the improvement is much greater.

Causal learning and inference as a rational process: the new synthesis.

PubMed

Holyoak, Keith J; Cheng, Patricia W

2011-01-01

Over the past decade, an active line of research within the field of human causal learning and inference has converged on a general representational framework: causal models integrated with bayesian probabilistic inference. We describe this new synthesis, which views causal learning and inference as a fundamentally rational process, and review a sample of the empirical findings that support the causal framework over associative alternatives. Causal events, like all events in the distal world as opposed to our proximal perceptual input, are inherently unobservable. A central assumption of the causal approach is that humans (and potentially nonhuman animals) have been designed in such a way as to infer the most invariant causal relations for achieving their goals based on observed events. In contrast, the associative approach assumes that learners only acquire associations among important observed events, omitting the representation of the distal relations. By incorporating bayesian inference over distributions of causal strength and causal structures, along with noisy-logical (i.e., causal) functions for integrating the influences of multiple causes on a single effect, human judgments about causal strength and structure can be predicted accurately for relatively simple causal structures. Dynamic models of learning based on the causal framework can explain patterns of acquisition observed with serial presentation of contingency data and are consistent with available neuroimaging data. The approach has been extended to a diverse range of inductive tasks, including category-based and analogical inferences.

Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons

PubMed Central

Pecevski, Dejan; Buesing, Lars; Maass, Wolfgang

2011-01-01

An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows (“explaining away”) and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons. PMID:22219717

Ontological Constraints in Children's Inductive Inferences: Evidence From a Comparison of Inferences Within Animals and Vehicles.

PubMed

Tarlowski, Andrzej

2018-01-01

There is a lively debate concerning the role of conceptual and perceptual information in young children's inductive inferences. While most studies focus on the role of basic level categories in induction the present research contributes to the debate by asking whether children's inductions are guided by ontological constraints. Two studies use a novel inductive paradigm to test whether young children have an expectation that all animals share internal commonalities that do not extend to perceptually similar inanimates. The results show that children make category-consistent responses when asked to project an internal feature from an animal to either a dissimilar animal or a similar toy replica. However, the children do not have a universal preference for category-consistent responses in an analogous task involving vehicles and vehicle toy replicas. The results also show the role of context and individual factors in inferences. Children's early reliance on ontological commitments in induction cannot be explained by perceptual similarity or by children's sensitivity to the authenticity of objects.

Ontological Constraints in Children's Inductive Inferences: Evidence From a Comparison of Inferences Within Animals and Vehicles

PubMed Central

Tarlowski, Andrzej

2018-01-01

There is a lively debate concerning the role of conceptual and perceptual information in young children's inductive inferences. While most studies focus on the role of basic level categories in induction the present research contributes to the debate by asking whether children's inductions are guided by ontological constraints. Two studies use a novel inductive paradigm to test whether young children have an expectation that all animals share internal commonalities that do not extend to perceptually similar inanimates. The results show that children make category-consistent responses when asked to project an internal feature from an animal to either a dissimilar animal or a similar toy replica. However, the children do not have a universal preference for category-consistent responses in an analogous task involving vehicles and vehicle toy replicas. The results also show the role of context and individual factors in inferences. Children's early reliance on ontological commitments in induction cannot be explained by perceptual similarity or by children's sensitivity to the authenticity of objects. PMID:29760669

Causal Inference and Explaining Away in a Spiking Network

PubMed Central

Moreno-Bote, Rubén; Drugowitsch, Jan

2015-01-01

While the brain uses spiking neurons for communication, theoretical research on brain computations has mostly focused on non-spiking networks. The nature of spike-based algorithms that achieve complex computations, such as object probabilistic inference, is largely unknown. Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons. The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities. The network infers the set of most likely causes from an observation using explaining away, which is dynamically implemented by spike-based, tuned inhibition. The algorithm performs remarkably well even when the network intrinsically generates variable spike trains, the timing of spikes is scrambled by external sources of noise, or the network is mistuned. This type of network might underlie tasks such as odor identification and classification. PMID:26621426

Causal Inference and Explaining Away in a Spiking Network.

PubMed

Moreno-Bote, Rubén; Drugowitsch, Jan

2015-12-01

While the brain uses spiking neurons for communication, theoretical research on brain computations has mostly focused on non-spiking networks. The nature of spike-based algorithms that achieve complex computations, such as object probabilistic inference, is largely unknown. Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons. The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities. The network infers the set of most likely causes from an observation using explaining away, which is dynamically implemented by spike-based, tuned inhibition. The algorithm performs remarkably well even when the network intrinsically generates variable spike trains, the timing of spikes is scrambled by external sources of noise, or the network is mistuned. This type of network might underlie tasks such as odor identification and classification.

Sequential inference as a mode of cognition and its correlates in fronto-parietal and hippocampal brain regions

PubMed Central

Friston, Karl J.; Dolan, Raymond J.

2017-01-01

Normative models of human cognition often appeal to Bayesian filtering, which provides optimal online estimates of unknown or hidden states of the world, based on previous observations. However, in many cases it is necessary to optimise beliefs about sequences of states rather than just the current state. Importantly, Bayesian filtering and sequential inference strategies make different predictions about beliefs and subsequent choices, rendering them behaviourally dissociable. Taking data from a probabilistic reversal task we show that subjects’ choices provide strong evidence that they are representing short sequences of states. Between-subject measures of this implicit sequential inference strategy had a neurobiological underpinning and correlated with grey matter density in prefrontal and parietal cortex, as well as the hippocampus. Our findings provide, to our knowledge, the first evidence for sequential inference in human cognition, and by exploiting between-subject variation in this measure we provide pointers to its neuronal substrates. PMID:28486504

Task-specific activity and connectivity within the mentalizing network during emotion and intention mentalizing.

PubMed

Atique, Bijoy; Erb, Michael; Gharabaghi, Alireza; Grodd, Wolfgang; Anders, Silke

2011-04-15

Mentalizing, i.e. the process of inferring another person's mental state, is thought to be primarily subserved by three brain regions, the VMPFC (ventromedial prefrontal cortex), precuneus and TPJ (temporo-parietal junction). However, it is still unclear what the exact roles of these regions in mentalizing are. Here, we compare activity within, and functional connectivity between, the VMPFC, precuneus and TPJ during two different mentalizing tasks. Specifically, we examine whether inferring another person's emotion ("emotion mentalizing") and inferring another person's intention ("intention mentalizing") activate similar or distinct subregions within the VMPFC, precuneus and TPJ, and whether these different kinds of mentalizing are associated with different patterns of functional connectivity between these regions. Our results indicate that emotion mentalizing and intention mentalizing activate partly distinct subregions of the right and left TPJ that can be spatially separated across participants. These subregions also showed different patterns of functional connectivity with the VMPFC: a more anterior region of the right and left TPJ, which was more strongly activated during emotion mentalizing, showed stronger functional connectivity with the VMPFC, particularly during emotion mentalizing, than a more posterior region that was more strongly activated during intention mentalizing. Critically, this double dissociation became evident only when the fine-scale distribution of activity within activated regions was analysed, and despite the fact that there was also a significant overlap of activity during the two tasks. Our findings provide first evidence that different neural modules might have evolved within the TPJ that show distinct patterns of functional connectivity and might subserve slightly different subfunctions of mentalizing. Copyright © 2010 Elsevier Inc. All rights reserved.

Evaluation of Physicians' Cognitive Styles.

PubMed

Djulbegovic, Benjamin; Beckstead, Jason W; Elqayam, Shira; Reljic, Tea; Hozo, Iztok; Kumar, Ambuj; Cannon-Bowers, Janis; Taylor, Stephanie; Tsalatsanis, Athanasios; Turner, Brandon; Paidas, Charles

2014-07-01

Patient outcomes critically depend on accuracy of physicians' judgment, yet little is known about individual differences in cognitive styles that underlie physicians' judgments. The objective of this study was to assess physicians' individual differences in cognitive styles relative to age, experience, and degree and type of training. Physicians at different levels of training and career completed a web-based survey of 6 scales measuring individual differences in cognitive styles (maximizing v. satisficing, analytical v. intuitive reasoning, need for cognition, intolerance toward ambiguity, objectivism, and cognitive reflection). We measured psychometric properties (Cronbach's α) of scales; relationship of age, experience, degree, and type of training; responses to scales; and accuracy on conditional inference task. The study included 165 trainees and 56 attending physicians (median age 31 years; range 25-69 years). All 6 constructs showed acceptable psychometric properties. Surprisingly, we found significant negative correlation between age and satisficing (r = -0.239; P = 0.017). Maximizing (willingness to engage in alternative search strategy) also decreased with age (r = -0.220; P = 0.047). Number of incorrect inferences negatively correlated with satisficing (r = -0.246; P = 0.014). Disposition to suppress intuitive responses was associated with correct responses on 3 of 4 inferential tasks. Trainees showed a tendency to engage in analytical thinking (r = 0.265; P = 0.025), while attendings displayed inclination toward intuitive-experiential thinking (r = 0.427; P = 0.046). However, trainees performed worse on conditional inference task. Physicians capable of suppressing an immediate intuitive response to questions and those scoring higher on rational thinking made fewer inferential mistakes. We found a negative correlation between age and maximizing: Physicians who were more advanced in their careers were less willing to spend time and effort in an exhaustive search for solutions. However, they appeared to have maintained their "mindware" for effective problem solving. © The Author(s) 2014.

Timing of repetition suppression of event-related potentials to unattended objects.

PubMed

Stefanics, Gabor; Heinzle, Jakob; Czigler, István; Valentini, Elia; Stephan, Klaas Enno

2018-05-26

Current theories of object perception emphasize the automatic nature of perceptual inference. Repetition suppression (RS), the successive decrease of brain responses to repeated stimuli, is thought to reflect the optimization of perceptual inference through neural plasticity. While functional imaging studies revealed brain regions that show suppressed responses to the repeated presentation of an object, little is known about the intra-trial time course of repetition effects to everyday objects. Here we used event-related potentials (ERP) to task-irrelevant line-drawn objects, while participants engaged in a distractor task. We quantified changes in ERPs over repetitions using three general linear models (GLM) that modelled RS by an exponential, linear, or categorical "change detection" function in each subject. Our aim was to select the model with highest evidence and determine the within-trial time-course and scalp distribution of repetition effects using that model. Model comparison revealed the superiority of the exponential model indicating that repetition effects are observable for trials beyond the first repetition. Model parameter estimates revealed a sequence of RS effects in three time windows (86-140ms, 322-360ms, and 400-446ms) and with occipital, temporo-parietal, and fronto-temporal distribution, respectively. An interval of repetition enhancement (RE) was also observed (320-340ms) over occipito-temporal sensors. Our results show that automatic processing of task-irrelevant objects involves multiple intervals of RS with distinct scalp topographies. These sequential intervals of RS and RE might reflect the short-term plasticity required for optimization of perceptual inference and the associated changes in prediction errors (PE) and predictions, respectively, over stimulus repetitions during automatic object processing. This article is protected by copyright. All rights reserved. © 2018 The Authors European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

The Dopaminergic Midbrain Encodes the Expected Certainty about Desired Outcomes.

PubMed

Schwartenbeck, Philipp; FitzGerald, Thomas H B; Mathys, Christoph; Dolan, Ray; Friston, Karl

2015-10-01

Dopamine plays a key role in learning; however, its exact function in decision making and choice remains unclear. Recently, we proposed a generic model based on active (Bayesian) inference wherein dopamine encodes the precision of beliefs about optimal policies. Put simply, dopamine discharges reflect the confidence that a chosen policy will lead to desired outcomes. We designed a novel task to test this hypothesis, where subjects played a "limited offer" game in a functional magnetic resonance imaging experiment. Subjects had to decide how long to wait for a high offer before accepting a low offer, with the risk of losing everything if they waited too long. Bayesian model comparison showed that behavior strongly supported active inference, based on surprise minimization, over classical utility maximization schemes. Furthermore, midbrain activity, encompassing dopamine projection neurons, was accurately predicted by trial-by-trial variations in model-based estimates of precision. Our findings demonstrate that human subjects infer both optimal policies and the precision of those inferences, and thus support the notion that humans perform hierarchical probabilistic Bayesian inference. In other words, subjects have to infer both what they should do as well as how confident they are in their choices, where confidence may be encoded by dopaminergic firing. © The Author 2014. Published by Oxford University Press.

The Dopaminergic Midbrain Encodes the Expected Certainty about Desired Outcomes

PubMed Central

Schwartenbeck, Philipp; FitzGerald, Thomas H. B.; Mathys, Christoph; Dolan, Ray; Friston, Karl

2015-01-01

Dopamine plays a key role in learning; however, its exact function in decision making and choice remains unclear. Recently, we proposed a generic model based on active (Bayesian) inference wherein dopamine encodes the precision of beliefs about optimal policies. Put simply, dopamine discharges reflect the confidence that a chosen policy will lead to desired outcomes. We designed a novel task to test this hypothesis, where subjects played a “limited offer” game in a functional magnetic resonance imaging experiment. Subjects had to decide how long to wait for a high offer before accepting a low offer, with the risk of losing everything if they waited too long. Bayesian model comparison showed that behavior strongly supported active inference, based on surprise minimization, over classical utility maximization schemes. Furthermore, midbrain activity, encompassing dopamine projection neurons, was accurately predicted by trial-by-trial variations in model-based estimates of precision. Our findings demonstrate that human subjects infer both optimal policies and the precision of those inferences, and thus support the notion that humans perform hierarchical probabilistic Bayesian inference. In other words, subjects have to infer both what they should do as well as how confident they are in their choices, where confidence may be encoded by dopaminergic firing. PMID:25056572

Behavioral and Neural Signatures of Reduced Updating of Alternative Options in Alcohol-Dependent Patients during Flexible Decision-Making.

PubMed

Reiter, Andrea M F; Deserno, Lorenz; Kallert, Thomas; Heinze, Hans-Jochen; Heinz, Andreas; Schlagenhauf, Florian

2016-10-26

Addicted individuals continue substance use despite the knowledge of harmful consequences and often report having no choice but to consume. Computational psychiatry accounts have linked this clinical observation to difficulties in making flexible and goal-directed decisions in dynamic environments via consideration of potential alternative choices. To probe this in alcohol-dependent patients (n = 43) versus healthy volunteers (n = 35), human participants performed an anticorrelated decision-making task during functional neuroimaging. Via computational modeling, we investigated behavioral and neural signatures of inference regarding the alternative option. While healthy control subjects exploited the anticorrelated structure of the task to guide decision-making, alcohol-dependent patients were relatively better explained by a model-free strategy due to reduced inference on the alternative option after punishment. Whereas model-free prediction error signals were preserved, alcohol-dependent patients exhibited blunted medial prefrontal signatures of inference on the alternative option. This reduction was associated with patients' behavioral deficit in updating the alternative choice option and their obsessive-compulsive drinking habits. All results remained significant when adjusting for potential confounders (e.g., neuropsychological measures and gray matter density). A disturbed integration of alternative choice options implemented by the medial prefrontal cortex appears to be one important explanation for the puzzling question of why addicted individuals continue drug consumption despite negative consequences. In addiction, patients maintain substance use despite devastating consequences and often report having no choice but to consume. These clinical observations have been theoretically linked to disturbed mechanisms of inference, for example, to difficulties when learning statistical regularities of the environmental structure to guide decisions. Using computational modeling, we demonstrate disturbed inference on alternative choice options in alcohol addiction. Patients neglecting "what might have happened" was accompanied by blunted coding of inference regarding alternative choice options in the medial prefrontal cortex. An impaired integration of alternative choice options implemented by the medial prefrontal cortex might contribute to ongoing drug consumption in the face of evident negative consequences. Copyright © 2016 the authors 0270-6474/16/3610935-14$15.00/0.

Postural control of typical developing boys during the transition from double-leg stance to single-leg stance.

PubMed

Deschamps, Kevin; Staes, Filip; Peerlinck, Kathelijne; Van Geet, Kristel; Hermans, Cedric; Lobet, Sebastien

2017-02-01

Literature is lacking information about postural control performance of typically developing children during a transition task from double-leg stance to single-leg stance. The purpose of the present study was therefore to evaluate the clinical feasibility of a transition task in typical developing age groups as well as to study the correlation between associated balance measures and age.Thirty-three typically developing boys aged 6-20 years performed a standard transition task from DLS to SLS with eyes open (EO) and eyes closed (EC). Balance features derived from the center of pressure displacement captured by a single force platform were correlated with age on the one hand and considered for differences in the perspective of limb dominance on the other hand.All TDB (typically developing boys) were able to perform the transition task with EO. With respect to EC condition, all TDB from the age group 6-7 years and the youngest of the age group 8-12 years (N = 4) were unable to perform the task. No significant differences were observed between the balance measures of the dominant and non-dominant limbs.With respect to EO condition, correlation analyses indicated that time to new stability point (TNSP) as well as the sway measure after this TNSP were correlated with age (p < 0.0001). For the EC condition, only the anthropometrically scaled sway measure was found to be correlated (p = 0.03). The results provide additional insight into balance development in childhood and may serve as a useful basis for assessing balance impairments in higher functioning children with musculoskeletal problems. What is Known: • Reference data regarding postural balance of typically developing children during walking, running, sit-to-stand, and bipodal and unipodal stance has been well documented in the literature. • These reference data provided not only insight into the maturation process of the postural control system, but also served in diagnosing and managing functional repercussions of neurological and orthopedic pathologies. What is New: • Objective data regarding postural balance of typical developing children during a transition task from double-leg stance to single-leg stance. • Insight into the role of maturation on the postural control system.

Improved orthologous databases to ease protozoan targets inference.

PubMed

Kotowski, Nelson; Jardim, Rodrigo; Dávila, Alberto M R

2015-09-29

Homology inference helps on identifying similarities, as well as differences among organisms, which provides a better insight on how closely related one might be to another. In addition, comparative genomics pipelines are widely adopted tools designed using different bioinformatics applications and algorithms. In this article, we propose a methodology to build improved orthologous databases with the potential to aid on protozoan target identification, one of the many tasks which benefit from comparative genomics tools. Our analyses are based on OrthoSearch, a comparative genomics pipeline originally designed to infer orthologs through protein-profile comparison, supported by an HMM, reciprocal best hits based approach. Our methodology allows OrthoSearch to confront two orthologous databases and to generate an improved new one. Such can be later used to infer potential protozoan targets through a similarity analysis against the human genome. The protein sequences of Cryptosporidium hominis, Entamoeba histolytica and Leishmania infantum genomes were comparatively analyzed against three orthologous databases: (i) EggNOG KOG, (ii) ProtozoaDB and (iii) Kegg Orthology (KO). That allowed us to create two new orthologous databases, "KO + EggNOG KOG" and "KO + EggNOG KOG + ProtozoaDB", with 16,938 and 27,701 orthologous groups, respectively. Such new orthologous databases were used for a regular OrthoSearch run. By confronting "KO + EggNOG KOG" and "KO + EggNOG KOG + ProtozoaDB" databases and protozoan species we were able to detect the following total of orthologous groups and coverage (relation between the inferred orthologous groups and the species total number of proteins): Cryptosporidium hominis: 1,821 (11 %) and 3,254 (12 %); Entamoeba histolytica: 2,245 (13 %) and 5,305 (19 %); Leishmania infantum: 2,702 (16 %) and 4,760 (17 %). Using our HMM-based methodology and the largest created orthologous database, it was possible to infer 13 orthologous groups which represent potential protozoan targets; these were found because of our distant homology approach. We also provide the number of species-specific, pair-to-pair and core groups from such analyses, depicted in Venn diagrams. The orthologous databases generated by our HMM-based methodology provide a broader dataset, with larger amounts of orthologous groups when compared to the original databases used as input. Those may be used for several homology inference analyses, annotation tasks and protozoan targets identification.

Social Cognition Psychometric Evaluation: Results of the Initial Psychometric Study

PubMed Central

Pinkham, Amy E.; Penn, David L.; Green, Michael F.; Harvey, Philip D.

2016-01-01

Measurement of social cognition in treatment trials remains problematic due to poor and limited psychometric data for many tasks. As part of the Social Cognition Psychometric Evaluation (SCOPE) study, the psychometric properties of 8 tasks were assessed. One hundred and seventy-nine stable outpatients with schizophrenia and 104 healthy controls completed the battery at baseline and a 2–4-week retest period at 2 sites. Tasks included the Ambiguous Intentions Hostility Questionnaire (AIHQ), Bell Lysaker Emotion Recognition Task (BLERT), Penn Emotion Recognition Task (ER-40), Relationships Across Domains (RAD), Reading the Mind in the Eyes Task (Eyes), The Awareness of Social Inferences Test (TASIT), Hinting Task, and Trustworthiness Task. Tasks were evaluated on: (i) test-retest reliability, (ii) utility as a repeated measure, (iii) relationship to functional outcome, (iv) practicality and tolerability, (v) sensitivity to group differences, and (vi) internal consistency. The BLERT and Hinting task showed the strongest psychometric properties across all evaluation criteria and are recommended for use in clinical trials. The ER-40, Eyes Task, and TASIT showed somewhat weaker psychometric properties and require further study. The AIHQ, RAD, and Trustworthiness Task showed poorer psychometric properties that suggest caution for their use in clinical trials. PMID:25943125

Advancing Scientific Reasoning in Upper Elementary Classrooms: Direct Instruction Versus Task Structuring

NASA Astrophysics Data System (ADS)

Lazonder, Ard W.; Wiskerke-Drost, Sjanou

2015-02-01

Several studies found that direct instruction and task structuring can effectively promote children's ability to design unconfounded experiments. The present study examined whether the impact of these interventions extends to other scientific reasoning skills by comparing the inquiry activities of 55 fifth-graders randomly assigned to one of three conditions. Children in the control condition investigated a four-variable inquiry task without additional support. Performance of this task in the direct instruction condition was preceded by a short training in experimental design, whereas children in the task structuring condition, who did not receive the introductory training, were given a version of the task that addressed the four variables one at a time. Analysis of children's experimentation behavior confirmed that direct instruction and task structuring are equally effective and superior to unguided inquiry. Both interventions also evoked more determinate predictions and valid inferences. These findings demonstrate that the effect of short-term interventions designed to promote unconfounded experimentation extends beyond the control of variables.

Formant transitions in the fluent speech of Farsi-speaking people who stutter.

PubMed

Dehqan, Ali; Yadegari, Fariba; Blomgren, Michael; Scherer, Ronald C

2016-06-01

Second formant (F2) transitions can be used to infer attributes of articulatory transitions. This study compared formant transitions during fluent speech segments of Farsi (Persian) speaking people who stutter and normally fluent Farsi speakers. Ten Iranian males who stutter and 10 normally fluent Iranian males participated. Sixteen different "CVt" tokens were embedded within the phrase "Begu CVt an". Measures included overall F2 transition frequency extents, durations, and derived overall slopes, initial F2 transition slopes at 30ms and 60ms, and speaking rate. (1) Mean overall formant frequency extent was significantly greater in 14 of the 16 CVt tokens for the group of stuttering speakers. (2) Stuttering speakers exhibited significantly longer overall F2 transitions for all 16 tokens compared to the nonstuttering speakers. (3) The overall F2 slopes were similar between the two groups. (4) The stuttering speakers exhibited significantly greater initial F2 transition slopes (positive or negative) for five of the 16 tokens at 30ms and six of the 16 tokens at 60ms. (5) The stuttering group produced a slower syllable rate than the non-stuttering group. During perceptually fluent utterances, the stuttering speakers had greater F2 frequency extents during transitions, took longer to reach vowel steady state, exhibited some evidence of steeper slopes at the beginning of transitions, had overall similar F2 formant slopes, and had slower speaking rates compared to nonstuttering speakers. Findings support the notion of different speech motor timing strategies in stuttering speakers. Findings are likely to be independent of the language spoken. Educational objectives This study compares aspects of F2 formant transitions between 10 stuttering and 10 nonstuttering speakers. Readers will be able to describe: (a) characteristics of formant frequency as a specific acoustic feature used to infer speech movements in stuttering and nonstuttering speakers, (b) two methods of measuring second formant (F2) transitions: the visual criteria method and fixed time criteria method, (c) characteristics of F2 transitions in the fluent speech of stuttering speakers and how those characteristics appear to differ from normally fluent speakers, and (d) possible cross-linguistic effects on acoustic analyses of stuttering. Copyright © 2016 Elsevier Inc. All rights reserved.

Learning to think like a user: using cognitive task analysis to meet today's health care design challenges.

PubMed

Militello, L G

1998-01-01

The growing role of information technology in our society has changed the very nature of many of the tasks that workers are called on to perform. Technology has resulted in a dramatic reduction in the number of proceduralized, rote tasks that workers must face. The impact of technology on many tasks and functions has been to greatly increase demands on the cognitive skills of workers. More procedural or predictable tasks are now handled by smart machines, while workers are responsible for tasks that require inference, diagnosis, judgment, and decision making. The increase in the cognitive demands placed on workers and the redistribution of tasks have created a need for a better understanding of the cognitive components of many tasks. This need has been recognized by many in the health care domain, including the U.S. Food and Drug Administration (FDA). Recent FDA regulations encourage the use of human factors in the development of medical devices, instruments, and systems. One promising set of methods for filling this need is cognitive task analysis.

Testing Transitivity of Preferences on Two-Alternative Forced Choice Data

PubMed Central

Regenwetter, Michel; Dana, Jason; Davis-Stober, Clintin P.

2010-01-01

As Duncan Luce and other prominent scholars have pointed out on several occasions, testing algebraic models against empirical data raises difficult conceptual, mathematical, and statistical challenges. Empirical data often result from statistical sampling processes, whereas algebraic theories are nonprobabilistic. Many probabilistic specifications lead to statistical boundary problems and are subject to nontrivial order constrained statistical inference. The present paper discusses Luce's challenge for a particularly prominent axiom: Transitivity. The axiom of transitivity is a central component in many algebraic theories of preference and choice. We offer the currently most complete solution to the challenge in the case of transitivity of binary preference on the theory side and two-alternative forced choice on the empirical side, explicitly for up to five, and implicitly for up to seven, choice alternatives. We also discuss the relationship between our proposed solution and weak stochastic transitivity. We recommend to abandon the latter as a model of transitive individual preferences. PMID:21833217

Circadian Rhythms in Executive Function during the Transition to Adolescence: The Effect of Synchrony between Chronotype and Time of Day

ERIC Educational Resources Information Center

Hahn, Constanze; Cowell, Jason M.; Wiprzycka, Ursula J.; Goldstein, David; Ralph, Martin; Hasher, Lynn; Zelazo, Philip David

2012-01-01

To explore the influence of circadian rhythms on executive function during early adolescence, we administered a battery of executive function measures (including a Go-Nogo task, the Iowa Gambling Task, a Self-ordered Pointing task, and an Intra/Extradimensional Shift task) to Morning-preference and Evening-preference participants (N = 80) between…

The Role of Cue-Target Translation in Backward Inhibition of Attentional Set

ERIC Educational Resources Information Center

Houghton, George; Pritchard, Rhys; Grange, James A.

2009-01-01

Backward inhibition (BI) refers to a reaction time cost incurred when returning to a recently abandoned task compared to returning to a task not recently performed. The effect has been proposed to reflect an inhibitory mechanism that aids transition from one task to another. The question arises as to precisely what aspects of a task may be…

77 FR 23667 - Department of Defense Task Force on the Care, Management, and Transition of Recovering Wounded...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-04-20

... CONTACT: Mail Delivery service through Recovering Warrior Task Force, Hoffman Building II, 200 Stovall St... Review of Non- Medical Case Management. 9:30-9:45 a.m. Break. 9:45-10:45 a.m. Task Force Recommendation... Task Force through the contact information in FOR FURTHER INFORMATION CONTACT, and this individual will...

Project FARE task III report : urban mass transportation industry reporting system design : interim task III report for November 1972-June 1973 period. Part III - Reporting system forms.

DOT National Transportation Integrated Search

1973-06-01

The report contains a description of the proposed uniform reporting system for the urban mass transit industry. It is presented in four volumes: Part I - Task Summary contains a description of how Task III was accomplished and the conclusions and rec...

Project FARE task III report : urban mass transportation industry reporting system design : interim task III report for November 1972-June 1973 period. Part IV - Commuter rail reporting.

DOT National Transportation Integrated Search

1973-06-01

This report contains a description of the proposed uniform reporting system for the urban mass transit industry. It is presented in four volumes: Part I - Task Summary contains a description of how Task III was accomplished and the conclusions and re...

Project FARE task III report : urban mass transportation industry reporting system design : interim task III report for November 1972-June 1973 period. Part II - Reporting system instructions.

DOT National Transportation Integrated Search

1973-06-01

This report contains a description of the proposed uniform reporting system for the urban mass transit industry. It is presented in four volumes: Part I - Task Summary contains a description of how Task III was accomplished and the conclusions and re...

Online mentalising investigated with functional MRI.

PubMed

Kircher, Tilo; Blümel, Isabelle; Marjoram, Dominic; Lataster, Tineke; Krabbendam, Lydia; Weber, Jochen; van Os, Jim; Krach, Sören

2009-05-01

For successful interpersonal communication, inferring intentions, goals or desires of others is highly advantageous. Increasingly, humans also interact with computers or robots. In this study, we sought to determine to what degree an interactive task, which involves receiving feedback from social partners that can be used to infer intent, engaged the medial prefrontal cortex, a region previously associated with Theory of Mind processes among others. Participants were scanned using fMRI as they played an adapted version of the Prisoner's Dilemma Game with alleged human and computer partners who were outside the scanner. The medial frontal cortex was activated when both human and computer partner were played, while the direct contrast revealed significantly stronger signal change during the human-human interaction. The results suggest a link between activity in the medial prefrontal cortex and the partner played in a mentalising task. This signal change was also present for to the computers partner. Implying agency or a will to non-human actors might be an innate human resource that could lead to an evolutionary advantage.

Tensor-product kernel-based representation encoding joint MRI view similarity.

PubMed

Alvarez-Meza, A; Cardenas-Pena, D; Castro-Ospina, A E; Alvarez, M; Castellanos-Dominguez, G

2014-01-01

To support 3D magnetic resonance image (MRI) analysis, a marginal image similarity (MIS) matrix holding MR inter-slice relationship along every axis view (Axial, Coronal, and Sagittal) can be estimated. However, mutual inference from MIS view information poses a difficult task since relationships between axes are nonlinear. To overcome this issue, we introduce a Tensor-Product Kernel-based Representation (TKR) that allows encoding brain structure patterns due to patient differences, gathering all MIS matrices into a single joint image similarity framework. The TKR training strategy is carried out into a low dimensional projected space to get less influence of voxel-derived noise. Obtained results for classifying the considered patient categories (gender and age) on real MRI database shows that the proposed TKR training approach outperforms the conventional voxel-wise sum of squared differences. The proposed approach may be useful to support MRI clustering and similarity inference tasks, which are required on template-based image segmentation and atlas construction.

PyTranSpot: A tool for multiband light curve modeling of planetary transits and stellar spots

NASA Astrophysics Data System (ADS)

Juvan, Ines G.; Lendl, M.; Cubillos, P. E.; Fossati, L.; Tregloan-Reed, J.; Lammer, H.; Guenther, E. W.; Hanslmeier, A.

2018-02-01

Several studies have shown that stellar activity features, such as occulted and non-occulted starspots, can affect the measurement of transit parameters biasing studies of transit timing variations and transmission spectra. We present PyTranSpot, which we designed to model multiband transit light curves showing starspot anomalies, inferring both transit and spot parameters. The code follows a pixellation approach to model the star with its corresponding limb darkening, spots, and transiting planet on a two dimensional Cartesian coordinate grid. We combine PyTranSpot with a Markov chain Monte Carlo framework to study and derive exoplanet transmission spectra, which provides statistically robust values for the physical properties and uncertainties of a transiting star-planet system. We validate PyTranSpot's performance by analyzing eleven synthetic light curves of four different star-planet systems and 20 transit light curves of the well-studied WASP-41b system. We also investigate the impact of starspots on transit parameters and derive wavelength dependent transit depth values for WASP-41b covering a range of 6200-9200 Å, indicating a flat transmission spectrum.

Transitions between Multiband Oscillatory Patterns Characterize Memory-Guided Perceptual Decisions in Prefrontal Circuits.

PubMed

Wimmer, Klaus; Ramon, Marc; Pasternak, Tatiana; Compte, Albert

2016-01-13

Neuronal activity in the lateral prefrontal cortex (LPFC) reflects the structure and cognitive demands of memory-guided sensory discrimination tasks. However, we still do not know how neuronal activity articulates in network states involved in perceiving, remembering, and comparing sensory information during such tasks. Oscillations in local field potentials (LFPs) provide fingerprints of such network dynamics. Here, we examined LFPs recorded from LPFC of macaques while they compared the directions or the speeds of two moving random-dot patterns, S1 and S2, separated by a delay. LFP activity in the theta, beta, and gamma bands tracked consecutive components of the task. In response to motion stimuli, LFP theta and gamma power increased, and beta power decreased, but showed only weak motion selectivity. In the delay, LFP beta power modulation anticipated the onset of S2 and encoded the task-relevant S1 feature, suggesting network dynamics associated with memory maintenance. After S2 onset the difference between the current stimulus S2 and the remembered S1 was strongly reflected in broadband LFP activity, with an early sensory-related component proportional to stimulus difference and a later choice-related component reflecting the behavioral decision buildup. Our results demonstrate that individual LFP bands reflect both sensory and cognitive processes engaged independently during different stages of the task. This activation pattern suggests that during elementary cognitive tasks, the prefrontal network transitions dynamically between states and that these transitions are characterized by the conjunction of LFP rhythms rather than by single LFP bands. Neurons in the brain communicate through electrical impulses and coordinate this activity in ensembles that pulsate rhythmically, very much like musical instruments in an orchestra. These rhythms change with "brain state," from sleep to waking, but also signal with different oscillation frequencies rapid changes between sensory and cognitive processing. Here, we studied rhythmic electrical activity in the monkey prefrontal cortex, an area implicated in working memory, decision making, and executive control. Monkeys had to identify and remember a visual motion pattern and compare it to a second pattern. We found orderly transitions between rhythmic activity where the same frequency channels were active in all ongoing prefrontal computations. This supports prefrontal circuit dynamics that transitions rapidly between complex rhythmic patterns during structured cognitive tasks. Copyright © 2016 the authors 0270-6474/16/360489-17$15.00/0.

Guidance on the prevention and mitigation of environmental, health and safety impacts of electromagnetic fields and radiation for electric transit systems

DOT National Transportation Integrated Search

2008-01-02

The Federal Transit Administration (FTA) Office of Planning and Environment (TPE) tasked the Volpe Center to develop guidance for transit planners, in order to address persisting public health and safety concerns with prolonged EMF environmental expo...

Applying Hope Theory to Support Middle School Transitions

ERIC Educational Resources Information Center

Akos, Patrick; Kurz, Maureen Shields

2016-01-01

Middle grades transitions pose challenges to many students who meet these tasks with varying levels of success. Contemporary developmental and strengths-based literature offers Hope Theory (Snyder, 2002), a research supported approach that can mitigate risks in school transitions. This article describes how middle grades educators can apply the…

East Tennessee Hydrogen Initiative: Task II - Transition of Bus Transit to Hydrogen: A Case Study of a Medium Sized Transit Agency

DOT National Transportation Integrated Search

2010-12-01

The current climate crisis and recent world events, including a global economic crisis and growing concerns over the availability and cost of petroleum fuels, has sparked a global interest in developing alternative, sustainable, clean fuel technologi...

78 FR 16675 - First Technology Transitions; Policy Task Force Workshop

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-18

... FEDERAL COMMUNICATIONS COMMISSION [GN Docket No. 13-5; DA 13-383] First Technology Transitions... workshops to analyze technology transitions from narrowband to broadband; from time-division multiplexing... capabilities of wireless and wireline (copper, fiber and coax) technologies today and in the future. 11:30 a.m...

CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks.

PubMed

Gillani, Zeeshan; Akash, Muhammad Sajid Hamid; Rahaman, M D Matiur; Chen, Ming

2014-11-30

Predication of gene regularity network (GRN) from expression data is a challenging task. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. Most promising methods are based on support vector machine (SVM). There is a need for comprehensive analysis on prediction accuracy of supervised method SVM using different kernels on different biological experimental conditions and network size. We developed a tool (CompareSVM) based on SVM to compare different kernel methods for inference of GRN. Using CompareSVM, we investigated and evaluated different SVM kernel methods on simulated datasets of microarray of different sizes in detail. The results obtained from CompareSVM showed that accuracy of inference method depends upon the nature of experimental condition and size of the network. For network with nodes (<200) and average (over all sizes of networks), SVM Gaussian kernel outperform on knockout, knockdown, and multifactorial datasets compared to all the other inference methods. For network with large number of nodes (~500), choice of inference method depend upon nature of experimental condition. CompareSVM is available at http://bis.zju.edu.cn/CompareSVM/ .

Finding Waldo: Learning about Users from their Interactions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, Eli T.; Ottley, Alvitta; Zhao, Helen

Visual analytics is inherently a collaboration between human and computer. However, in current visual analytics systems, the computer has limited means of knowing about its users and their analysis processes. While existing research has shown that a user’s interactions with a system reflect a large amount of the user’s reasoning process, there has been limited advancement in developing automated, real-time techniques that mine interactions to learn about the user. In this paper, we demonstrate that we can accurately predict a user’s task performance and infer some user personality traits by using machine learning techniques to analyze interaction data. Specifically, wemore » conduct an experiment in which participants perform a visual search task and we apply well-known machine learning algorithms to three encodings of the users interaction data. We achieve, depending on algorithm and encoding, between 62% and 96% accuracy at predicting whether each user will be fast or slow at completing the task. Beyond predicting performance, we demonstrate that using the same techniques, we can infer aspects of the user’s personality factors, including locus of control, extraversion, and neuroticism. Further analyses show that strong results can be attained with limited observation time, in some cases, 82% of the final accuracy is gained after a quarter of the average task completion time. Overall, our findings show that interactions can provide information to the computer about its human collaborator, and establish a foundation for realizing mixed- initiative visual analytics systems.« less

Performance Technology Program (PTP-S 2). Volume 9: Evaluation of reentry vehicle nosetip transition and heat transfer in the AEDC hyperballistics track G

NASA Astrophysics Data System (ADS)

Wassel, A. T.; Shih, W. C. L.; Curtis, R. J.

1981-01-01

Boundary layer transition and surface heating distributions on graphite fine weave carbon-carbon, and metallic nosetip materials were derived from surface temperature responses measured in nitrogen environments during both free-flight and track-guided testing in the AEDC Hyperballistics Range/Track G. Innovative test procedures were developed, and heat transfer results were validated against established theory through experiments using a super-smooth tungsten model. Quantitative definitions of mean transition front locations were established by deriving heat flux distributions from measured temperatures, and comparisons made with existing nosetip transition correlations. Qualitative transition locations were inferred directly from temperature distributions to investigate preferred orientations on fine weave nosetips. Levels of roughness augmented heat transfer were generally shown to be below values predicted by state of the art methods.

When knowledge activated from memory intrudes on probabilistic inferences from description - the case of stereotypes.

PubMed

Dorrough, Angela R; Glöckner, Andreas; Betsch, Tilmann; Wille, Anika

2017-10-01

To make decisions in probabilistic inference tasks, individuals integrate relevant information partly in an automatic manner. Thereby, potentially irrelevant stimuli that are additionally presented can intrude on the decision process (e.g., Söllner, Bröder, Glöckner, & Betsch, 2014). We investigate whether such an intrusion effect can also be caused by potentially irrelevant or even misleading knowledge activated from memory. In four studies that combine a standard information board paradigm from decision research with a standard manipulation from social psychology, we investigate the case of stereotypes and demonstrate that stereotype knowledge can yield intrusion biases in probabilistic inferences from description. The magnitude of these biases increases with stereotype accessibility and decreases with a clarification of the rational solution. Copyright © 2017 Elsevier B.V. All rights reserved.

INfORM: Inference of NetwOrk Response Modules.

PubMed

Marwah, Veer Singh; Kinaret, Pia Anneli Sofia; Serra, Angela; Scala, Giovanni; Lauerma, Antti; Fortino, Vittorio; Greco, Dario

2018-06-15

Detecting and interpreting responsive modules from gene expression data by using network-based approaches is a common but laborious task. It often requires the application of several computational methods implemented in different software packages, forcing biologists to compile complex analytical pipelines. Here we introduce INfORM (Inference of NetwOrk Response Modules), an R shiny application that enables non-expert users to detect, evaluate and select gene modules with high statistical and biological significance. INfORM is a comprehensive tool for the identification of biologically meaningful response modules from consensus gene networks inferred by using multiple algorithms. It is accessible through an intuitive graphical user interface allowing for a level of abstraction from the computational steps. INfORM is freely available for academic use at https://github.com/Greco-Lab/INfORM. Supplementary data are available at Bioinformatics online.

Is Social Categorization the Missing Link Between Weak Central Coherence and Mental State Inference Abilities in Autism? Preliminary Evidence from a General Population Sample.

PubMed

Skorich, Daniel P; May, Adrienne R; Talipski, Louisa A; Hall, Marnie H; Dolstra, Anita J; Gash, Tahlia B; Gunningham, Beth H

2016-03-01

We explore the relationship between the 'theory of mind' (ToM) and 'central coherence' difficulties of autism. We introduce covariation between hierarchically-embedded categories and social information--at the local level, the global level, or at both levels simultaneously--within a category confusion task. We then ask participants to infer the mental state of novel category members, and measure participants' autism-spectrum quotient (AQ). Results reveal a positive relationship between AQ and the degree of local/global social categorization, which in turn predicts the pattern of mental state inferences. These results provide preliminary evidence for a causal relationship between central coherence and ToM abilities. Implications with regard to ToM processes, social categorization, intervention, and the development of a unified account of autism are discussed.

Neural Mechanism of Inferring Person's Inner Attitude towards Another Person through Observing the Facial Affect in an Emotional Context.

PubMed

Kim, Ji-Woong; Kim, Jae-Jin; Jeong, Bumseok; Kim, Sung-Eun; Ki, Seon Wan

2010-03-01

The goal of the present study was to identify the brain mechanism involved in the attribution of person's attitude toward another person, using facial affective pictures and pictures displaying an affectively-loaded situation. Twenty four right-handed healthy subjects volunteered for our study. We used functional magnetic resonance imaging (MRI) to examine brain activation during attitude attribution task as compared to gender matching tasks. We identified activation in the left inferior frontal cortex, left superior temporal sulcus, and left inferior parietal lobule during the attitude attribution task, compared to the gender matching task. This study suggests that mirror neuron system and ventrolateral inferior frontal cortex play a critical role in the attribution of a person's inner attitude towards another person in an emotional situation.

Theory-Based Causal Induction

ERIC Educational Resources Information Center

Griffiths, Thomas L.; Tenenbaum, Joshua B.

2009-01-01

Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations…

The Effects of Workload Transitions in a Multitasking Environment

DTIC Science & Technology

2016-09-13

completed the NASA Task Load Index to assess subjective workload, and the shortened Dundee Stress State Questionnaire to measure subjective task-related...and the analysis of both the NASA Task Load Index and of the shortened Dundee State Questionnaire did not reveal any significant differences related...Electroencepholography (EEG), NASA Task Load Index ( NASA TLX), Dundee Stress State Questionnaire (DSSQ), Hysteresis 16. SECURITY CLASSIFICATION OF

Herschel/HIFI⋆ observations of the circumstellar ammonia lines in IRC+10216

PubMed Central

Schmidt, M. R.; He, J. H.; Szczerba, R.; Bujarrabal, V.; Alcolea, J.; Cernicharo, J.; Decin, L.; Justtanont, K.; Teyssier, D.; Menten, K. M.; Neufeld, D. A.; Olofsson, H.; Planesas, P.; Marston, A. P.; Sobolev, A. M.; de Koter, A.; Schöier, F. L.

2016-01-01

Context A discrepancy exists between the abundance of ammonia (NH3) derived previously for the circumstellar envelope (CSE) of IRC+10216 from far-IR submillimeter rotational lines and that inferred from radio inversion or mid-infrared (MIR) absorption transitions. Aims To address the discrepancy described above, new high-resolution far-infrared (FIR) observations of both ortho- and para-NH3 transitions toward IRC+10216 were obtained with Herschel, with the goal of determining the ammonia abundance and constraining the distribution of NH3 in the envelope of IRC+10216. Methods We used the Heterodyne Instrument for the Far Infrared (HIFI) on board Herschel to observe all rotational transitions up to the J = 3 level (three ortho- and six para-NH3 lines). We conducted non-LTE multilevel radiative transfer modelling, including the effects of near-infrared (NIR) radiative pumping through vibrational transitions. The computed emission line profiles are compared with the new HIFI data, the radio inversion transitions, and the MIR absorption lines in the ν2 band taken from the literature. Results We found that NIR pumping is of key importance for understanding the excitation of rotational levels of NH3. The derived NH3 abundances relative to molecular hydrogen were (2.8 ± 0.5) × 10−8 for ortho-NH3 and (3.2−0.6+0.7)×10−8 for para-NH3, consistent with an ortho/para ratio of 1. These values are in a rough agreement with abundances derived from the inversion transitions, as well as with the total abundance of NH3 inferred from the MIR absorption lines. To explain the observed rotational transitions, ammonia must be formed near to the central star at a radius close to the end of the wind acceleration region, but no larger than about 20 stellar radii (1σ confidence level). PMID:28065983

Impaired self-agency inferences in schizophrenia: The role of cognitive capacity and causal reasoning style.

PubMed

Prikken, M; van der Weiden, A; Kahn, R S; Aarts, H; van Haren, N E M

2018-01-01

The sense of self-agency, i.e., experiencing oneself as the cause of one's own actions, is impaired in patients with schizophrenia. Normally, inferences of self-agency are enhanced when actual outcomes match with pre-activated outcome information, where this pre-activation can result from explicitly set goals (i.e., goal-based route) or implicitly primed outcome information (i.e., prime-based route). Previous research suggests that patients show specific impairments in the prime-based route, implicating that they do not rely on matches between implicitly available outcome information and actual action-outcomes when inferring self-agency. The question remains: Why? Here, we examine whether neurocognitive functioning and self-serving bias (SSB) may explain abnormalities in patients' agency inferences. Thirty-six patients and 36 healthy controls performed a commonly used agency inference task to measure goal- and prime-based self-agency inferences. Neurocognitive functioning was assessed with the Brief Assessment of Cognition in Schizophrenia (BACS) and the SSB was assessed with the Internal Personal and Situational Attributions Questionnaire. Results showed a substantial smaller effect of primed outcome information on agency experiences in patients compared with healthy controls. Whereas patients and controls differed on BACS and marginally on SSB scores, these differences were not related to patients' impairments in prime-based agency inferences. Patients showed impairments in prime-based agency inferences, thereby replicating previous studies. This finding could not be explained by cognitive dysfunction or SSB. Results are discussed in the context of the recent surge to understand and examine deficits in agency experiences in schizophrenia. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Transitive inference in adults with autism spectrum disorders

PubMed Central

Solomon, Marjorie; Frank, Michael J.; Smith, Anne C.; Ly, Stanford; Carter, Cameron S.

2012-01-01

Individuals with autism spectrum disorders (ASDs) exhibit intact rote learning with impaired generalization. A transitive inference paradigm, involving training on four sequentially presented stimulus pairs containing overlapping items, with subsequent testing on two novel pairs, was used to investigate this pattern of learning in 27 young adults with ASDs and 31 matched neurotypical individuals (TYPs). On the basis of findings about memory and neuropathology, we hypothesized that individuals with ASDs would use a relational flexibility/conjunctive strategy reliant on an intact hippocampus, versus an associative strength/value transfer strategy requiring intact interactions between the prefrontal cortex and the striatum. Hypotheses were largely confirmed. ASDs demonstrated reduced interference from intervening pairs in early training; only TYPs formed a serial position curve by test; and ASDs exhibited impairments on the novel test pair consisting of end items with intact performance on the inner test pair. However, comparable serial position curves formed for both groups by the end of the first block. PMID:21656344

Boundaries for martensitic transition of 7Li under pressure

DOE PAGES

Schaeffer, Anne Marie; Cai, Weizhao; Olejnik, Ella; ...

2015-08-14

We report that physical properties of lithium under extreme pressures continuously reveal unexpected features. These include a sequence of structural transitions to lower symmetry phases, metal-insulator-metal transition, superconductivity with one of the highest elemental transition temperatures, and a maximum followed by a minimum in its melting line. The instability of the bcc structure of lithium is well established by the presence of a temperature-driven martensitic phase transition. The boundaries of this phase, however, have not been previously explored above 3 GPa. All higher pressure phase boundaries are either extrapolations or inferred based on indirect evidence. Here we explore the pressuremore » dependence of the martensitic transition of lithium up to 7 GPa using a combination of neutron and X-ray scattering. We find a rather unexpected deviation from the extrapolated boundaries of the hR3 phase of lithium. Furthermore, there is evidence that, above ~3 GPa, once in fcc phase, lithium does not undergo a martensitic transition.« less

Radial Anisotropy in the Mantle Transition Zone and Its Implications

NASA Astrophysics Data System (ADS)

Chang, S. J.; Ferreira, A. M.

2016-12-01

Seismic anisotropy is a useful tool to investigate mantle flow, mantle convection, and the presence of melts in mantle, since it provides information on the direction of mantle flow or the orientation of melts by combining it with laboratory results in mineral physics. Although the uppermost and lowermost mantle with strong anisotropy have been well studied, anisotropic properties of the mantle transition zone is still enigmatic. We use a recent global radially anisotropic model, SGLOBE-rani, to examine the patterns of radial anisotropy in the mantle transition zone. Strong faster SV velocity anomalies are found in the upper transition zone beneath subduction zones in the western Pacific, which decrease with depth, thereby nearly isotropic in the lower transition zone. This may imply that the origin for the anisotropy is the lattice-preferred orientation of wadsleyite, the dominant anisotropic mineral in the upper transition zone. The water content in the upper transition zone may be inferred from radial anisotropy because of the report that anisotropic intensity depends on the water content in wadsleyite.

Inferring gene ontologies from pairwise similarity data

PubMed Central

Kramer, Michael; Dutkowski, Janusz; Yu, Michael; Bafna, Vineet; Ideker, Trey

2014-01-01

Motivation: While the manually curated Gene Ontology (GO) is widely used, inferring a GO directly from -omics data is a compelling new problem. Recognizing that ontologies are a directed acyclic graph (DAG) of terms and hierarchical relations, algorithms are needed that: analyze a full matrix of gene–gene pairwise similarities from -omics data;infer true hierarchical structure in these data rather than enforcing hierarchy as a computational artifact; andrespect biological pleiotropy, by which a term in the hierarchy can relate to multiple higher level terms. Methods addressing these requirements are just beginning to emerge—none has been evaluated for GO inference. Methods: We consider two algorithms [Clique Extracted Ontology (CliXO), LocalFitness] that uniquely satisfy these requirements, compared with methods including standard clustering. CliXO is a new approach that finds maximal cliques in a network induced by progressive thresholding of a similarity matrix. We evaluate each method’s ability to reconstruct the GO biological process ontology from a similarity matrix based on (a) semantic similarities for GO itself or (b) three -omics datasets for yeast. Results: For task (a) using semantic similarity, CliXO accurately reconstructs GO (>99% precision, recall) and outperforms other approaches (<20% precision, <20% recall). For task (b) using -omics data, CliXO outperforms other methods using two -omics datasets and achieves ∼30% precision and recall using YeastNet v3, similar to an earlier approach (Network Extracted Ontology) and better than LocalFitness or standard clustering (20–25% precision, recall). Conclusion: This study provides algorithmic foundation for building gene ontologies by capturing hierarchical and pleiotropic structure embedded in biomolecular data. Contact: tideker@ucsd.edu PMID:24932003

Knowledge, expectations, and inductive reasoning within conceptual hierarchies.

PubMed

Coley, John D; Hayes, Brett; Lawson, Christopher; Moloney, Michelle

2004-01-01

Previous research (e.g. Cognition 64 (1997) 73) suggests that the privileged level for inductive inference in a folk biological conceptual hierarchy does not correspond to the "basic" level (i.e. the level at which concepts are both informative and distinct). To further explore inductive inference within conceptual hierarchies, we examine relations between knowledge of concepts at different hierarchical levels, expectations about conceptual coherence, and inductive inference. In Experiments 1 and 2, 5- and 8-year-olds and adults listed features of living kind (Experiments 1 and 2) and artifact (Experiment 2) concepts at different hierarchical levels (e.g. plant, tree, oak, desert oak), and also rated the strength of generalizations to the same concepts. For living kinds, the level that showed a relative advantage on these two tasks differed; the greatest increase in features listed tended to occur at the life-form level (e.g. tree), whereas the greatest increase in inductive strength tended to occur at the folk-generic level (e.g. oak). Knowledge and induction also showed different developmental trajectories. For artifact concepts, the levels at which the greatest gains in knowledge and induction occurred were more varied, and corresponded more closely across tasks. In Experiment 3, adults reported beliefs about within-category similarity for concepts at different levels of animal, plant and artifact hierarchies, and rated inductive strength as before. For living kind concepts, expectations about category coherence predicted patterns of inductions; knowledge did not. For artifact concepts, both knowledge and expectations predicted patterns of induction. Results suggest that beliefs about conceptual coherence play an important role in guiding inductive inference, that this role may be largely independent of specific knowledge of concepts, and that such beliefs are especially important in reasoning about living kinds.

Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining.

PubMed

Hero, Alfred O; Rajaratnam, Bala

2016-01-01

When can reliable inference be drawn in fue "Big Data" context? This paper presents a framework for answering this fundamental question in the context of correlation mining, wifu implications for general large scale inference. In large scale data applications like genomics, connectomics, and eco-informatics fue dataset is often variable-rich but sample-starved: a regime where the number n of acquired samples (statistical replicates) is far fewer than fue number p of observed variables (genes, neurons, voxels, or chemical constituents). Much of recent work has focused on understanding the computational complexity of proposed methods for "Big Data". Sample complexity however has received relatively less attention, especially in the setting when the sample size n is fixed, and the dimension p grows without bound. To address fuis gap, we develop a unified statistical framework that explicitly quantifies the sample complexity of various inferential tasks. Sampling regimes can be divided into several categories: 1) the classical asymptotic regime where fue variable dimension is fixed and fue sample size goes to infinity; 2) the mixed asymptotic regime where both variable dimension and sample size go to infinity at comparable rates; 3) the purely high dimensional asymptotic regime where the variable dimension goes to infinity and the sample size is fixed. Each regime has its niche but only the latter regime applies to exa cale data dimension. We illustrate this high dimensional framework for the problem of correlation mining, where it is the matrix of pairwise and partial correlations among the variables fua t are of interest. Correlation mining arises in numerous applications and subsumes the regression context as a special case. we demonstrate various regimes of correlation mining based on the unifying perspective of high dimensional learning rates and sample complexity for different structured covariance models and different inference tasks.

Fostering Social Cognition through an Imitation- and Synchronization-Based Dance/Movement Intervention in Adults with Autism Spectrum Disorder: A Controlled Proof-of-Concept Study.

PubMed

Koehne, Svenja; Behrends, Andrea; Fairhurst, Merle T; Dziobek, Isabel

2016-01-01

Since social cognition is impaired in individuals with autism spectrum disorder (ASD), this study aimed at establishing the efficacy of a newly developed imitation- and synchronization-based dance/movement intervention (SI-DMI) in fostering emotion inference and empathic feelings (emotional reaction to feelings of others) in adults with high-functioning ASD. Fifty-five adults with ASD (IQ ≥85) who were blinded to the aim of the study were assigned to receive either 10 weeks of a dance/movement intervention focusing on interpersonal movement imitation and synchronization (SI-DMI, n = 27) or a control movement intervention (CMI, n = 24) focusing on individual motor coordination (2 participants from each group declined before baseline testing). The primary outcome measure was the objective Multifaceted Empathy Test targeting emotion inference and empathic feelings. Secondary outcomes were scores on the self-rated Interpersonal Reactivity Index. The well-established automatic imitation task and synchronization finger-tapping task were used to quantify effects on imitation and synchronization functions, complemented by the more naturalistic Assessment of Spontaneous Interaction in Movement. Intention-to-treat analyses revealed that from baseline to 3 months, patients treated with SI-DMI showed a significantly larger improvement in emotion inference (d = 0.58), but not empathic feelings, than those treated with CMI (d = -0.04). On the close generalization level, SI-DMI increased synchronization skills and imitation tendencies, as well as whole-body imitation/synchronization and movement reciprocity/dialogue, compared to CMI. SI-DMI can be successful in promoting emotion inference in adults with ASD and warrants further investigation. © 2015 S. Karger AG, Basel.

Social cognition dysfunctions in patients with epilepsy: Evidence from patients with temporal lobe and idiopathic generalized epilepsies.

PubMed

Realmuto, Sabrina; Zummo, Leila; Cerami, Chiara; Agrò, Luigi; Dodich, Alessandra; Canessa, Nicola; Zizzo, Andrea; Fierro, Brigida; Daniele, Ornella

2015-06-01

Despite an extensive literature on cognitive impairments in focal and generalized epilepsy, only a few number of studies specifically explored social cognition disorders in epilepsy syndromes. The aim of our study was to investigate social cognition abilities in patients with temporal lobe epilepsy (TLE) and in patients with idiopathic generalized epilepsy (IGE). Thirty-nine patients (21 patients with TLE and 18 patients with IGE) and 21 matched healthy controls (HCs) were recruited. All subjects underwent a basic neuropsychological battery plus two experimental tasks evaluating emotion recognition from facial expression (Ekman-60-Faces test, Ek-60F) and mental state attribution (Story-based Empathy Task, SET). In particular, the latter is a newly developed task that assesses the ability to infer others' intentions (i.e., intention attribution - IA) and emotions (i.e., emotion attribution - EA) compared with a control condition of physical causality (i.e., causal inferences - CI). Compared with HCs, patients with TLE showed significantly lower performances on both social cognition tasks. In particular, all SET subconditions as well as the recognition of negative emotions were significantly impaired in patients with TLE vs. HCs. On the contrary, patients with IGE showed impairments on anger recognition only without any deficit at the SET task. Emotion recognition deficits occur in patients with epilepsy, possibly because of a global disruption of a pathway involving frontal, temporal, and limbic regions. Impairments of mental state attribution specifically characterize the neuropsychological profile of patients with TLE in the context of the in-depth temporal dysfunction typical of such patients. Impairments of socioemotional processing have to be considered as part of the neuropsychological assessment in both TLE and IGE in view of a correct management and for future therapeutic interventions. Copyright © 2015 Elsevier Inc. All rights reserved.

75 FR 59698 - Federal Advisory Committee; Department of Defense Task Force on the Care, Management, and...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-28

.... SUPPLEMENTARY INFORMATION: The Task Force is a non-discretionary Federal advisory committee established to (a... for wounded, ill, and injured members of the Armed Forces; ii. Medical case management; iii. Non... Task Force on the Care, Management, and Transition of Recovering Wounded, Ill, and Injured Member of...

Project FARE Task IV Report: Urban Mass Transportation Industry Financial and Operating Data Reporting System. Volume I. Task and Project Summary.

DOT National Transportation Integrated Search

1973-11-01

The report contains a description of the uniform reporting system for the urban mass transit industry designed and tested in Project FARE. It is presented in five volumes. Volume I contains a description of how Task IV was accomplished and the conclu...

Inference of population splits and mixtures from genome-wide allele frequency data.

PubMed

Pickrell, Joseph K; Pritchard, Jonathan K

2012-01-01

Many aspects of the historical relationships between populations in a species are reflected in genetic data. Inferring these relationships from genetic data, however, remains a challenging task. In this paper, we present a statistical model for inferring the patterns of population splits and mixtures in multiple populations. In our model, the sampled populations in a species are related to their common ancestor through a graph of ancestral populations. Using genome-wide allele frequency data and a Gaussian approximation to genetic drift, we infer the structure of this graph. We applied this method to a set of 55 human populations and a set of 82 dog breeds and wild canids. In both species, we show that a simple bifurcating tree does not fully describe the data; in contrast, we infer many migration events. While some of the migration events that we find have been detected previously, many have not. For example, in the human data, we infer that Cambodians trace approximately 16% of their ancestry to a population ancestral to other extant East Asian populations. In the dog data, we infer that both the boxer and basenji trace a considerable fraction of their ancestry (9% and 25%, respectively) to wolves subsequent to domestication and that East Asian toy breeds (the Shih Tzu and the Pekingese) result from admixture between modern toy breeds and "ancient" Asian breeds. Software implementing the model described here, called TreeMix, is available at http://treemix.googlecode.com.

Feature inference with uncertain categorization: Re-assessing Anderson's rational model.

PubMed

Konovalova, Elizaveta; Le Mens, Gaël

2017-09-18

A key function of categories is to help predictions about unobserved features of objects. At the same time, humans are often in situations where the categories of the objects they perceive are uncertain. In an influential paper, Anderson (Psychological Review, 98(3), 409-429, 1991) proposed a rational model for feature inferences with uncertain categorization. A crucial feature of this model is the conditional independence assumption-it assumes that the within category feature correlation is zero. In prior research, this model has been found to provide a poor fit to participants' inferences. This evidence is restricted to task environments inconsistent with the conditional independence assumption. Currently available evidence thus provides little information about how this model would fit participants' inferences in a setting with conditional independence. In four experiments based on a novel paradigm and one experiment based on an existing paradigm, we assess the performance of Anderson's model under conditional independence. We find that this model predicts participants' inferences better than competing models. One model assumes that inferences are based on just the most likely category. The second model is insensitive to categories but sensitive to overall feature correlation. The performance of Anderson's model is evidence that inferences were influenced not only by the more likely category but also by the other candidate category. Our findings suggest that a version of Anderson's model which relaxes the conditional independence assumption will likely perform well in environments characterized by within-category feature correlation.

The Feedback-related Negativity Codes Components of Abstract Inference during Reward-based Decision-making.

PubMed

Reiter, Andrea M F; Koch, Stefan P; Schröger, Erich; Hinrichs, Hermann; Heinze, Hans-Jochen; Deserno, Lorenz; Schlagenhauf, Florian

2016-08-01

Behavioral control is influenced not only by learning from the choices made and the rewards obtained but also by "what might have happened," that is, inference about unchosen options and their fictive outcomes. Substantial progress has been made in understanding the neural signatures of direct learning from choices that are actually made and their associated rewards via reward prediction errors (RPEs). However, electrophysiological correlates of abstract inference in decision-making are less clear. One seminal theory suggests that the so-called feedback-related negativity (FRN), an ERP peaking 200-300 msec after a feedback stimulus at frontocentral sites of the scalp, codes RPEs. Hitherto, the FRN has been predominantly related to a so-called "model-free" RPE: The difference between the observed outcome and what had been expected. Here, by means of computational modeling of choice behavior, we show that individuals employ abstract, "double-update" inference on the task structure by concurrently tracking values of chosen stimuli (associated with observed outcomes) and unchosen stimuli (linked to fictive outcomes). In a parametric analysis, model-free RPEs as well as their modification because of abstract inference were regressed against single-trial FRN amplitudes. We demonstrate that components related to abstract inference uniquely explain variance in the FRN beyond model-free RPEs. These findings advance our understanding of the FRN and its role in behavioral adaptation. This might further the investigation of disturbed abstract inference, as proposed, for example, for psychiatric disorders, and its underlying neural correlates.

Transitioning to Adulthood: How Do Young Adults Fare and What Characteristics Are Associated with a Lower-Risk Transition? Research Brief. Publication #2014-18

ERIC Educational Resources Information Center

Terzian, Mary A.; Moore, Kristin A.; Constance, Nicole

2014-01-01

Youth must navigate various developmental tasks as they transition to adulthood, and during this period of "emerging adulthood," young people explore roles and relationships before committing to the ones they will fill as adults. This brief seeks to identify patterns and transitions during emerging adulthood to obtain a better…

An Introduction to Confidence Intervals for Both Statistical Estimates and Effect Sizes.

ERIC Educational Resources Information Center

Capraro, Mary Margaret

This paper summarizes methods of estimating confidence intervals, including classical intervals and intervals for effect sizes. The recent American Psychological Association (APA) Task Force on Statistical Inference report suggested that confidence intervals should always be reported, and the fifth edition of the APA "Publication Manual"…

Affect-Based Adaptation of an Applied Video Game for Educational Purposes

ERIC Educational Resources Information Center

Bontchev, Boyan; Vassileva, Dessislava

2017-01-01

Purpose: This paper aims to clarify how affect-based adaptation can improve implicit recognition of playing style of individuals during game sessions. This study presents the "Rush for Gold" game using dynamic difficulty adjustment of tasks based on both player performance and affectation inferred through electrodermal activity and…

People's Intuitions about Randomness and Probability: An Empirical Study

ERIC Educational Resources Information Center

Lecoutre, Marie-Paule; Rovira, Katia; Lecoutre, Bruno; Poitevineau, Jacques

2006-01-01

What people mean by randomness should be taken into account when teaching statistical inference. This experiment explored subjective beliefs about randomness and probability through two successive tasks. Subjects were asked to categorize 16 familiar items: 8 real items from everyday life experiences, and 8 stochastic items involving a repeatable…

Sensory and Cognitive Determinants of Reading Speed

ERIC Educational Resources Information Center

Jackson, Mark D.; McClelland, James L.

1975-01-01

Fast and average readers were tested on four tasks. Fast readers appear to pick up more information per fixation on structured textual material, and had a greater span of apprehension for unrelated elements. Results disagree with the view that reading speed depends solely on ability to infer missing information. (CHK)

Thin-Slice Perception Develops Slowly

ERIC Educational Resources Information Center

Balas, Benjamin; Kanwisher, Nancy; Saxe, Rebecca

2012-01-01

Body language and facial gesture provide sufficient visual information to support high-level social inferences from "thin slices" of behavior. Given short movies of nonverbal behavior, adults make reliable judgments in a large number of tasks. Here we find that the high precision of adults' nonverbal social perception depends on the slow…

Person Perception in Young Children across Two Cultures

ERIC Educational Resources Information Center

Chen, Eva E.; Corriveau, Kathleen H.; Harris, Paul L.

2016-01-01

To adult humans, the task of forming an impression of another social being seems effortless and even obligatory. In 2 experiments, we offer the first systematic cross-cultural examination of impression formation in European American and East Asian preschool children. Children across both cultures easily inferred basic personality traits, such as…

Using Panorama Theater To Teach Middle School Social Studies.

ERIC Educational Resources Information Center

Chilcoat, George W.

1995-01-01

Describes how use of panorama theater to teach middle school social studies can aid in teaching the academic skills of defining a problem, locating and collecting data, organizing and designing tasks, drawing inferences, creating and building interpretations, revising and editing, and interpreting data. Presents a classroom example of a panorama…

Children's Use of Categories and Mental States to Predict Social Behavior

ERIC Educational Resources Information Center

Chalik, Lisa; Rivera, Cyrielle; Rhodes, Marjorie

2014-01-01

Integrating generic information about categories with knowledge of specific individuals is a critical component of successful inductive inferences. The present study tested whether children's approach to this task systematically shifts as they develop causal understandings of the mechanisms that shape individual action. In the current study, 3-and…

The Empirical Nature and Statistical Treatment of Missing Data

ERIC Educational Resources Information Center

Tannenbaum, Christyn E.

2009-01-01

Introduction. Missing data is a common problem in research and can produce severely misleading analyses, including biased estimates of statistical parameters, and erroneous conclusions. In its 1999 report, the APA Task Force on Statistical Inference encouraged authors to report complications such as missing data and discouraged the use of…

Inferring Speaker Affect in Spoken Natural Language Communication

ERIC Educational Resources Information Center

Pon-Barry, Heather Roberta

2013-01-01

The field of spoken language processing is concerned with creating computer programs that can understand human speech and produce human-like speech. Regarding the problem of understanding human speech, there is currently growing interest in moving beyond speech recognition (the task of transcribing the words in an audio stream) and towards…

Understanding Emergency Medicine Physicians Multitasking Behaviors Around Interruptions.

PubMed

Fong, Allan; Ratwani, Raj M

2018-06-11

Interruptions can adversely impact human performance, particularly in fast-paced and high-risk environments such as the emergency department (ED). Understanding physician behaviors before, during, and after interruptions is important to the design and promotion of safe and effective workflow solutions. However, traditional human factors based interruption models do not accurately reflect the complexities of real-world environments like the ED and may not capture multiple interruptions and multitasking. We present a more comprehensive framework for understanding interruptions that is composed of three phases, each with multiple levels: Interruption Start Transition, Interruption Engagement, and Interruption End Transition. This three-phase framework is not constrained to discrete task transitions, providing a robust method to categorize multitasking behaviors around interruptions. We apply this framework in categorizing 457 interruption episodes. 457 interruption episodes were captured during 36 hours of observation. The interrupted task was immediately suspended 348 (76.1%) times. Participants engaged in new self-initiated tasks during the interrupting task 164 (35.9%) times and did not directly resume the interrupted task in 284 (62.1%) interruption episodes. Using this framework provides a more detailed description of the types of physician behaviors in complex environments. Understanding the different types of interruption and resumption patterns, which may have a different impact on performance, can support the design of interruption mitigation strategies. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Cognitive Engineering of Advanced Information Technology for Air Force Systems Design and Deployment: Prototype for Air Defense Intelligence and Operations

DTIC Science & Technology

1989-10-01

train members of the eventual decision-making group to have different strategies, called " policies ," for making inferences and/or decisions. For example...group task, the task side of the lens model is structured so that each person must modify his judgment policy by learning from the other person in order...judgment policy ; that is, they would receive in both pictorial and textual form a description of how each person combines information to make a judgment

Cortical Hierarchies Perform Bayesian Causal Inference in Multisensory Perception

PubMed Central

Rohe, Tim; Noppeney, Uta

2015-01-01

To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources. Thus, perception inherently relies on solving the “causal inference problem.” Behaviorally, humans solve this problem optimally as predicted by Bayesian Causal Inference; yet, the underlying neural mechanisms are unexplored. Combining psychophysics, Bayesian modeling, functional magnetic resonance imaging (fMRI), and multivariate decoding in an audiovisual spatial localization task, we demonstrate that Bayesian Causal Inference is performed by a hierarchy of multisensory processes in the human brain. At the bottom of the hierarchy, in auditory and visual areas, location is represented on the basis that the two signals are generated by independent sources (= segregation). At the next stage, in posterior intraparietal sulcus, location is estimated under the assumption that the two signals are from a common source (= forced fusion). Only at the top of the hierarchy, in anterior intraparietal sulcus, the uncertainty about the causal structure of the world is taken into account and sensory signals are combined as predicted by Bayesian Causal Inference. Characterizing the computational operations of signal interactions reveals the hierarchical nature of multisensory perception in human neocortex. It unravels how the brain accomplishes Bayesian Causal Inference, a statistical computation fundamental for perception and cognition. Our results demonstrate how the brain combines information in the face of uncertainty about the underlying causal structure of the world. PMID:25710328

Inferring the background traffic arrival process in the Internet.

PubMed

Hága, Péter; Csabai, István; Vattay, Gábor

2009-12-01

Phase transition has been found in many complex interactivity systems. Complex networks are not exception either but there are quite few real systems where we can directly understand the emergence of this nontrivial behavior from the microscopic view. In this paper, we present the emergence of the phase transition between the congested and uncongested phases of a network link. We demonstrate a method to infer the background traffic arrival process, which is one of the key state parameters of the Internet traffic. The traffic arrival process in the Internet has been investigated in several studies, since the recognition of its self-similar nature. The statistical properties of the traffic arrival process are very important since they are fundamental in modeling the dynamical behavior. Here, we demonstrate how the widely used packet train technique can be used to determine the main properties of the traffic arrival process. We show that the packet train dispersion is sensitive to the congestion on the network path. We introduce the packet train stretch as an order parameter to describe the phase transition between the congested and uncongested phases of the bottleneck link in the path. We find that the distribution of the background traffic arrival process can be determined from the average packet train dispersion at the critical point of the system.

Statistical mechanics of unsupervised feature learning in a restricted Boltzmann machine with binary synapses

NASA Astrophysics Data System (ADS)

Huang, Haiping

2017-05-01

Revealing hidden features in unlabeled data is called unsupervised feature learning, which plays an important role in pretraining a deep neural network. Here we provide a statistical mechanics analysis of the unsupervised learning in a restricted Boltzmann machine with binary synapses. A message passing equation to infer the hidden feature is derived, and furthermore, variants of this equation are analyzed. A statistical analysis by replica theory describes the thermodynamic properties of the model. Our analysis confirms an entropy crisis preceding the non-convergence of the message passing equation, suggesting a discontinuous phase transition as a key characteristic of the restricted Boltzmann machine. Continuous phase transition is also confirmed depending on the embedded feature strength in the data. The mean-field result under the replica symmetric assumption agrees with that obtained by running message passing algorithms on single instances of finite sizes. Interestingly, in an approximate Hopfield model, the entropy crisis is absent, and a continuous phase transition is observed instead. We also develop an iterative equation to infer the hyper-parameter (temperature) hidden in the data, which in physics corresponds to iteratively imposing Nishimori condition. Our study provides insights towards understanding the thermodynamic properties of the restricted Boltzmann machine learning, and moreover important theoretical basis to build simplified deep networks.

Gaze shifts during dual-tasking stair descent.

PubMed

Miyasike-daSilva, Veronica; McIlroy, William E

2016-11-01

To investigate the role of vision in stair locomotion, young adults descended a seven-step staircase during unrestricted walking (CONTROL), and while performing a concurrent visual reaction time (RT) task displayed on a monitor. The monitor was located at either 3.5 m (HIGH) or 0.5 m (LOW) above ground level at the end of the stairway, which either restricted (HIGH) or facilitated (LOW) the view of the stairs in the lower field of view as participants walked downstairs. Downward gaze shifts (recorded with an eye tracker) and gait speed were significantly reduced in HIGH and LOW compared with CONTROL. Gaze and locomotor behaviour were not different between HIGH and LOW. However, inter-individual variability increased in HIGH, in which participants combined different response characteristics including slower walking, handrail use, downward gaze, and/or increasing RTs. The fastest RTs occurred in the midsteps (non-transition steps). While gait and visual task performance were not statistically different prior to the top and bottom transition steps, gaze behaviour and RT were more variable prior to transition steps in HIGH. This study demonstrated that, in the presence of a visual task, people do not look down as often when walking downstairs and require minimum adjustments provided that the view of the stairs is available in the lower field of view. The middle of the stairs seems to require less from executive function, whereas visual attention appears a requirement to detect the last transition via gaze shifts or peripheral vision.

Bayesian Estimation and Inference Using Stochastic Electronics

PubMed Central

Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M.; Hamilton, Tara J.; Tapson, Jonathan; van Schaik, André

2016-01-01

In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream. PMID:27047326

Bayesian Estimation and Inference Using Stochastic Electronics.

PubMed

Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan; van Schaik, André

2016-01-01

In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream.

The Emergence of Visual Awareness: Temporal Dynamics in Relation to Task and Mask Type

PubMed Central

Kiefer, Markus; Kammer, Thomas

2017-01-01

One aspect of consciousness phenomena, the temporal emergence of visual awareness, has been subject of a controversial debate. How can visual awareness, that is the experiential quality of visual stimuli, be characterized best? Is there a sharp discontinuous or dichotomous transition between unaware and fully aware states, or does awareness emerge gradually encompassing intermediate states? Previous studies yielded conflicting results and supported both dichotomous and gradual views. It is well conceivable that these conflicting results are more than noise, but reflect the dynamic nature of the temporal emergence of visual awareness. Using a psychophysical approach, the present research tested whether the emergence of visual awareness is context-dependent with a temporal two-alternative forced choice task. During backward masking of word targets, it was assessed whether the relative temporal sequence of stimulus thresholds is modulated by the task (stimulus presence, letter case, lexical decision, and semantic category) and by mask type. Four masks with different similarity to the target features were created. Psychophysical functions were then fitted to the accuracy data in the different task conditions as a function of the stimulus mask SOA in order to determine the inflection point (conscious threshold of each feature) and slope of the psychophysical function (transition from unaware to aware within each feature). Depending on feature-mask similarity, thresholds in the different tasks were highly dispersed suggesting a graded transition from unawareness to awareness or had less differentiated thresholds indicating that clusters of features probed by the tasks quite simultaneously contribute to the percept. The latter observation, although not compatible with the notion of a sharp all-or-none transition between unaware and aware states, suggests a less gradual or more discontinuous emergence of awareness. Analyses of slopes of the fitted psychophysical functions also indicated that the emergence of awareness of single features is variable and might be influenced by the continuity of the feature dimensions. The present work thus suggests that the emergence of awareness is neither purely gradual nor dichotomous, but highly dynamic depending on the task and mask type. PMID:28316583

Shuttle Return To Flight Experimental Results: Protuberance Effects on Boundary Layer Transition

NASA Technical Reports Server (NTRS)

Liechty, Derek S.; Berry, Scott A.; Horvath, Thomas J.

2006-01-01

The effect of isolated roughness elements on the windward boundary layer of the Shuttle Orbiter has been experimentally examined in the Langley Aerothermodynamic Laboratory in support of an agency-wide effort to prepare the Shuttle Orbiter for return to flight. This experimental effort was initiated to provide a roughness effects database for developing transition criteria to support on-orbit decisions to repair damage to the thermal protection system. Boundary layer transition results were obtained using trips of varying heights and locations along the centerline and attachment lines of 0.0075-scale models. Global heat transfer images using phosphor thermography of the Orbiter windward surface and the corresponding heating distributions were used to infer the state of the boundary layer (laminar, transitional, or turbulent). The database contained within this report will be used to formulate protuberance-induced transition correlations using predicted boundary layer edge parameters.

Earth's interior. Dehydration melting at the top of the lower mantle.

PubMed

Schmandt, Brandon; Jacobsen, Steven D; Becker, Thorsten W; Liu, Zhenxian; Dueker, Kenneth G

2014-06-13

The high water storage capacity of minerals in Earth's mantle transition zone (410- to 660-kilometer depth) implies the possibility of a deep H2O reservoir, which could cause dehydration melting of vertically flowing mantle. We examined the effects of downwelling from the transition zone into the lower mantle with high-pressure laboratory experiments, numerical modeling, and seismic P-to-S conversions recorded by a dense seismic array in North America. In experiments, the transition of hydrous ringwoodite to perovskite and (Mg,Fe)O produces intergranular melt. Detections of abrupt decreases in seismic velocity where downwelling mantle is inferred are consistent with partial melt below 660 kilometers. These results suggest hydration of a large region of the transition zone and that dehydration melting may act to trap H2O in the transition zone. Copyright © 2014, American Association for the Advancement of Science.

A Catalog of Transit Timing Posterior Distributions for all Kepler Planet Candidate Events

NASA Astrophysics Data System (ADS)

Montet, Benjamin Tyler; Becker, Juliette C.; Johnson, John

2015-08-01

Kepler has ushered in a new era of planetary dynamics, enabling the detection of interactions between multiple planets in transiting systems for hundreds of systems. These interactions, observed as transit timing variations (TTVs), have been used to find non-transiting companions to transiting systems and to measure masses, eccentricities, and inclinations of transiting planets. Often, physical parameters are inferred by comparing the observed light curve to the result of a photodynamical model, a time-intensive process that often ignores the effects of correlated noise in the light curve. Catalogs of transit timing observations have previously neglected non-Gaussian uncertainties in the times of transit, uncertainties in the transit shape, and short cadence data. Here, we present a catalog of not only times of transit centers, but also posterior distributions on the time of transit for every planet candidate transit event in the Kepler data, developed through importance sampling of each transit. This catalog allows us to marginalize over uncertainties in the transit shape and incorporate short cadence data, the effects of correlated noise, and non-Gaussian posteriors. Our catalog will enable dynamical studies that reflect accurately the precision of Kepler and its limitations without requiring the computational power to model the light curve completely with every integration.

Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons.

PubMed

Probst, Dimitri; Petrovici, Mihai A; Bytschok, Ilja; Bill, Johannes; Pecevski, Dejan; Schemmel, Johannes; Meier, Karlheinz

2015-01-01

The means by which cortical neural networks are able to efficiently solve inference problems remains an open question in computational neuroscience. Recently, abstract models of Bayesian computation in neural circuits have been proposed, but they lack a mechanistic interpretation at the single-cell level. In this article, we describe a complete theoretical framework for building networks of leaky integrate-and-fire neurons that can sample from arbitrary probability distributions over binary random variables. We test our framework for a model inference task based on a psychophysical phenomenon (the Knill-Kersten optical illusion) and further assess its performance when applied to randomly generated distributions. As the local computations performed by the network strongly depend on the interaction between neurons, we compare several types of couplings mediated by either single synapses or interneuron chains. Due to its robustness to substrate imperfections such as parameter noise and background noise correlations, our model is particularly interesting for implementation on novel, neuro-inspired computing architectures, which can thereby serve as a fast, low-power substrate for solving real-world inference problems.

Supporting students' learning in the domain of computer science

NASA Astrophysics Data System (ADS)

Gasparinatou, Alexandra; Grigoriadou, Maria

2011-03-01

Previous studies have shown that students with low knowledge understand and learn better from more cohesive texts, whereas high-knowledge students have been shown to learn better from texts of lower cohesion. This study examines whether high-knowledge readers in computer science benefit from a text of low cohesion. Undergraduate students (n = 65) read one of four versions of a text concerning Local Network Topologies, orthogonally varying local and global cohesion. Participants' comprehension was examined through free-recall measure, text-based, bridging-inference, elaborative-inference, problem-solving questions and a sorting task. The results indicated that high-knowledge readers benefited from the low-cohesion text. The interaction of text cohesion and knowledge was reliable for the sorting activity, for elaborative-inference and for problem-solving questions. Although high-knowledge readers performed better in text-based and in bridging-inference questions with the low-cohesion text, the interaction of text cohesion and knowledge was not reliable. The results suggest a more complex view of when and for whom textual cohesion affects comprehension and consequently learning in computer science.

Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons

PubMed Central

Probst, Dimitri; Petrovici, Mihai A.; Bytschok, Ilja; Bill, Johannes; Pecevski, Dejan; Schemmel, Johannes; Meier, Karlheinz

2015-01-01

The means by which cortical neural networks are able to efficiently solve inference problems remains an open question in computational neuroscience. Recently, abstract models of Bayesian computation in neural circuits have been proposed, but they lack a mechanistic interpretation at the single-cell level. In this article, we describe a complete theoretical framework for building networks of leaky integrate-and-fire neurons that can sample from arbitrary probability distributions over binary random variables. We test our framework for a model inference task based on a psychophysical phenomenon (the Knill-Kersten optical illusion) and further assess its performance when applied to randomly generated distributions. As the local computations performed by the network strongly depend on the interaction between neurons, we compare several types of couplings mediated by either single synapses or interneuron chains. Due to its robustness to substrate imperfections such as parameter noise and background noise correlations, our model is particularly interesting for implementation on novel, neuro-inspired computing architectures, which can thereby serve as a fast, low-power substrate for solving real-world inference problems. PMID:25729361

Dopamine reward prediction errors reflect hidden state inference across time

PubMed Central

Starkweather, Clara Kwon; Babayan, Benedicte M.; Uchida, Naoshige; Gershman, Samuel J.

2017-01-01

Midbrain dopamine neurons signal reward prediction error (RPE), or actual minus expected reward. The temporal difference (TD) learning model has been a cornerstone in understanding how dopamine RPEs could drive associative learning. Classically, TD learning imparts value to features that serially track elapsed time relative to observable stimuli. In the real world, however, sensory stimuli provide ambiguous information about the hidden state of the environment, leading to the proposal that TD learning might instead compute a value signal based on an inferred distribution of hidden states (a ‘belief state’). In this work, we asked whether dopaminergic signaling supports a TD learning framework that operates over hidden states. We found that dopamine signaling exhibited a striking difference between two tasks that differed only with respect to whether reward was delivered deterministically. Our results favor an associative learning rule that combines cached values with hidden state inference. PMID:28263301

Post-decision biases reveal a self-consistency principle in perceptual inference.

PubMed

Luu, Long; Stocker, Alan A

2018-05-15

Making a categorical judgment can systematically bias our subsequent perception of the world. We show that these biases are well explained by a self-consistent Bayesian observer whose perceptual inference process is causally conditioned on the preceding choice. We quantitatively validated the model and its key assumptions with a targeted set of three psychophysical experiments, focusing on a task sequence where subjects first had to make a categorical orientation judgment before estimating the actual orientation of a visual stimulus. Subjects exhibited a high degree of consistency between categorical judgment and estimate, which is difficult to reconcile with alternative models in the face of late, memory related noise. The observed bias patterns resemble the well-known changes in subjective preferences associated with cognitive dissonance, which suggests that the brain's inference processes may be governed by a universal self-consistency constraint that avoids entertaining 'dissonant' interpretations of the evidence. © 2018, Luu et al.

The NIFTy way of Bayesian signal inference

NASA Astrophysics Data System (ADS)

Selig, Marco

2014-12-01

We introduce NIFTy, "Numerical Information Field Theory", a software package for the development of Bayesian signal inference algorithms that operate independently from any underlying spatial grid and its resolution. A large number of Bayesian and Maximum Entropy methods for 1D signal reconstruction, 2D imaging, as well as 3D tomography, appear formally similar, but one often finds individualized implementations that are neither flexible nor easily transferable. Signal inference in the framework of NIFTy can be done in an abstract way, such that algorithms, prototyped in 1D, can be applied to real world problems in higher-dimensional settings. NIFTy as a versatile library is applicable and already has been applied in 1D, 2D, 3D and spherical settings. A recent application is the D3PO algorithm targeting the non-trivial task of denoising, deconvolving, and decomposing photon observations in high energy astronomy.

Bayesian Cue Integration as a Developmental Outcome of Reward Mediated Learning

PubMed Central

Weisswange, Thomas H.; Rothkopf, Constantin A.; Rodemann, Tobias; Triesch, Jochen

2011-01-01

Average human behavior in cue combination tasks is well predicted by Bayesian inference models. As this capability is acquired over developmental timescales, the question arises, how it is learned. Here we investigated whether reward dependent learning, that is well established at the computational, behavioral, and neuronal levels, could contribute to this development. It is shown that a model free reinforcement learning algorithm can indeed learn to do cue integration, i.e. weight uncertain cues according to their respective reliabilities and even do so if reliabilities are changing. We also consider the case of causal inference where multimodal signals can originate from one or multiple separate objects and should not always be integrated. In this case, the learner is shown to develop a behavior that is closest to Bayesian model averaging. We conclude that reward mediated learning could be a driving force for the development of cue integration and causal inference. PMID:21750717

The influence of cognitive ability and instructional set on causal conditional inference.

PubMed

Evans, Jonathan St B T; Handley, Simon J; Neilens, Helen; Over, David

2010-05-01

We report a large study in which participants are invited to draw inferences from causal conditional sentences with varying degrees of believability. General intelligence was measured, and participants were split into groups of high and low ability. Under strict deductive-reasoning instructions, it was observed that higher ability participants were significantly less influenced by prior belief than were those of lower ability. This effect disappeared, however, when pragmatic reasoning instructions were employed in a separate group. These findings are in accord with dual-process theories of reasoning. We also took detailed measures of beliefs in the conditional sentences used for the reasoning tasks. Statistical modelling showed that it is not belief in the conditional statement per se that is the causal factor, but rather correlates of it. Two different models of belief-based reasoning were found to fit the data according to the kind of instructions and the type of inference under consideration.

Two-year-olds use the generic/non-generic distinction to guide their inferences about novel kinds

PubMed Central

Graham, Susan A.; Nayer, Samantha L.; Gelman, Susan A.

2011-01-01

These studies investigated 24- and 30-month-olds’ sensitivity to generic versus nongeneric language when acquiring knowledge about novel kinds. Toddlers were administered an inductive inference task, during which they heard a generic noun-phrase (e.g., “Blicks drink milk”) or a non-generic noun-phrase (e.g., “This blick drinks milk”) paired with an action (e.g., drinking) modeled on an object. They were then provided with the model and a non-model exemplar and asked to imitate the action. After hearing non-generic phrases, 30-month-olds, but not 24-month-olds, imitated more often with the model than with the non-model exemplar. In contrast, after hearing generic phrases, 30-month-olds imitated equally often with both exemplars. These results suggest that 30-month-olds use the generic/non-generic distinction to guide their inferences about novel kinds. PMID:21410928

Dopamine reward prediction errors reflect hidden-state inference across time.

PubMed

Starkweather, Clara Kwon; Babayan, Benedicte M; Uchida, Naoshige; Gershman, Samuel J

2017-04-01

Midbrain dopamine neurons signal reward prediction error (RPE), or actual minus expected reward. The temporal difference (TD) learning model has been a cornerstone in understanding how dopamine RPEs could drive associative learning. Classically, TD learning imparts value to features that serially track elapsed time relative to observable stimuli. In the real world, however, sensory stimuli provide ambiguous information about the hidden state of the environment, leading to the proposal that TD learning might instead compute a value signal based on an inferred distribution of hidden states (a 'belief state'). Here we asked whether dopaminergic signaling supports a TD learning framework that operates over hidden states. We found that dopamine signaling showed a notable difference between two tasks that differed only with respect to whether reward was delivered in a deterministic manner. Our results favor an associative learning rule that combines cached values with hidden-state inference.

Dopamine, reward learning, and active inference

PubMed Central

FitzGerald, Thomas H. B.; Dolan, Raymond J.; Friston, Karl

2015-01-01

Temporal difference learning models propose phasic dopamine signaling encodes reward prediction errors that drive learning. This is supported by studies where optogenetic stimulation of dopamine neurons can stand in lieu of actual reward. Nevertheless, a large body of data also shows that dopamine is not necessary for learning, and that dopamine depletion primarily affects task performance. We offer a resolution to this paradox based on an hypothesis that dopamine encodes the precision of beliefs about alternative actions, and thus controls the outcome-sensitivity of behavior. We extend an active inference scheme for solving Markov decision processes to include learning, and show that simulated dopamine dynamics strongly resemble those actually observed during instrumental conditioning. Furthermore, simulated dopamine depletion impairs performance but spares learning, while simulated excitation of dopamine neurons drives reward learning, through aberrant inference about outcome states. Our formal approach provides a novel and parsimonious reconciliation of apparently divergent experimental findings. PMID:26581305

Dopamine, reward learning, and active inference.

PubMed

FitzGerald, Thomas H B; Dolan, Raymond J; Friston, Karl

2015-01-01

Temporal difference learning models propose phasic dopamine signaling encodes reward prediction errors that drive learning. This is supported by studies where optogenetic stimulation of dopamine neurons can stand in lieu of actual reward. Nevertheless, a large body of data also shows that dopamine is not necessary for learning, and that dopamine depletion primarily affects task performance. We offer a resolution to this paradox based on an hypothesis that dopamine encodes the precision of beliefs about alternative actions, and thus controls the outcome-sensitivity of behavior. We extend an active inference scheme for solving Markov decision processes to include learning, and show that simulated dopamine dynamics strongly resemble those actually observed during instrumental conditioning. Furthermore, simulated dopamine depletion impairs performance but spares learning, while simulated excitation of dopamine neurons drives reward learning, through aberrant inference about outcome states. Our formal approach provides a novel and parsimonious reconciliation of apparently divergent experimental findings.

Testing Atmospheric Retrieval Modeling Assumptions for Transiting Planet Atmospheres: Preparatory science for the James Webb Space Telescope and beyond.

NASA Astrophysics Data System (ADS)

Line, Michael

The field of transiting exoplanet atmosphere characterization has grown considerably over the past decade given the wealth of photometric and spectroscopic data from the Hubble and Spitzer space telescopes. In order to interpret these data, atmospheric models combined with Bayesian approaches are required. From spectra, these approaches permit us to infer fundamental atmospheric properties and how their compositions can relate back to planet formation. However, such approaches must make a wide range of assumptions regarding the physics/parameterizations included in the model atmospheres. There has yet to be a comprehensive investigation exploring how these model assumptions influence our interpretations of exoplanetary spectra. Understanding the impact of these assumptions is especially important since the James Webb Space Telescope (JWST) is expected to invest a substantial portion of its time observing transiting planet atmospheres. It is therefore prudent to optimize and enhance our tools to maximize the scientific return from the revolutionary data to come. The primary goal of the proposed work is to determine the pieces of information we can robustly learn from transiting planet spectra as obtained by JWST and other future, space-based platforms, by investigating commonly overlooked model assumptions. We propose to explore the following effects and how they impact our ability to infer exoplanet atmospheric properties: 1. Stellar/Planetary Uncertainties: Transit/occultation eclipse depths and subsequent planetary spectra are measured relative to their host stars. How do stellar uncertainties, on radius, effective temperature, metallicity, and gravity, as well as uncertainties in the planetary radius and gravity, propagate into the uncertainties on atmospheric composition and thermal structure? Will these uncertainties significantly bias our atmospheric interpretations? Is it possible to use the relative measurements of the planetary spectra to provide additional constraints on the stellar properties? 2. The "1D" Assumption: Atmospheres are inherently three-dimensional. Many exoplanet atmosphere models, especially within retrieval frameworks, assume 1D physics and chemistry when interpreting spectra. How does this "1D" atmosphere assumption bias our interpretation of exoplanet spectra? Do we have to consider global temperature variations such as day-night contrasts or hot spots? What about spatially inhomogeneous molecular abundances and clouds? How will this change our interpretations of phase resolved spectra? 3. Clouds/Hazes: Understanding how clouds/hazes impact transit spectra is absolutely critical if we are to obtain proper estimates of basic atmospheric quantities. How do the assumptions in cloud physics bias our inferences of molecular abundances in transmission? What kind of data (wavelengths, signal-to-noise, resolution) do we need to infer cloud composition, vertical extent, spatial distribution (patchy or global), and size distributions? The proposed work is relevant and timely to the scope of the NASA Exoplanet Research program. The proposed work aims to further develop the critical theoretical modeling tools required to rigorously interpret transiting exoplanet atmosphere data in order to maximize the science return from JWST and beyond. This work will serve as a benchmark study for defining the data (wavelength ranges, signal-to-noises, and resolutions) required from a modeling perspective to "characterize exoplanets and their atmospheres in order to inform target and operational choices for current NASA missions, and/or targeting, operational, and formulation data for future NASA observatories". Doing so will allow us to better "understand the chemical and physical processes of exoplanets (their atmospheres)" which will ultimately " improve understanding of the origins of exoplanetary systems" through robust planetary elemental abundance determinations.

Directional Migration of Recirculating Lymphocytes through Lymph Nodes via Random Walks

PubMed Central

Thomas, Niclas; Matejovicova, Lenka; Srikusalanukul, Wichat; Shawe-Taylor, John; Chain, Benny

2012-01-01

Naive T lymphocytes exhibit extensive antigen-independent recirculation between blood and lymph nodes, where they may encounter dendritic cells carrying cognate antigen. We examine how long different T cells may spend in an individual lymph node by examining data from long term cannulation of blood and efferent lymphatics of a single lymph node in the sheep. We determine empirically the distribution of transit times of migrating T cells by applying the Least Absolute Shrinkage & Selection Operator () or regularised to fit experimental data describing the proportion of labelled infused cells in blood and efferent lymphatics over time. The optimal inferred solution reveals a distribution with high variance and strong skew. The mode transit time is typically between 10 and 20 hours, but a significant number of cells spend more than 70 hours before exiting. We complement the empirical machine learning based approach by modelling lymphocyte passage through the lymph node . On the basis of previous two photon analysis of lymphocyte movement, we optimised distributions which describe the transit times (first passage times) of discrete one dimensional and continuous (Brownian) three dimensional random walks with drift. The optimal fit is obtained when drift is small, i.e. the ratio of probabilities of migrating forward and backward within the node is close to one. These distributions are qualitatively similar to the inferred empirical distribution, with high variance and strong skew. In contrast, an optimised normal distribution of transit times (symmetrical around mean) fitted the data poorly. The results demonstrate that the rapid recirculation of lymphocytes observed at a macro level is compatible with predominantly randomised movement within lymph nodes, and significant probabilities of long transit times. We discuss how this pattern of migration may contribute to facilitating interactions between low frequency T cells and antigen presenting cells carrying cognate antigen. PMID:23028891

Coronal O VI emission observed with UVCS/SOHO during solar flares: Comparison with soft X-ray observations

NASA Astrophysics Data System (ADS)

Mancuso, S.; Giordano, S.; Raymond, J. C.

2016-06-01

In this work, we derive the O VI 1032 Å luminosity profiles of 58 flares, during their impulsive phase, based on off-limb measurements by the Ultraviolet Coronagraph Spectrometer (UVCS) aboard the SOlar and Heliospheric Observatory (SOHO). The O VI luminosities from the transition region plasma (here defined as the region with temperatures 5.0 ≤ log T (K) ≤ 6.0) were inferred from the analysis of the resonantly scattered radiation of the O VI coronal ions. The temperature of maximum ionization for O VI is log Tmax (K) = 5.47. By comparison with simultaneous soft X-ray measurements, we investigate the likely source (chromospheric evaporation, footpoint emission, or heated prominence ejecta) for the transition region emission observed during the impulsive phase. In our study, we find evidence of the main characteristics predicted by the evaporation scenario. Specifically, most O VI flares precede the X-ray peaks typically by several minutes with a mean of 3.2 ± 0.1 min, and clear correlations are found between the soft X-ray and transition region luminosities following power laws with indices ~ 0.7 ± 0.3. Overall, the results are consistent with transition region emission originating from chromospheric evaporation; the thermal X-ray emission peaks after the emission from the evaporation flow as the loops fill with hot plasma. Finally, we were able to infer flow speeds in the range ~20-100 km s-1 for one-third of the events, 14 of which showed speeds between 60 and 80 km s-1. These values are compatible with those found through direct spectroscopic observations at transition region temperatures by the EUV Imaging Spectrometer (EIS) on board Hinode.

Unmixing the Mixing Cost: Contributions from Dimensional Relevance and Stimulus-Response Suppression

ERIC Educational Resources Information Center

Mari-Beffa, Paloma; Cooper, Stephen; Houghton, George

2012-01-01

When participants repeat the same task in a context in which the task may also switch (a mixed block), performance deteriorates compared to when there is only one task repeating (a pure block). Three experiments were designed to assess how perceptual and motor transitions influenced this mixing cost. Experiment 1 provided three pure block…

76 FR 38118 - Meeting of the Department of Defense Task Force on the Care, Management, and Transition of...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-29

... DEPARTMENT OF DEFENSE Office of the Secretary Meeting of the Department of Defense Task Force on... Forces AGENCY: Department of Defense. ACTION: Meeting notice. SUMMARY: Under the provisions of the... that the following Federal Advisory Committee meeting of the Department of Defense Task Force on the...

Part-task vs. whole-task training on a supervisory control task

NASA Technical Reports Server (NTRS)

Battiste, Vernol

1987-01-01

The efficacy of a part-task training for the psychomotor portion of a supervisory control simulation was compared to that of the whole-task training, using six subjects in each group, who were asked to perform a task as quickly as possible. Part-task training was provided with the cursor-control device prior to transition to the whole-task. The analysis of both the training and experimental trials demonstrated a significant performance advantage for the part-task group: the tasks were performed better and at higher speed. Although the subjects finally achieved the same level of performance in terms of score, the part-task method was preferable for economic reasons, since simple pretraining systems are significantly less expensive than the whole-task training systems.

High pressure and temperature induced structural and elastic properties of lutetium chalcogenides

NASA Astrophysics Data System (ADS)

Shriya, S.; Kinge, R.; Khenata, R.; Varshney, Dinesh

2018-04-01

The high-pressure structural phase transition and pressure as well temperature induced elastic properties of rock salt to CsCl structures in semiconducting LuX (X = S, Se, and Te) chalcogenides compound have been performed using effective interionic interaction potential with emphasis on charge transfer interactions and covalent contribution. Estimated values of phase transition pressure and the volume discontinuity in pressure-volume phase diagram indicate the structural phase transition from ZnS to NaCl structure. From the investigations of elastic constants the pressure (temperature) dependent volume collapse/expansion, melting temperature TM, Hardness (HV), and young modulus (E) the LuX lattice infers mechanical stiffening, and thermal softening.

Metacognitive development of deaf children: lessons from the appearance-reality and false belief tasks.

PubMed

Courtin, Cyril; Melot, Anne-Marie

2005-01-01

'Theory of mind' development is now an important research field in deaf studies. Past research with the classic false belief task has consistently reported a delay in theory of mind development in deaf children born of hearing parents, while performance of second-generation deaf children is more problematic with some contradictory results. The present paper is aimed at testing the metacognitive abilities of deaf children on two tasks: the appearance-reality paradigm designed by Flavell, Flavell and Green (1983) and the classic false belief inference task (Wimmer & Perner, 1983; Hogrefe, Wimmer & Perner, 1986). Twenty-eight second-generation deaf children, 60 deaf children of hearing parents and 36 hearing children, aged 5 to 7, were tested and compared on three appearance-reality and three false belief items. Results show that early exposure to language, be it signed or oral, facilitates performance on the two theory of mind tasks. In addition, native signers equal hearing children in the appearance-reality task while surpassing them on the false belief one. The differences of performance patterns in the two tasks are discussed in terms of linguistic and metarepresentational development.

Automation trust and attention allocation in multitasking workspace.

PubMed

Karpinsky, Nicole D; Chancey, Eric T; Palmer, Dakota B; Yamani, Yusuke

2018-07-01

Previous research suggests that operators with high workload can distrust and then poorly monitor automation, which has been generally inferred from automation dependence behaviors. To test automation monitoring more directly, the current study measured operators' visual attention allocation, workload, and trust toward imperfect automation in a dynamic multitasking environment. Participants concurrently performed a manual tracking task with two levels of difficulty and a system monitoring task assisted by an unreliable signaling system. Eye movement data indicate that operators allocate less visual attention to monitor automation when the tracking task is more difficult. Participants reported reduced levels of trust toward the signaling system when the tracking task demanded more focused visual attention. Analyses revealed that trust mediated the relationship between the load of the tracking task and attention allocation in Experiment 1, an effect that was not replicated in Experiment 2. Results imply a complex process underlying task load, visual attention allocation, and automation trust during multitasking. Automation designers should consider operators' task load in multitasking workspaces to avoid reduced automation monitoring and distrust toward imperfect signaling systems. Copyright © 2018. Published by Elsevier Ltd.

The Impact of the Transition to HE: Emotions, Feelings and Sensations

ERIC Educational Resources Information Center

Dias, Diana; Sá, Maria José

2014-01-01

Transition to Higher Education (HE) is a significant life event and it is supposed to be a very agreeable experience to students. However, such impact is not linear, being mediated by students' psychosocial variables and by their own perceptions concerning the HE environment. Transition to HE encompasses many tasks to cope with changes:…

Children's and Adults' Judgments of the Controllability of Cognitive Activities

ERIC Educational Resources Information Center

Pillow, Bradford H.; Pearson, RaeAnne M.

2015-01-01

Two experiments investigated 1st-, 3rd-, and 5th-grade children's and adults' judgments related to the controllability of cognitive activities, including object recognition, inferential reasoning, counting, and pretending. In Experiment 1, fifth-grade children and adults rated transitive inference and interpretation of ambiguous pictures as more…

Socialization of Emotion: Who Influences Whom and How?

ERIC Educational Resources Information Center

Zahn-Waxler, Carolyn

2010-01-01

Emotion socialization begins within the family setting and extends outward as children transition into expanded social worlds. Children contribute to their socialization from the first years of life, so the dynamics between parents and children are reciprocal in nature. Because socialization influences are best inferred from patterns that unfold…

The Heuristic Value of Cognitive Terminology

ERIC Educational Resources Information Center

Zentall, Thomas R.

2012-01-01

If judiciously applied, cognitive terminology can encourage further examination of phenomena in useful ways that may not otherwise be studied. I give examples of 3 phenomena, the study of which have benefitted from a cognitive perspective. For the first, transitive inference behavior, it appears that non-cognitive accounts cannot satisfactorily…

Out with the Old and in with the New—Is Backward Inhibition a Domain-Specific Process?

PubMed Central

Menghini, Deny; Vicari, Stefano; Petrosini, Laura; Ferlazzo, Fabio

2015-01-01

Effective task switching is supported by the inhibition of the just executed task, so that potential interference from previously executed tasks is adaptively counteracted. This inhibitory mechanism, named Backward Inhibition (BI), has been inferred from the finding that switching back to a recently executed task (A-B-A task sequence) is harder than switching back to a less recently executed task (C-B-A task sequence). Despite the fact that BI effects do impact performance on everyday life activities, up to now it is still not clear whether the BI represents an amodal and material-independent process or whether it interacts with the task material. To address this issue, a group of individuals with Williams syndrome (WS) characterized by specific difficulties in maintaining and processing visuo-spatial, but not verbal, information, and a mental age- and gender-matched group of typically developing (TD) children were subjected to three task-switching experiments requiring verbal or visuo-spatial material to be processed. Results showed that individuals with WS exhibited a normal BI effect during verbal task-switching, but a clear deficit during visuo-spatial task-switching. Overall, our findings demonstrating that the BI is a material-specific process have important implications for theoretical models of cognitive control and its architecture. PMID:26565628

The curious incident of the photo that was accused of being false: issues of domain specificity in development, autism, and brain imaging.

PubMed

Perner, Josef; Leekam, Susan

2008-01-01

We resume an exchange of ideas with Uta Frith that started before the turn of the century. The curious incident responsible for this exchange was the finding that children with autism fail tests of false belief, while they pass Zaitchik's (1990) photograph task (Leekam & Perner, 1991). This finding led to the conclusion that children with autism have a domain-specific impairment in Theory of Mind (mental representations), because the photograph task and the false-belief task are structurally equivalent except for the nonmental character of photographs. In this paper we argue that the false-belief task and the false-photograph task are not structurally equivalent and are not empirically associated. Instead a truly structurally equivalent task is the false-sign task. Performance on this task is strongly associated with the false-belief task. A version of this task, the misleading-signal task, also poses severe problems for children with autism (Bowler, Briskman, Gurvidi, & Fornells-Ambrojo, 2005). These new findings therefore challenge the earlier interpretation of a domain-specific difficulty in inferring mental states and suggest that children with autism also have difficulty understanding misleading nonmental objects. Brain imaging data using false-belief, "false"-photo, and false-sign scenarios provide further supporting evidence for our conclusions.

Minimum Action Path Theory Reveals the Details of Stochastic Transitions Out of Oscillatory States

NASA Astrophysics Data System (ADS)

de la Cruz, Roberto; Perez-Carrasco, Ruben; Guerrero, Pilar; Alarcon, Tomas; Page, Karen M.

2018-03-01

Cell state determination is the outcome of intrinsically stochastic biochemical reactions. Transitions between such states are studied as noise-driven escape problems in the chemical species space. Escape can occur via multiple possible multidimensional paths, with probabilities depending nonlocally on the noise. Here we characterize the escape from an oscillatory biochemical state by minimizing the Freidlin-Wentzell action, deriving from it the stochastic spiral exit path from the limit cycle. We also use the minimized action to infer the escape time probability density function.

Minimum Action Path Theory Reveals the Details of Stochastic Transitions Out of Oscillatory States.

PubMed

de la Cruz, Roberto; Perez-Carrasco, Ruben; Guerrero, Pilar; Alarcon, Tomas; Page, Karen M

2018-03-23

Cell state determination is the outcome of intrinsically stochastic biochemical reactions. Transitions between such states are studied as noise-driven escape problems in the chemical species space. Escape can occur via multiple possible multidimensional paths, with probabilities depending nonlocally on the noise. Here we characterize the escape from an oscillatory biochemical state by minimizing the Freidlin-Wentzell action, deriving from it the stochastic spiral exit path from the limit cycle. We also use the minimized action to infer the escape time probability density function.

Adolescent transition: Ordinary People (1980), Fly Away Home (1996), and (500) Days Of Summer (2009).

PubMed

Miller, Frederick C

2011-06-01

Five important transitional tasks of adolescent development are (i) taming the upsurge of desires and impulses, both sexual and aggressive, into constructive and creative directions; (ii) establishing independence from infantile family ties (while maintaining some involvement with the family of origin); (iii) reconciling self-preoccupations with social attachments; (iv) reworking identifications, especially sexual; and (v) establishing romantic attachments and solidifying ongoing stable love relationships. These tasks are illustrated with the help of three movies, namely Ordinary People, Fly Away Home, and (500) Days of Summer.

Ways to Write a Milestone: Approaches to Operationalizing the Development of Competence in Graduate Medical Education.

PubMed

Leep Hunderfund, Andrea N; Reed, Darcy A; Starr, Stephanie R; Havyer, Rachel D; Lang, Tara R; Norby, Suzanne M

2017-09-01

To identify approaches to operationalizing the development of competence in Accreditation Council for Graduate Medical Education (ACGME) milestones. The authors reviewed all 25 "Milestone Project" documents available on the ACGME Web site on September 11, 2013, using an iterative process to identify approaches to operationalizing the development of competence in the milestones associated with each of 601 subcompetencies. Fifteen approaches were identified. Ten focused on attributes and activities of the learner, such as their ability to perform different, increasingly difficult tasks (304/601; 51%), perform a task better and faster (171/601; 45%), or perform a task more consistently (123/601; 20%). Two approaches focused on context, inferring competence from performing a task in increasingly difficult situations (236/601; 29%) or an expanding scope of engagement (169/601; 28%). Two used socially defined indicators of competence such as progression from "learning" to "teaching," "leading," or "role modeling" (271/601; 45%). One approach focused on the supervisor's role, inferring competence from a decreasing need for supervision or assistance (151/601; 25%). Multiple approaches were often combined within a single set of milestones (mean 3.9, SD 1.6). Initial ACGME milestones operationalize the development of competence in many ways. These findings offer insights into how physicians understand and assess the developmental progression of competence and an opportunity to consider how different approaches may affect the validity of milestone-based assessments. The results of this analysis can inform the work of educators developing or revising milestones, interpreting milestone data, or creating assessment tools to inform milestone-based performance measures.

Identification of the Fire Threat in Urban Transit Vehicles

DOT National Transportation Integrated Search

1980-06-01

To improve mass transportation, UMTA tasked the Transportation Systems Center (TSC) to assess the overall fire threat in transit systems and to identify and recommend suitable remedial actions. This report presents the identification of the fire thre...

EU-US standards harmonization task group report : testing for ITS communications.

DOT National Transportation Integrated Search

2003-01-01

The Cape Cod Regional Transit Authority's (CCRTA) Advanced Public Transportation System (APTS) project is an application of Intelligent Transportation Systems (ITS) to fixed-route and paratransit operations in a rural transit setting. The purpose of ...

U.S. Transit Track Assessment and Research Needs

DOT National Transportation Integrated Search

1979-12-01

The overall objective of the study is to help expand and systematize the current search for improvements in transit track. The study was initiated to identify new technology and research tasks that may help increase the performance, reliability, and ...

Development of Measures of Service Availability : Volume 2. Task Technical Reports.

DOT National Transportation Integrated Search

1978-06-01

The study (a part of UMTA's Automatic Guideway Transit Technology program) is aimed at developing a set of measures for "service availability" which will be meaningful, readily understandable, and acceptable to transit operators, suppliers, and inter...

Cape Cod Transit Task Force : Five-Year Public Transportation Plan

DOT National Transportation Integrated Search

2002-06-30

The U.S. Department of Transportation's Volpe National Transportation Systems : Center has been working in cooperation with the Cape Cod Regional Transit : Authority, the Cape Cod Commission, and other organizations participating on : the Cape Cod Tr...

A multi-criteria inference approach for anti-desertification management.

PubMed

Tervonen, Tommi; Sepehr, Adel; Kadziński, Miłosz

2015-10-01

We propose an approach for classifying land zones into categories indicating their resilience against desertification. Environmental management support is provided by a multi-criteria inference method that derives a set of value functions compatible with the given classification examples, and applies them to define, for the rest of the zones, their possible classes. In addition, a representative value function is inferred to explain the relative importance of the criteria to the stakeholders. We use the approach for classifying 28 administrative regions of the Khorasan Razavi province in Iran into three equilibrium classes: collapsed, transition, and sustainable zones. The model is parameterized with enhanced vegetation index measurements from 2005 to 2012, and 7 other natural and anthropogenic indicators for the status of the region in 2012. Results indicate that grazing density and land use changes are the main anthropogenic factors affecting desertification in Khorasan Razavi. The inference procedure suggests that the classification model is underdetermined in terms of attributes, but the approach itself is promising for supporting the management of anti-desertification efforts. Copyright © 2015 Elsevier Ltd. All rights reserved.

Statistical comparison of a hybrid approach with approximate and exact inference models for Fusion 2+

NASA Astrophysics Data System (ADS)

Lee, K. David; Wiesenfeld, Eric; Gelfand, Andrew

2007-04-01

One of the greatest challenges in modern combat is maintaining a high level of timely Situational Awareness (SA). In many situations, computational complexity and accuracy considerations make the development and deployment of real-time, high-level inference tools very difficult. An innovative hybrid framework that combines Bayesian inference, in the form of Bayesian Networks, and Possibility Theory, in the form of Fuzzy Logic systems, has recently been introduced to provide a rigorous framework for high-level inference. In previous research, the theoretical basis and benefits of the hybrid approach have been developed. However, lacking is a concrete experimental comparison of the hybrid framework with traditional fusion methods, to demonstrate and quantify this benefit. The goal of this research, therefore, is to provide a statistical analysis on the comparison of the accuracy and performance of hybrid network theory, with pure Bayesian and Fuzzy systems and an inexact Bayesian system approximated using Particle Filtering. To accomplish this task, domain specific models will be developed under these different theoretical approaches and then evaluated, via Monte Carlo Simulation, in comparison to situational ground truth to measure accuracy and fidelity. Following this, a rigorous statistical analysis of the performance results will be performed, to quantify the benefit of hybrid inference to other fusion tools.

Inference of the sparse kinetic Ising model using the decimation method

NASA Astrophysics Data System (ADS)

Decelle, Aurélien; Zhang, Pan

2015-05-01

In this paper we study the inference of the kinetic Ising model on sparse graphs by the decimation method. The decimation method, which was first proposed in Decelle and Ricci-Tersenghi [Phys. Rev. Lett. 112, 070603 (2014), 10.1103/PhysRevLett.112.070603] for the static inverse Ising problem, tries to recover the topology of the inferred system by setting the weakest couplings to zero iteratively. During the decimation process the likelihood function is maximized over the remaining couplings. Unlike the ℓ1-optimization-based methods, the decimation method does not use the Laplace distribution as a heuristic choice of prior to select a sparse solution. In our case, the whole process can be done auto-matically without fixing any parameters by hand. We show that in the dynamical inference problem, where the task is to reconstruct the couplings of an Ising model given the data, the decimation process can be applied naturally into a maximum-likelihood optimization algorithm, as opposed to the static case where pseudolikelihood method needs to be adopted. We also use extensive numerical studies to validate the accuracy of our methods in dynamical inference problems. Our results illustrate that, on various topologies and with different distribution of couplings, the decimation method outperforms the widely used ℓ1-optimization-based methods.

Visual shape perception as Bayesian inference of 3D object-centered shape representations.

PubMed

Erdogan, Goker; Jacobs, Robert A

2017-11-01

Despite decades of research, little is known about how people visually perceive object shape. We hypothesize that a promising approach to shape perception is provided by a "visual perception as Bayesian inference" framework which augments an emphasis on visual representation with an emphasis on the idea that shape perception is a form of statistical inference. Our hypothesis claims that shape perception of unfamiliar objects can be characterized as statistical inference of 3D shape in an object-centered coordinate system. We describe a computational model based on our theoretical framework, and provide evidence for the model along two lines. First, we show that, counterintuitively, the model accounts for viewpoint-dependency of object recognition, traditionally regarded as evidence against people's use of 3D object-centered shape representations. Second, we report the results of an experiment using a shape similarity task, and present an extensive evaluation of existing models' abilities to account for the experimental data. We find that our shape inference model captures subjects' behaviors better than competing models. Taken as a whole, our experimental and computational results illustrate the promise of our approach and suggest that people's shape representations of unfamiliar objects are probabilistic, 3D, and object-centered. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Sequential Monte Carlo for inference of latent ARMA time-series with innovations correlated in time

NASA Astrophysics Data System (ADS)

Urteaga, Iñigo; Bugallo, Mónica F.; Djurić, Petar M.

2017-12-01

We consider the problem of sequential inference of latent time-series with innovations correlated in time and observed via nonlinear functions. We accommodate time-varying phenomena with diverse properties by means of a flexible mathematical representation of the data. We characterize statistically such time-series by a Bayesian analysis of their densities. The density that describes the transition of the state from time t to the next time instant t+1 is used for implementation of novel sequential Monte Carlo (SMC) methods. We present a set of SMC methods for inference of latent ARMA time-series with innovations correlated in time for different assumptions in knowledge of parameters. The methods operate in a unified and consistent manner for data with diverse memory properties. We show the validity of the proposed approach by comprehensive simulations of the challenging stochastic volatility model.

Use of a wave reverberation technique to infer the density compression of shocked liquid deuterium to 75 GPa.

PubMed

Knudson, M D; Hanson, D L; Bailey, J E; Hall, C A; Asay, J R

2003-01-24

A novel approach was developed to probe density compression of liquid deuterium (L-D2) along the principal Hugoniot. Relative transit times of shock waves reverberating within the sample are shown to be sensitive to the compression due to the first shock. This technique has proven to be more sensitive than the conventional method of inferring density from the shock and mass velocity, at least in this high-pressure regime. Results in the range of 22-75 GPa indicate an approximately fourfold density compression, and provide data to differentiate between proposed theories for hydrogen and its isotopes.

Stationary properties of maximum-entropy random walks.

PubMed

Dixit, Purushottam D

2015-10-01

Maximum-entropy (ME) inference of state probabilities using state-dependent constraints is popular in the study of complex systems. In stochastic systems, how state space topology and path-dependent constraints affect ME-inferred state probabilities remains unknown. To that end, we derive the transition probabilities and the stationary distribution of a maximum path entropy Markov process subject to state- and path-dependent constraints. A main finding is that the stationary distribution over states differs significantly from the Boltzmann distribution and reflects a competition between path multiplicity and imposed constraints. We illustrate our results with particle diffusion on a two-dimensional landscape. Connections with the path integral approach to diffusion are discussed.

An Introductory Summary of Various Effect Size Choices.

ERIC Educational Resources Information Center

Cromwell, Susan

This paper provides a tutorial summary of some of the many effect size choices so that members of the Southwest Educational Research Association would be better able to follow the recommendations of the American Psychological Association (APA) publication manual, the APA Task Force on Statistical Inference, and the publication requirements of some…

True morels (Morchella, Pezizales) of Europe and North America: evolutionary relationships inferred from multilocus data and a unified taxonomy

USDA-ARS?s Scientific Manuscript database

Applying early names, 29 with or without original material, to genealogical species is challenging. For morels this task is especially difficult because of high morphological stasis and high plasticity of apothecium color and shape. Here, we propose a nomenclatural revision of true morels (Morchella...

Morphological Awareness and Bilingual Word Learning: A Longitudinal Structural Equation Modeling Study

ERIC Educational Resources Information Center

Zhang, Dongbo; Koda, Keiko; Leong, Che Kan

2016-01-01

This longitudinal study examined the contribution of morphological awareness to bilingual word learning of Malay-English bilingual children in Singapore where English is the medium of instruction. Participants took morphological awareness and lexical inference tasks in both English and Malay twice with an interval of about half a year, the first…

Reading Minds: How Infants Come to Understand Others

ERIC Educational Resources Information Center

Gopnik, Alison; Seiver, Elizabeth

2009-01-01

Navigating the social world is an extraordinarily difficult and complex task. How do we think about other people's minds, and how do we come to infer other people's intentions from their actions? Developmental psychologists have shown that even very young infants are attuned to the emotions of those around them, imitate facial expressions and…

A Hybrid Approach to Inferring a Consistent Temporal Relation Set in Natural Language Text

ERIC Educational Resources Information Center

Lee, Chong Min

2013-01-01

This dissertation investigates the temporal relation identification task. The goal is to construct consistent temporal relations between temporal entities (e.g., events and time expressions) in a narrative. Constructing consistent temporal relations is challenging due to the exponential increase in the number of candidates for temporal relations…

Affective Behavior and Nonverbal Interaction in Collaborative Virtual Environments

ERIC Educational Resources Information Center

Peña, Adriana; Rangel, Nora; Muñoz, Mirna; Mejia, Jezreel; Lara, Graciela

2016-01-01

While a person's internal state might not be easily inferred through an automatic computer system, within a group, people express themselves through their interaction with others. The group members' interaction can be then helpful to understand, to certain extent, its members' affective behavior in any case toward the task at hand. In this…

Tracking and Inferring Spatial Rotation by Children and Great Apes

ERIC Educational Resources Information Center

Okamoto-Barth; Sanae; Call, Josep

2008-01-01

Finding hidden objects in space is a fundamental ability that has received considerable research attention from both a developmental and a comparative perspective. Tracking the rotational displacements of containers and hidden objects is a particularly challenging task. This study investigated the ability of 3-, 5-, 7-, and 9-year-old children and…

Estimating Standardized Linear Contrasts of Means with Desired Precision

ERIC Educational Resources Information Center

Bonett, Douglas G.

2009-01-01

L. Wilkinson and the Task Force on Statistical Inference (1999) recommended reporting confidence intervals for measures of effect sizes. If the sample size is too small, the confidence interval may be too wide to provide meaningful information. Recently, K. Kelley and J. R. Rausch (2006) used an iterative approach to computer-generate tables of…

Examining Perception of Competency through Practicum Competencies Outline

ERIC Educational Resources Information Center

Esposito, Giovanna; Freda, Maria Francesca; Bosco, Valentina

2015-01-01

Purpose: This study aims to examine the self-perceived competencies of 231 Italian students enrolled in a psychological degree program and involved in a practicum. It analyzes the subjective perception of the competences that students expect to develop, acknowledge as developed and that might be inferred from tasks performed during the practicum;…

Speaker Reliability in Preschoolers' Inferences about the Meanings of Novel Words

ERIC Educational Resources Information Center

Sobel, David M.; Sedivy, Julie; Buchanan, David W.; Hennessy, Rachel

2012-01-01

Preschoolers participated in a modified version of the disambiguation task, designed to test whether the pragmatic environment generated by a reliable or unreliable speaker affected how children interpreted novel labels. Two objects were visible to children, while a third was only visible to the speaker (a fact known by the child). Manipulating…

Teacher Talk: Cognitive Goals Inferred from Instruction.

ERIC Educational Resources Information Center

Adams, Verna M.

To suggest that activity in the classroom shift from a focus on memorizing procedures to using mathematical reasoning is to suggest a shift in the classroom environment accompanied by shifts in teacher talk. The task of this report was to introduce the idea of teachers' orienting behaviors aimed at facilitating student cognition, and to suggest…

The Costs of Supervised Classification: The Effect of Learning Task on Conceptual Flexibility

ERIC Educational Resources Information Center

Hoffman, Aaron B.; Rehder, Bob

2010-01-01

Research has shown that learning a concept via standard supervised classification leads to a focus on diagnostic features, whereas learning by inferring missing features promotes the acquisition of within-category information. Accordingly, we predicted that classification learning would produce a deficit in people's ability to draw "novel…

Unlocking the Laboratory: Autonomous Wireless Sensor Authentication in Practice

ERIC Educational Resources Information Center

Huggard, Meriel; McGoldrick, Ciaran

2013-01-01

Purpose: The purpose of this study is to evaluate a practical laboratory task where final year undergraduate students design, implement and validate an inferred security wireless sensor access system. Design/methodology/approach: The quality of the learning and technical environment was evaluated from a number of perspectives using a mixed methods…

Multiple player tracking in sports video: a dual-mode two-way bayesian inference approach with progressive observation modeling.

PubMed

Xing, Junliang; Ai, Haizhou; Liu, Liwei; Lao, Shihong

2011-06-01

Multiple object tracking (MOT) is a very challenging task yet of fundamental importance for many practical applications. In this paper, we focus on the problem of tracking multiple players in sports video which is even more difficult due to the abrupt movements of players and their complex interactions. To handle the difficulties in this problem, we present a new MOT algorithm which contributes both in the observation modeling level and in the tracking strategy level. For the observation modeling, we develop a progressive observation modeling process that is able to provide strong tracking observations and greatly facilitate the tracking task. For the tracking strategy, we propose a dual-mode two-way Bayesian inference approach which dynamically switches between an offline general model and an online dedicated model to deal with single isolated object tracking and multiple occluded object tracking integrally by forward filtering and backward smoothing. Extensive experiments on different kinds of sports videos, including football, basketball, as well as hockey, demonstrate the effectiveness and efficiency of the proposed method.

77 FR 77046 - Defense Business Board; Notice of Federal Advisory Committee Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-31

... ``Employing Our Veterans Part II: Review of Pilot Transition Goal Plans Success Program'' Task Group Studies.... Availability of Materials for the Meeting: A copy of the agenda and the terms of reference for the Task Group... also be available at the meeting. Meeting Agenda 9:15 a.m.--11:30 a.m. Task Group Outbrief and Board...

Crew Integration & Automation Testbed and Robotic Follower Programs

DTIC Science & Technology

2001-05-30

Evolving Technologies for Reduced Crew Operation” Vehicle Tech Demo #1 (VTT) Vehicle Tech Demo #2 ( CAT ATD) Two Man Transition Future Combat...Simulation Advanced Electronic Architecture Concept Vehicle Shown with Onboard Safety Driver Advanced Interfaces CAT ATD Exit Criteria...Provide 1000 Hz control loop for critical real-time tasks CAT Workload IPT Process and Product Schedule Crew Task List Task Timelines Workload Analysis

Development of Hot and Cool Executive Function during the Transition to Adolescence

ERIC Educational Resources Information Center

Prencipe, Angela; Kesek, Amanda; Cohen, Julia; Lamm, Connie; Lewis, Marc D.; Zelazo, Philip David

2011-01-01

This study examined the development of executive function (EF) in a typically developing sample from middle childhood to adolescence using a range of tasks varying in affective significance. A total of 102 participants between 8 and 15 years of age completed the Iowa Gambling Task, the Color Word Stroop, a Delay Discounting task, and a Digit Span…

A Cross-Sectional Behavioral Genetic Analysis of Task Persistence in the Transition to Middle Childhood

ERIC Educational Resources Information Center

Deater-Deckard, Kirby; Petrill, Stephen A.; Thompson, Lee A.; DeThorne, Laura S.

2005-01-01

Task persistence, measured by a composite score of independent teacher, tester and observer reports, was examined using behavioral genetic analysis. Participants included 92 monozygotic and 137 same-sex dizygotic twin pairs in Kindergarten or 1st grade (4.3 to 7.9 years old). Task persistence was widely distributed, higher among older children,…

The cognitive roles of behavioral variability: idiosyncratic acts as the foundation of identity and as transitional, preparatory, and confirmatory phases.

PubMed

Eilam, David

2015-02-01

Behavior in obsessive compulsive disorder (OCD), in habitual daily tasks, and in sport and cultural rituals is deconstructed into elemental acts and categorized into common acts, performed by all individuals completing a similar task, and idiosyncratic acts, not performed by all individuals. Never skipped, common acts establish the pragmatic part of motor tasks. Repetitive performance of a few common acts renders rituals a rigid form, whereby common acts may serve as memes for cultural transmission. While idiosyncratic acts are not pragmatically necessary for task completion, they fulfill important cognitive roles. They form a long preparatory phase in tasks that involve high stakes, and a long confirmatory phase in OCD rituals. Idiosyncratic acts also form transitional phases between motor tasks, and are involved in establishing identity and preserving the flexibility necessary for adapting to varying circumstances. Behavioral variability, as manifested in idiosyncrasy, thus does not seem to be a noise or by-product of motor activity, but an essential cognitive component that has been preserved in the evolution of behavioral patterns, similar to the genetic variability in biology. Copyright © 2014 Elsevier Ltd. All rights reserved.

Boosting multi-state models.

PubMed

Reulen, Holger; Kneib, Thomas

2016-04-01

One important goal in multi-state modelling is to explore information about conditional transition-type-specific hazard rate functions by estimating influencing effects of explanatory variables. This may be performed using single transition-type-specific models if these covariate effects are assumed to be different across transition-types. To investigate whether this assumption holds or whether one of the effects is equal across several transition-types (cross-transition-type effect), a combined model has to be applied, for instance with the use of a stratified partial likelihood formulation. Here, prior knowledge about the underlying covariate effect mechanisms is often sparse, especially about ineffectivenesses of transition-type-specific or cross-transition-type effects. As a consequence, data-driven variable selection is an important task: a large number of estimable effects has to be taken into account if joint modelling of all transition-types is performed. A related but subsequent task is model choice: is an effect satisfactory estimated assuming linearity, or is the true underlying nature strongly deviating from linearity? This article introduces component-wise Functional Gradient Descent Boosting (short boosting) for multi-state models, an approach performing unsupervised variable selection and model choice simultaneously within a single estimation run. We demonstrate that features and advantages in the application of boosting introduced and illustrated in classical regression scenarios remain present in the transfer to multi-state models. As a consequence, boosting provides an effective means to answer questions about ineffectiveness and non-linearity of single transition-type-specific or cross-transition-type effects.

Hybrid computing using a neural network with dynamic external memory.

PubMed

Graves, Alex; Wayne, Greg; Reynolds, Malcolm; Harley, Tim; Danihelka, Ivo; Grabska-Barwińska, Agnieszka; Colmenarejo, Sergio Gómez; Grefenstette, Edward; Ramalho, Tiago; Agapiou, John; Badia, Adrià Puigdomènech; Hermann, Karl Moritz; Zwols, Yori; Ostrovski, Georg; Cain, Adam; King, Helen; Summerfield, Christopher; Blunsom, Phil; Kavukcuoglu, Koray; Hassabis, Demis

2016-10-27

Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning, but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer. Like a conventional computer, it can use its memory to represent and manipulate complex data structures, but, like a neural network, it can learn to do so from data. When trained with supervised learning, we demonstrate that a DNC can successfully answer synthetic questions designed to emulate reasoning and inference problems in natural language. We show that it can learn tasks such as finding the shortest path between specified points and inferring the missing links in randomly generated graphs, and then generalize these tasks to specific graphs such as transport networks and family trees. When trained with reinforcement learning, a DNC can complete a moving blocks puzzle in which changing goals are specified by sequences of symbols. Taken together, our results demonstrate that DNCs have the capacity to solve complex, structured tasks that are inaccessible to neural networks without external read-write memory.