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.

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)

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.

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

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.

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

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.

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,…

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).

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.

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.

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

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.

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…

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).

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.

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…

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.

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.

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,…

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

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.

The Representation of Comparative Relations and the Transitive Inference Task

ERIC Educational Resources Information Center

Riley, Christine A.

1976-01-01

The question of how children represent and use comparative or partially ordered information is examined. Two experiments tested a conjecture that a common representation, a linear order, underlies the processing of all comparatives. (Author/MS)

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.

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.

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…

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.

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.

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.

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

Bayes and blickets: Effects of knowledge on causal induction in children and adults

PubMed Central

Griffiths, Thomas L.; Sobel, David M.; Tenenbaum, Joshua B.; Gopnik, Alison

2011-01-01

People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which participants learned about the causal properties of a set of objects. The studies varied the two factors that our Bayesian approach predicted should be relevant to causal induction: the prior probability with which causal relations exist, and the assumption of a deterministic or a probabilistic relation between cause and effect. Adults’ judgments (Experiments 1, 2, and 4) were in close correspondence with the quantitative predictions of the model, and children’s judgments (Experiments 3 and 5) agreed qualitatively with this account. PMID:21972897

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

The early emergence and puzzling decline of relational reasoning: Effects of knowledge and search on inferring abstract concepts.

PubMed

Walker, Caren M; Bridgers, Sophie; Gopnik, Alison

2016-11-01

We explore the developmental trajectory and underlying mechanisms of abstract relational reasoning. We describe a surprising developmental pattern: Younger learners are better than older ones at inferring abstract causal relations. Walker and Gopnik (2014) demonstrated that toddlers are able to infer that an effect was caused by a relation between two objects (whether they are the same or different), rather than by individual kinds of objects. While these findings are consistent with evidence that infants recognize same-different relations, they contrast with a large literature suggesting that older children tend to have difficulty inferring these relations. Why might this be? In Experiment 1a, we demonstrate that while younger children (18-30-month-olds) have no difficulty learning these relational concepts, older children (36-48-month-olds) fail to draw this abstract inference. Experiment 1b replicates the finding with 18-30-month-olds using a more demanding intervention task. Experiment 2 tests whether this difference in performance might be because older children have developed the general hypothesis that individual kinds of objects are causal - the high initial probability of this alternative hypothesis might override the data that favors the relational hypothesis. Providing additional information falsifying the alternative hypothesis improves older children's performance. Finally, Experiment 3 demonstrates that prompting for explanations during learning also improves performance, even without any additional information. These findings are discussed in light of recent computational and algorithmic theories of learning. Copyright © 2016 Elsevier B.V. All rights reserved.

Schema-driven facilitation of new hierarchy learning in the transitive inference paradigm

PubMed Central

Kumaran, Dharshan

2013-01-01

Prior knowledge, in the form of a mental schema or framework, is viewed to facilitate the learning of new information in a range of experimental and everyday scenarios. Despite rising interest in the cognitive and neural mechanisms underlying schema-driven facilitation of new learning, few paradigms have been developed to examine this issue in humans. Here we develop a multiphase experimental scenario aimed at characterizing schema-based effects in the context of a paradigm that has been very widely used across species, the transitive inference task. We show that an associative schema, comprised of prior knowledge of the rank positions of familiar items in the hierarchy, has a marked effect on transitivity performance and the development of relational knowledge of the hierarchy that cannot be accounted for by more general changes in task strategy. Further, we show that participants are capable of deploying prior knowledge to successful effect under surprising conditions (i.e., when corrective feedback is totally absent), but only when the associative schema is robust. Finally, our results provide insights into the cognitive mechanisms underlying such schema-driven effects, and suggest that new hierarchy learning in the transitive inference task can occur through a contextual transfer mechanism that exploits the structure of associative experiences. PMID:23782509

Schema-driven facilitation of new hierarchy learning in the transitive inference paradigm.

PubMed

Kumaran, Dharshan

2013-06-19

Prior knowledge, in the form of a mental schema or framework, is viewed to facilitate the learning of new information in a range of experimental and everyday scenarios. Despite rising interest in the cognitive and neural mechanisms underlying schema-driven facilitation of new learning, few paradigms have been developed to examine this issue in humans. Here we develop a multiphase experimental scenario aimed at characterizing schema-based effects in the context of a paradigm that has been very widely used across species, the transitive inference task. We show that an associative schema, comprised of prior knowledge of the rank positions of familiar items in the hierarchy, has a marked effect on transitivity performance and the development of relational knowledge of the hierarchy that cannot be accounted for by more general changes in task strategy. Further, we show that participants are capable of deploying prior knowledge to successful effect under surprising conditions (i.e., when corrective feedback is totally absent), but only when the associative schema is robust. Finally, our results provide insights into the cognitive mechanisms underlying such schema-driven effects, and suggest that new hierarchy learning in the transitive inference task can occur through a contextual transfer mechanism that exploits the structure of associative experiences.

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.

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

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.

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.

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…

A Proposed Resolution of Curious Conflicts in the Literature on Linear Syllogisms. Technical Report No. 8.

ERIC Educational Resources Information Center

Sternberg, Robert J.

Linear syllogisms present two premises, each describing a relation between two terms. The individual's task is to infer the relation among the three terms of the linear syllogism, and then answer a question about one or more of these relations. For example, "John is taller than Bob. Sam is shorter than Bob. Who is the tallest?" Students…

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

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.

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.

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.

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

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.

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).

Transitive inference in humans (Homo sapiens) and rhesus macaques (Macaca mulatta) after massed training of the last two list items.

PubMed

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

2017-08-01

Transitive inference (TI) is a classic learning paradigm for which the relative contributions of experienced rewards and representation-based inference have been debated vigorously, particularly regarding the notion that animals are capable of logic and reasoning. Rhesus macaque subjects and human participants performed a TI task in which, prior to learning a 7-item list (ABCDEFG), a block of trials presented exclusively the pair FG. Contrary to the expectation of associative models, the high prior rate of reward for F did not disrupt subsequent learning of the entire list. Monkeys (who each completed many sessions with novel stimuli) learned to anticipate that novel stimuli should be preferred over F. We interpret this as evidence of a task representation of TI that generalizes beyond learning about specific stimuli. Humans (who were task-naïve) showed a transitory bias to F when it was paired with novel stimuli, but very rapidly unlearned that bias. Performance with respect to the remaining stimuli was consistent with past reports of TI in both species. These results are difficult to reconcile with any account that assigns the strength of association between individual stimuli and rewards. Instead, they support sophisticated cognitive processes in both species, albeit with some species differences. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

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.

An investigation of reasoning by analogy in schizophrenia and autism spectrum disorder

PubMed Central

Krawczyk, Daniel C.; Kandalaft, Michelle R.; Didehbani, Nyaz; Allen, Tandra T.; McClelland, M. Michelle; Tamminga, Carol A.; Chapman, Sandra B.

2014-01-01

Relational reasoning ability relies upon by both cognitive and social factors. We compared analogical reasoning performance in healthy controls (HC) to performance in individuals with Autism Spectrum Disorder (ASD), and individuals with schizophrenia (SZ). The experimental task required participants to find correspondences between drawings of scenes. Participants were asked to infer which item within one scene best matched a relational item within the second scene. We varied relational complexity, presence of distraction, and type of objects in the analogies (living or non-living items). We hypothesized that the cognitive differences present in SZ would reduce relational inferences relative to ASD and HC. We also hypothesized that both SZ and ASD would show lower performance on living item problems relative to HC due to lower social function scores. Overall accuracy was higher for HC relative to SZ, consistent with prior research. Across groups, higher relational complexity reduced analogical responding, as did the presence of non-living items. Separate group analyses revealed that the ASD group was less accurate at making relational inferences in problems that involved mainly non-living items and when distractors were present. The SZ group showed differences in problem type similar to the ASD group. Additionally, we found significant correlations between social cognitive ability and analogical reasoning, particularly for the SZ group. These results indicate that differences in cognitive and social abilities impact the ability to infer analogical correspondences along with numbers of relational elements and types of objects present in the problems. PMID:25191240

Group social rank is associated with performance on a spatial learning task.

PubMed

Langley, Ellis J G; van Horik, Jayden O; Whiteside, Mark A; Madden, Joah R

2018-02-01

Dominant individuals differ from subordinates in their performances on cognitive tasks across a suite of taxa. Previous studies often only consider dyadic relationships, rather than the more ecologically relevant social hierarchies or networks, hence failing to account for how dyadic relationships may be adjusted within larger social groups. We used a novel statistical method: randomized Elo-ratings, to infer the social hierarchy of 18 male pheasants, Phasianus colchicus , while in a captive, mixed-sex group with a linear hierarchy. We assayed individual learning performance of these males on a binary spatial discrimination task to investigate whether inter-individual variation in performance is associated with group social rank. Task performance improved with increasing trial number and was positively related to social rank, with higher ranking males showing greater levels of success. Motivation to participate in the task was not related to social rank or task performance, thus indicating that these rank-related differences are not a consequence of differences in motivation to complete the task. Our results provide important information about how variation in cognitive performance relates to an individual's social rank within a group. Whether the social environment causes differences in learning performance or instead, inherent differences in learning ability predetermine rank remains to be tested.

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.

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.

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…

Exploiting graph kernels for high performance biomedical relation extraction.

PubMed

Panyam, Nagesh C; Verspoor, Karin; Cohn, Trevor; Ramamohanarao, Kotagiri

2018-01-30

Relation extraction from biomedical publications is an important task in the area of semantic mining of text. Kernel methods for supervised relation extraction are often preferred over manual feature engineering methods, when classifying highly ordered structures such as trees and graphs obtained from syntactic parsing of a sentence. Tree kernels such as the Subset Tree Kernel and Partial Tree Kernel have been shown to be effective for classifying constituency parse trees and basic dependency parse graphs of a sentence. Graph kernels such as the All Path Graph kernel (APG) and Approximate Subgraph Matching (ASM) kernel have been shown to be suitable for classifying general graphs with cycles, such as the enhanced dependency parse graph of a sentence. In this work, we present a high performance Chemical-Induced Disease (CID) relation extraction system. We present a comparative study of kernel methods for the CID task and also extend our study to the Protein-Protein Interaction (PPI) extraction task, an important biomedical relation extraction task. We discuss novel modifications to the ASM kernel to boost its performance and a method to apply graph kernels for extracting relations expressed in multiple sentences. Our system for CID relation extraction attains an F-score of 60%, without using external knowledge sources or task specific heuristic or rules. In comparison, the state of the art Chemical-Disease Relation Extraction system achieves an F-score of 56% using an ensemble of multiple machine learning methods, which is then boosted to 61% with a rule based system employing task specific post processing rules. For the CID task, graph kernels outperform tree kernels substantially, and the best performance is obtained with APG kernel that attains an F-score of 60%, followed by the ASM kernel at 57%. The performance difference between the ASM and APG kernels for CID sentence level relation extraction is not significant. In our evaluation of ASM for the PPI task, ASM performed better than APG kernel for the BioInfer dataset, in the Area Under Curve (AUC) measure (74% vs 69%). However, for all the other PPI datasets, namely AIMed, HPRD50, IEPA and LLL, ASM is substantially outperformed by the APG kernel in F-score and AUC measures. We demonstrate a high performance Chemical Induced Disease relation extraction, without employing external knowledge sources or task specific heuristics. Our work shows that graph kernels are effective in extracting relations that are expressed in multiple sentences. We also show that the graph kernels, namely the ASM and APG kernels, substantially outperform the tree kernels. Among the graph kernels, we showed the ASM kernel as effective for biomedical relation extraction, with comparable performance to the APG kernel for datasets such as the CID-sentence level relation extraction and BioInfer in PPI. Overall, the APG kernel is shown to be significantly more accurate than the ASM kernel, achieving better performance on most datasets.

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.

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.

Inference generation during discourse and its relation to social competence: an online investigation of abilities of children with and without language impairment.

PubMed

Ford, Janet A; Milosky, Linda M

2008-04-01

This study examined whether young children with typical language development (TL) and children with language impairment (LI) make emotion inferences online during the process of discourse comprehension, identified variables that predict emotion inferencing, and explored the relationship of these variables to social competence. Preschool children (16 TL and 16 LI) watched narrated videos designed to activate knowledge about a particular emotional state. Following each story, children named a facial expression that either matched or did not match the anticipated emotion. Several experimental tasks examined linguistic and nonlinguistic abilities. Finally, each child's teacher completed a measure of social competence. Children with TL named expressions significantly more slowly in the mismatched condition than in the matched condition, whereas children with LI did not differ in response times between the conditions. Language and vocal response time measures were related to emotion inferencing ability, and this ability predicted social competence scores. The findings suggest that children with TL are inferring emotions during the comprehension process, whereas children with LI often fail to make these inferences. Making emotion inferences is related to discourse comprehension and to social competence in children. The current findings provide evidence that language and vocal response time measures predicted inferencing ability and suggest that additional factors may influence discourse inferencing and social competence.

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

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.

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.

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.

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).

Responses on a lateralized lexical decision task relate to both reading times and comprehension.

PubMed

Michael, Mary

2009-12-01

Research over the last few years has shown that the dominance of the left hemisphere in language processing is less complete than previously thought [Beeman, M. (1993). Semantic processing in the right hemisphere may contribute to drawing inferences from discourse. Brain and Language, 44, 80-120; Faust, M., & Chiarello, C. (1998). Sentence context and lexical ambiguity resolution by the two hemispheres. Neuropsychologia, 36(9), 827-835; Weems, S. A., & Zaidel, E. (2004). The relationship between reading ability and lateralized lexical decision. Brain and Cognition, 55(3), 507-515]. Engaging the right brain in language processing is required for processing speaker/writer intention, particularly in those subtle interpretive processes that help in deciphering humor, irony, and emotional inference. In two experiments employing a divided field or lateralized lexical decision task (LLDT), accuracy and reaction times (RTs) were related to reading times and comprehension on sentence reading. Differences seen in RTs and error rates by visual fields were found to relate to performance. Smaller differences in performance between fields tended to be related to better performance on the LLDT in both experiments and, in Experiment 1, to reading measures. Readers who can exploit both hemispheres for language processing equally appear to be at an advantage in lexical access and possibly also in reading performance.

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.

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.

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.

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.

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.

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…

Responses on a Lateralized Lexical Decision Task Relate to both Reading Times and Comprehension

ERIC Educational Resources Information Center

Michael, Mary

2009-01-01

Research over the last few years has shown that the dominance of the left hemisphere in language processing is less complete than previously thought [Beeman, M. (1993). "Semantic processing in the right hemisphere may contribute to drawing inferences from discourse." "Brain and Language," 44, 80-120; Faust, M., & Chiarello, C. (1998). "Sentence…

Departure from Normality in Multivariate Normative Comparison: The Cramer Alternative for Hotelling's "T[squared]"

ERIC Educational Resources Information Center

Grasman, Raoul P. P. P.; Huizenga, Hilde M.; Geurts, Hilde M.

2010-01-01

Crawford and Howell (1998) have pointed out that the common practice of z-score inference on cognitive disability is inappropriate if a patient's performance on a task is compared with relatively few typical control individuals. Appropriate univariate and multivariate statistical tests have been proposed for these studies, but these are only valid…

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…

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.

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.

Biological motion perception links diverse facets of theory of mind during middle childhood.

PubMed

Rice, Katherine; Anderson, Laura C; Velnoskey, Kayla; Thompson, James C; Redcay, Elizabeth

2016-06-01

Two cornerstones of social development--social perception and theory of mind--undergo brain and behavioral changes during middle childhood, but the link between these developing domains is unclear. One theoretical perspective argues that these skills represent domain-specific areas of social development, whereas other perspectives suggest that both skills may reflect a more integrated social system. Given recent evidence from adults that these superficially different domains may be related, the current study examined the developmental relation between these social processes in 52 children aged 7 to 12 years. Controlling for age and IQ, social perception (perception of biological motion in noise) was significantly correlated with two measures of theory of mind: one in which children made mental state inferences based on photographs of the eye region of the face and another in which children made mental state inferences based on stories. Social perception, however, was not correlated with children's ability to make physical inferences from stories about people. Furthermore, the mental state inference tasks were not correlated with each other, suggesting a role for social perception in linking various facets of theory of mind. Copyright © 2015 Elsevier Inc. All rights reserved.

Reprint of "Biological motion perception links diverse facets of theory of mind during middle childhood".

PubMed

Rice, Katherine; Anderson, Laura C; Velnoskey, Kayla; Thompson, James C; Redcay, Elizabeth

2016-09-01

Two cornerstones of social development-social perception and theory of mind-undergo brain and behavioral changes during middle childhood, but the link between these developing domains is unclear. One theoretical perspective argues that these skills represent domain-specific areas of social development, whereas other perspectives suggest that both skills may reflect a more integrated social system. Given recent evidence from adults that these superficially different domains may be related, the current study examined the developmental relation between these social processes in 52 children aged 7 to 12years. Controlling for age and IQ, social perception (perception of biological motion in noise) was significantly correlated with two measures of theory of mind: one in which children made mental state inferences based on photographs of the eye region of the face and another in which children made mental state inferences based on stories. Social perception, however, was not correlated with children's ability to make physical inferences from stories about people. Furthermore, the mental state inference tasks were not correlated with each other, suggesting a role for social perception in linking various facets of theory of mind. Copyright © 2015 Elsevier Inc. All rights reserved.

Sources of group differences in functional connectivity: an investigation applied to autism spectrum disorder.

PubMed

Jones, Tyler B; Bandettini, Peter A; Kenworthy, Lauren; Case, Laura K; Milleville, Shawn C; Martin, Alex; Birn, Rasmus M

2010-01-01

An increasing number of fMRI studies are using the correlation of low-frequency fluctuations between brain regions, believed to reflect synchronized variations in neuronal activity, to infer "functional connectivity". In studies of autism spectrum disorder (ASD), decreases in this measure of connectivity have been found by focusing on the response to task modulation, by using only the rest periods, or by analyzing purely resting-state data. This difference in connectivity, however, could result from a number of different mechanisms--differences in noise, task-related fluctuations, task performance, or spontaneous neuronal activity. In this study, we investigate the difference in functional connectivity between adolescents with high-functioning ASD and typically developing control subjects by examining the residual fluctuations occurring on top of the fMRI response to an overt verbal fluency task. We find decreased correlations of these residuals (a decreased "connectivity") in ASD subjects. Furthermore, we find that this decrease was not due to task-related effects, block-to-block variations in task performance, or increased noise, and the difference was greatest when primarily rest periods are considered. These findings suggest that the estimate of disrupted functional connectivity in ASD is likely driven by differences in task-unrelated neuronal fluctuations.

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.

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 and the brain: a meta-analysis.

PubMed

Van Overwalle, Frank

2009-03-01

This meta-analysis explores the location and function of brain areas involved in social cognition, or the capacity to understand people's behavioral intentions, social beliefs, and personality traits. On the basis of over 200 fMRI studies, it tests alternative theoretical proposals that attempt to explain how several brain areas process information relevant for social cognition. The results suggest that inferring temporary states such as goals, intentions, and desires of other people-even when they are false and unjust from our own perspective--strongly engages the temporo-parietal junction (TPJ). Inferring more enduring dispositions of others and the self, or interpersonal norms and scripts, engages the medial prefrontal cortex (mPFC), although temporal states can also activate the mPFC. Other candidate tasks reflecting general-purpose brain processes that may potentially subserve social cognition are briefly reviewed, such as sequence learning, causality detection, emotion processing, and executive functioning (action monitoring, attention, dual task monitoring, episodic memory retrieval), but none of them overlaps uniquely with the regions activated during social cognition. Hence, it appears that social cognition particularly engages the TPJ and mPFC regions. The available evidence is consistent with the role of a TPJ-related mirror system for inferring temporary goals and intentions at a relatively perceptual level of representation, and the mPFC as a module that integrates social information across time and allows reflection and representation of traits and norms, and presumably also of intentionality, at a more abstract cognitive level.

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.

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…

Social-cognitive deficits in normal aging

PubMed Central

Moran, Joseph M.; Jolly, Eshin; Mitchell, Jason P.

2012-01-01

A sizeable number of studies have implicated the default network (e.g., medial prefrontal and parietal cortices) in tasks that require participants to infer the mental states of others—that is, to mentalize. Parallel research has demonstrated that default network function declines over the lifespan, suggesting that older adults may show impairments in social-cognitive tasks that require mentalizing. Older and younger human adults were scanned using functional magnetic resonance imaging (fMRI) while performing three different social-cognitive tasks. Across three mentalizing paradigms, younger and older adults viewed animated shapes in brief social vignettes, stories about a person's moral actions and false belief stories. Consistent with predictions, older adults responded less accurately to stories about others' false beliefs and made less use of actors' intentions to judge the moral permissibility of behavior. These impairments in performance during social-cognitive tasks were accompanied by age-related decreases across all three paradigms in the BOLD response of a single brain region—dorsomedial prefrontal cortex. These findings suggest specific, task-independent age-related deficits in mentalizing that are localizeable to changes in circumscribed subregions of the default network. PMID:22514317

Hippocampal structure predicts statistical learning and associative inference abilities during development

PubMed Central

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

2016-01-01

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 6–30 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. PMID:27575916

Cognitive Abilities on Transitive Inference Using a Novel Touchscreen Technology for Mice

PubMed Central

Silverman, J.L.; Gastrell, P.T.; Karras, M.N.; Solomon, M.; Crawley, J.N.

2015-01-01

Cognitive abilities are impaired in neurodevelopmental disorders, including autism spectrum disorder (ASD) and schizophrenia. Preclinical models with strong endophenotypes relevant to cognitive dysfunctions offer a valuable resource for therapeutic development. However, improved assays to test higher order cognition are needed. We employed touchscreen technology to design a complex transitive inference (TI) assay that requires cognitive flexibility and relational learning. C57BL/6J (B6) mice with good cognitive skills and BTBR T+tf/J (BTBR), a model of ASD with cognitive deficits, were evaluated in simple and complex touchscreen assays. Both B6 and BTBR acquired visual discrimination and reversal. BTBR displayed deficits on components of TI, when 4 stimuli pairs were interspersed, which required flexible integrated knowledge. BTBR displayed impairment on the A > E inference, analogous to the A > E deficit in ASD. B6 and BTBR mice both reached criterion on the B > D comparison, unlike the B > D impairment in schizophrenia. These results demonstrate that mice are capable of complex discriminations and higher order tasks using methods and equipment paralleling those used in humans. Our discovery that a mouse model of ASD displays a TI deficit similar to humans with ASD supports the use of the touchscreen technology for complex cognitive tasks in mouse models of neurodevelopmental disorders. PMID:24293564

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…

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.

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.

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.

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).

Grouping individual independent BOLD effects: a new way to ICA group analysis

NASA Astrophysics Data System (ADS)

Duann, Jeng-Ren; Jung, Tzyy-Ping; Sejnowski, Terrence J.; Makeig, Scott

2009-04-01

A new group analysis method to summarize the task-related BOLD responses based on independent component analysis (ICA) was presented. As opposite to the previously proposed group ICA (gICA) method, which first combined multi-subject fMRI data in either temporal or spatial domain and applied ICA decomposition only once to the combined fMRI data to extract the task-related BOLD effects, the method presented here applied ICA decomposition to the individual subjects' fMRI data to first find the independent BOLD effects specifically for each individual subject. Then, the task-related independent BOLD component was selected among the resulting independent components from the single-subject ICA decomposition and hence grouped across subjects to derive the group inference. In this new ICA group analysis (ICAga) method, one does not need to assume that the task-related BOLD time courses are identical across brain areas and subjects as used in the grand ICA decomposition on the spatially concatenated fMRI data. Neither does one need to assume that after spatial normalization, the voxels at the same coordinates represent exactly the same functional or structural brain anatomies across different subjects. These two assumptions have been problematic given the recent BOLD activation evidences. Further, since the independent BOLD effects were obtained from each individual subject, the ICAga method can better account for the individual differences in the task-related BOLD effects. Unlike the gICA approach whereby the task-related BOLD effects could only be accounted for by a single unified BOLD model across multiple subjects. As a result, the newly proposed method, ICAga, was able to better fit the task-related BOLD effects at individual level and thus allow grouping more appropriate multisubject BOLD effects in the group analysis.

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)

Inferring ontology graph structures using OWL reasoning.

PubMed

Rodríguez-García, Miguel Ángel; Hoehndorf, Robert

2018-01-05

Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology enrichment or computation of semantic similarity. More recently, ontologies are developed in a formal language such as the Web Ontology Language (OWL) and consist of a set of axioms through which classes are defined or constrained. While the taxonomy of an ontology can be inferred directly from the axioms of an ontology as one of the standard OWL reasoning tasks, creating general graph structures from OWL ontologies that exploit the ontologies' semantic content remains a challenge. We developed a method to transform ontologies into graphs using an automated reasoner while taking into account all relations between classes. Searching for (existential) patterns in the deductive closure of ontologies, we can identify relations between classes that are implied but not asserted and generate graph structures that encode for a large part of the ontologies' semantic content. We demonstrate the advantages of our method by applying it to inference of protein-protein interactions through semantic similarity over the Gene Ontology and demonstrate that performance is increased when graph structures are inferred using deductive inference according to our method. Our software and experiment results are available at http://github.com/bio-ontology-research-group/Onto2Graph . Onto2Graph is a method to generate graph structures from OWL ontologies using automated reasoning. The resulting graphs can be used for improved ontology visualization and ontology-based data analysis.

Understanding advanced theory of mind and empathy in high-functioning adults with autism spectrum disorder.

PubMed

Mathersul, Danielle; McDonald, Skye; Rushby, Jacqueline A

2013-01-01

It has been argued that higher functioning individuals with autism spectrum disorders (ASDs) have specific deficits in advanced but not simple theory of mind (ToM), yet the questionable ecological validity of some tasks reduces the strength of this assumption. The present study employed The Awareness of Social Inference Test (TASIT), which uses video vignettes to assess comprehension of subtle conversational inferences (sarcasm, lies/deception). Given the proposed relationships between advanced ToM and cognitive and affective empathy, these associations were also investigated. As expected, the high-functioning adults with ASDs demonstrated specific deficits in comprehending the beliefs, intentions, and meaning of nonliteral expressions. They also had significantly lower cognitive and affective empathy. Cognitive empathy was related to ToM and group membership whereas affective empathy was only related to group membership.

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

The Influence of Task Dynamics on Inductive Generalizations: How Sequential and Simultaneous Presentation of Evidence Impacts the Strength and Scope of Property Projections

ERIC Educational Resources Information Center

Lawson, Chris A.

2017-01-01

Young children are remarkably flexible reasoners insofar as they modify their inferences to accommodate the conceptual information or perceptual relations represented in an inductive problem. Children's inductive reasoning is highly sensitive to what evidence is presented to them. Four experiments with 115 preschoolers (M[subscript age] = 4;8) and…

Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data

DTIC Science & Technology

2014-11-01

NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING...we can apply our results to problems of attrition in which missingness is a severe obstacle to sound inferences. Related works are discussed in...due to the collider path between Y and Ry ). 8 Related Work Deletion based methods such as listwise deletion that are easy to understand as well as

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.

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.

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.

Unobtrusive monitoring of divided attention in a cognitive health coaching intervention for the elderly.

PubMed

McKanna, James A; Pavel, Misha; Jimison, Holly

2010-11-13

Assessment of cognitive functionality is an important aspect of care for elders. Unfortunately, few tools exist to measure divided attention, the ability to allocate attention to different aspects of tasks. An accurate determination of divided attention would allow inference of generalized cognitive decline, as well as providing a quantifiable indicator of an important component of driving skill. We propose a new method for determining relative divided attention ability through unobtrusive monitoring of computer use. Specifically, we measure performance on a dual-task cognitive computer exercise as part of a health coaching intervention. This metric indicates whether the user has the ability to pay attention to both tasks at once, or is primarily attending to one task at a time (sacrificing optimal performance). The monitoring of divided attention in a home environment is a key component of both the early detection of cognitive problems and for assessing the efficacy of coaching interventions.

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.

Specialization and group size: brain and behavioural correlates of colony size in ants lacking morphological castes

PubMed Central

Amador-Vargas, Sabrina; Gronenberg, Wulfila; Wcislo, William T.; Mueller, Ulrich

2015-01-01

Group size in both multicellular organisms and animal societies can correlate with the degree of division of labour. For ants, the task specialization hypothesis (TSH) proposes that increased behavioural specialization enabled by larger group size corresponds to anatomical specialization of worker brains. Alternatively, the social brain hypothesis proposes that increased levels of social stimuli in larger colonies lead to enlarged brain regions in all workers, regardless of their task specialization. We tested these hypotheses in acacia ants (Pseudomyrmex spinicola), which exhibit behavioural but not morphological task specialization. In wild colonies, we marked, followed and tested ant workers involved in foraging tasks on the leaves (leaf-ants) and in defensive tasks on the host tree trunk (trunk-ants). Task specialization increased with colony size, especially in defensive tasks. The relationship between colony size and brain region volume was task-dependent, supporting the TSH. Specifically, as colony size increased, the relative size of regions within the mushroom bodies of the brain decreased in trunk-ants but increased in leaf-ants; those regions play important roles in learning and memory. Our findings suggest that workers specialized in defence may have reduced learning abilities relative to leaf-ants; these inferences remain to be tested. In societies with monomorphic workers, brain polymorphism enhanced by group size could be a mechanism by which division of labour is achieved. PMID:25567649

Gaming is related to enhanced working memory performance and task-related cortical activity.

PubMed

Moisala, M; Salmela, V; Hietajärvi, L; Carlson, S; Vuontela, V; Lonka, K; Hakkarainen, K; Salmela-Aro, K; Alho, K

2017-01-15

Gaming experience has been suggested to lead to performance enhancements in a wide variety of working memory tasks. Previous studies have, however, mostly focused on adult expert gamers and have not included measurements of both behavioral performance and brain activity. In the current study, 167 adolescents and young adults (aged 13-24 years) with different amounts of gaming experience performed an n-back working memory task with vowels, with the sensory modality of the vowel stream switching between audition and vision at random intervals. We studied the relationship between self-reported daily gaming activity, working memory (n-back) task performance and related brain activity measured using functional magnetic resonance imaging (fMRI). The results revealed that the extent of daily gaming activity was related to enhancements in both performance accuracy and speed during the most demanding (2-back) level of the working memory task. This improved working memory performance was accompanied by enhanced recruitment of a fronto-parietal cortical network, especially the dorsolateral prefrontal cortex. In contrast, during the less demanding (1-back) level of the task, gaming was associated with decreased activity in the same cortical regions. Our results suggest that a greater degree of daily gaming experience is associated with better working memory functioning and task difficulty-dependent modulation in fronto-parietal brain activity already in adolescence and even when non-expert gamers are studied. The direction of causality within this association cannot be inferred with certainty due to the correlational nature of the current study. Copyright © 2016 Elsevier B.V. All rights reserved.

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

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;…

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.

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.

Using memories to understand others: the role of episodic memory in theory of mind impairment in Alzheimer disease.

PubMed

Moreau, Noémie; Viallet, François; Champagne-Lavau, Maud

2013-09-01

Theory of mind (TOM) refers to the ability to infer one's own and other's mental states. Growing evidence highlighted the presence of impairment on the most complex TOM tasks in Alzheimer disease (AD). However, how TOM deficit is related to other cognitive dysfunctions and more specifically to episodic memory impairment - the prominent feature of this disease - is still under debate. Recent neuroanatomical findings have shown that remembering past events and inferring others' states of mind share the same cerebral network suggesting the two abilities share a common process .This paper proposes to review emergent evidence of TOM impairment in AD patients and to discuss the evidence of a relationship between TOM and episodic memory. We will discuss about AD patients' deficit in TOM being possibly related to their difficulties in recollecting memories of past social interactions. Copyright © 2013 Elsevier B.V. All rights reserved.

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.

Reward inference by primate prefrontal and striatal neurons.

PubMed

Pan, Xiaochuan; Fan, Hongwei; Sawa, Kosuke; Tsuda, Ichiro; Tsukada, Minoru; Sakagami, Masamichi

2014-01-22

The brain contains multiple yet distinct systems involved in reward prediction. To understand the nature of these processes, we recorded single-unit activity from the lateral prefrontal cortex (LPFC) and the striatum in monkeys performing a reward inference task using an asymmetric reward schedule. We found that neurons both in the LPFC and in the striatum predicted reward values for stimuli that had been previously well experienced with set reward quantities in the asymmetric reward task. Importantly, these LPFC neurons could predict the reward value of a stimulus using transitive inference even when the monkeys had not yet learned the stimulus-reward association directly; whereas these striatal neurons did not show such an ability. Nevertheless, because there were two set amounts of reward (large and small), the selected striatal neurons were able to exclusively infer the reward value (e.g., large) of one novel stimulus from a pair after directly experiencing the alternative stimulus with the other reward value (e.g., small). Our results suggest that although neurons that predict reward value for old stimuli in the LPFC could also do so for new stimuli via transitive inference, those in the striatum could only predict reward for new stimuli via exclusive inference. Moreover, the striatum showed more complex functions than was surmised previously for model-free learning.

Reward Inference by Primate Prefrontal and Striatal Neurons

PubMed Central

Pan, Xiaochuan; Fan, Hongwei; Sawa, Kosuke; Tsuda, Ichiro; Tsukada, Minoru

2014-01-01

The brain contains multiple yet distinct systems involved in reward prediction. To understand the nature of these processes, we recorded single-unit activity from the lateral prefrontal cortex (LPFC) and the striatum in monkeys performing a reward inference task using an asymmetric reward schedule. We found that neurons both in the LPFC and in the striatum predicted reward values for stimuli that had been previously well experienced with set reward quantities in the asymmetric reward task. Importantly, these LPFC neurons could predict the reward value of a stimulus using transitive inference even when the monkeys had not yet learned the stimulus–reward association directly; whereas these striatal neurons did not show such an ability. Nevertheless, because there were two set amounts of reward (large and small), the selected striatal neurons were able to exclusively infer the reward value (e.g., large) of one novel stimulus from a pair after directly experiencing the alternative stimulus with the other reward value (e.g., small). Our results suggest that although neurons that predict reward value for old stimuli in the LPFC could also do so for new stimuli via transitive inference, those in the striatum could only predict reward for new stimuli via exclusive inference. Moreover, the striatum showed more complex functions than was surmised previously for model-free learning. PMID:24453328

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.

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.

Possible roles for fronto-striatal circuits in reading disorder

PubMed Central

Hancock, Roeland; Richlan, Fabio; Hoeft, Fumiko

2016-01-01

Several studies have reported hyperactivation in frontal and striatal regions in individuals with reading disorder (RD) during reading-related tasks. Hyperactivation in these regions is typically interpreted as a form of neural compensation and related to articulatory processing. Fronto-striatal hyperactivation in RD can however, also arise from fundamental impairment in reading related processes, such as phonological processing and implicit sequence learning relevant to early language acquisition. We review current evidence for the compensation hypothesis in RD and apply large-scale reverse inference to investigate anatomical overlap between hyperactivation regions and neural systems for articulation, phonological processing, implicit sequence learning. We found anatomical convergence between hyperactivation regions and regions supporting articulation, consistent with the proposed compensatory role of these regions, and low convergence with phonological and implicit sequence learning regions. Although the application of large-scale reverse inference to decode function in a clinical population should be interpreted cautiously, our findings suggest future lines of research that may clarify the functional significance of hyperactivation in RD. PMID:27826071

Expressing pride: Effects on perceived agency, communality, and stereotype-based gender disparities.

PubMed

Brosi, Prisca; Spörrle, Matthias; Welpe, Isabell M; Heilman, Madeline E

2016-09-01

Two experimental studies were conducted to investigate how the expression of pride shapes agency-related and communality-related judgments, and how those judgments differ when the pride expresser is a man or a woman. Results indicated that the expression of pride (as compared to the expression of happiness) had positive effects on perceptions of agency and inferences about task-oriented leadership competence, and negative effects on perceptions of communality and inferences about people-oriented leadership competence. Pride expression also elevated ascriptions of interpersonal hostility. For agency-related judgments and ascriptions of interpersonal hostility, these effects were consistently stronger when the pride expresser was a woman than a man. Moreover, the expression of pride was found to affect disparities in judgments about men and women, eliminating the stereotype-consistent differences that were evident when happiness was expressed. With a display of pride women were not seen as any more deficient in agency-related attributes and competencies, nor were they seen as any more exceptional in communality-related attributes and competencies, than were men. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

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.

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).

Is Neural Activity Detected by ERP-Based Brain-Computer Interfaces Task Specific?

PubMed

Wenzel, Markus A; Almeida, Inês; Blankertz, Benjamin

2016-01-01

Brain-computer interfaces (BCIs) that are based on event-related potentials (ERPs) can estimate to which stimulus a user pays particular attention. In typical BCIs, the user silently counts the selected stimulus (which is repeatedly presented among other stimuli) in order to focus the attention. The stimulus of interest is then inferred from the electroencephalogram (EEG). Detecting attention allocation implicitly could be also beneficial for human-computer interaction (HCI), because it would allow software to adapt to the user's interest. However, a counting task would be inappropriate for the envisaged implicit application in HCI. Therefore, the question was addressed if the detectable neural activity is specific for silent counting, or if it can be evoked also by other tasks that direct the attention to certain stimuli. Thirteen people performed a silent counting, an arithmetic and a memory task. The tasks required the subjects to pay particular attention to target stimuli of a random color. The stimulus presentation was the same in all three tasks, which allowed a direct comparison of the experimental conditions. Classifiers that were trained to detect the targets in one task, according to patterns present in the EEG signal, could detect targets in all other tasks (irrespective of some task-related differences in the EEG). The neural activity detected by the classifiers is not strictly task specific but can be generalized over tasks and is presumably a result of the attention allocation or of the augmented workload. The results may hold promise for the transfer of classification algorithms from BCI research to implicit relevance detection in HCI.

No role of beta receptors in cognitive flexibility: Evidence from a task-switching paradigm in a randomized controlled trial.

PubMed

Steenbergen, L; Sellaro, R; de Rover, M; Hommel, B; Colzato, L S

2015-06-04

There is evidence that noradrenergic coeruleo-cortical projections are involved in different forms of cognitive flexibility. So far, no studies in humans have investigated the involvement of beta receptors on task-switching performance, a well-established measure of cognitive flexibility. The present study investigated whether the administration of propranolol (a central and peripheral beta-adrenergic antagonist) affected switching costs (i.e., the increase of reaction time in task-switching trials relative to task-repetition trials). Sixteen healthy adult human subjects performed a global-local task-switching paradigm in a double-blind, within-subjects design study investigating the effects of 80mg of propranolol hydrochloride (a β1 and β2 adrenergic receptor antagonist) vs. an oral dose of microcrystalline cellulose (placebo pill). The acute administration of propranolol did not affect the size of switching costs compared to the intake of the neutral placebo. Our results, corroborated by Bayesian inference, suggest that beta receptors do not modulate cognitive flexibility as measured by task-switching performance. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

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…

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.

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.

ERP correlates of object recognition memory in Down syndrome: Do active and passive tasks measure the same thing?

PubMed

Van Hoogmoed, A H; Nadel, L; Spanò, G; Edgin, J O

2016-02-01

Event related potentials (ERPs) can help to determine the cognitive and neural processes underlying memory functions and are often used to study populations with severe memory impairment. In healthy adults, memory is typically assessed with active tasks, while in patient studies passive memory paradigms are generally used. In this study we examined whether active and passive continuous object recognition tasks measure the same underlying memory process in typically developing (TD) adults and in individuals with Down syndrome (DS), a population with known hippocampal impairment. We further explored how ERPs in these tasks relate to behavioral measures of memory. Data-driven analysis techniques revealed large differences in old-new effects in the active versus passive task in TD adults, but no difference between these tasks in DS. The group with DS required additional processing in the active task in comparison to the TD group in two ways. First, the old-new effect started 150 ms later. Second, more repetitions were required to show the old-new effect. In the group with DS, performance on a behavioral measure of object-location memory was related to ERP measures across both tasks. In total, our results suggest that active and passive ERP memory measures do not differ in DS and likely reflect the use of implicit memory, but not explicit processing, on both tasks. Our findings highlight the need for a greater understanding of the comparison between active and passive ERP paradigms before they are inferred to measure similar functions across populations (e.g., infants or intellectual disability). Copyright © 2016 Elsevier Ltd. All rights reserved.

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

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.

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.

Removing an intersubject variance component in a general linear model improves multiway factoring of event-related spectral perturbations in group EEG studies.

PubMed

Spence, Jeffrey S; Brier, Matthew R; Hart, John; Ferree, Thomas C

2013-03-01

Linear statistical models are used very effectively to assess task-related differences in EEG power spectral analyses. Mixed models, in particular, accommodate more than one variance component in a multisubject study, where many trials of each condition of interest are measured on each subject. Generally, intra- and intersubject variances are both important to determine correct standard errors for inference on functions of model parameters, but it is often assumed that intersubject variance is the most important consideration in a group study. In this article, we show that, under common assumptions, estimates of some functions of model parameters, including estimates of task-related differences, are properly tested relative to the intrasubject variance component only. A substantial gain in statistical power can arise from the proper separation of variance components when there is more than one source of variability. We first develop this result analytically, then show how it benefits a multiway factoring of spectral, spatial, and temporal components from EEG data acquired in a group of healthy subjects performing a well-studied response inhibition task. Copyright © 2011 Wiley Periodicals, Inc.

Multichannel Convolutional Neural Network for Biological Relation Extraction.

PubMed

Quan, Chanqin; Hua, Lei; Sun, Xiao; Bai, Wenjun

2016-01-01

The plethora of biomedical relations which are embedded in medical logs (records) demands researchers' attention. Previous theoretical and practical focuses were restricted on traditional machine learning techniques. However, these methods are susceptible to the issues of "vocabulary gap" and data sparseness and the unattainable automation process in feature extraction. To address aforementioned issues, in this work, we propose a multichannel convolutional neural network (MCCNN) for automated biomedical relation extraction. The proposed model has the following two contributions: (1) it enables the fusion of multiple (e.g., five) versions in word embeddings; (2) the need for manual feature engineering can be obviated by automated feature learning with convolutional neural network (CNN). We evaluated our model on two biomedical relation extraction tasks: drug-drug interaction (DDI) extraction and protein-protein interaction (PPI) extraction. For DDI task, our system achieved an overall f -score of 70.2% compared to the standard linear SVM based system (e.g., 67.0%) on DDIExtraction 2013 challenge dataset. And for PPI task, we evaluated our system on Aimed and BioInfer PPI corpus; our system exceeded the state-of-art ensemble SVM system by 2.7% and 5.6% on f -scores.

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

Prospective memory and aging: evidence for preserved spontaneous retrieval with exact but not related cues.

PubMed

Mullet, Hillary G; Scullin, Michael K; Hess, Theodore J; Scullin, Rachel B; Arnold, Kathleen M; Einstein, Gilles O

2013-12-01

We examined whether normal aging spares or compromises cue-driven spontaneous retrieval processes that support prospective remembering. In Experiment 1, young and older adults performed prospective-memory tasks that required either strategic monitoring processes for retrieval (nonfocal) or for which participants relied on spontaneous retrieval processes (focal). We found age differences for nonfocal, but not focal, prospective-memory performance. Experiments 2 and 3 used an intention-interference paradigm in which participants were asked to perform a prospective-memory task (e.g., press "Q" when the word money appears) in the context of an image-rating task and were then told to suspend their prospective-memory intention until after completing an intervening lexical-decision task. During the lexical-decision task, we presented the exact prospective-memory cue (e.g., money; Experiments 2 and 3) or a semantically related lure (e.g., wallet; Experiment 3), and we inferred spontaneous retrieval from slowed lexical-decision responses to these items relative to matched control items. Young and older adults showed significant slowing when the exact prospective-memory cue was presented. Only young adults, however, showed significant slowing to the semantically related lure items. Collectively, these results partially support the multiprocess theory prediction that aging spares spontaneous retrieval processes. Spontaneous retrieval processes may become less sensitive with aging, such that older adults are less likely to respond to cues that do not exactly match their encoded targets. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Specialization and group size: brain and behavioural correlates of colony size in ants lacking morphological castes.

PubMed

Amador-Vargas, Sabrina; Gronenberg, Wulfila; Wcislo, William T; Mueller, Ulrich

2015-02-22

Group size in both multicellular organisms and animal societies can correlate with the degree of division of labour. For ants, the task specialization hypothesis (TSH) proposes that increased behavioural specialization enabled by larger group size corresponds to anatomical specialization of worker brains. Alternatively, the social brain hypothesis proposes that increased levels of social stimuli in larger colonies lead to enlarged brain regions in all workers, regardless of their task specialization. We tested these hypotheses in acacia ants (Pseudomyrmex spinicola), which exhibit behavioural but not morphological task specialization. In wild colonies, we marked, followed and tested ant workers involved in foraging tasks on the leaves (leaf-ants) and in defensive tasks on the host tree trunk (trunk-ants). Task specialization increased with colony size, especially in defensive tasks. The relationship between colony size and brain region volume was task-dependent, supporting the TSH. Specifically, as colony size increased, the relative size of regions within the mushroom bodies of the brain decreased in trunk-ants but increased in leaf-ants; those regions play important roles in learning and memory. Our findings suggest that workers specialized in defence may have reduced learning abilities relative to leaf-ants; these inferences remain to be tested. In societies with monomorphic workers, brain polymorphism enhanced by group size could be a mechanism by which division of labour is achieved. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Learning and inference using complex generative models in a spatial localization task.

PubMed

Bejjanki, Vikranth R; Knill, David C; Aslin, Richard N

2016-01-01

A large body of research has established that, under relatively simple task conditions, human observers integrate uncertain sensory information with learned prior knowledge in an approximately Bayes-optimal manner. However, in many natural tasks, observers must perform this sensory-plus-prior integration when the underlying generative model of the environment consists of multiple causes. Here we ask if the Bayes-optimal integration seen with simple tasks also applies to such natural tasks when the generative model is more complex, or whether observers rely instead on a less efficient set of heuristics that approximate ideal performance. Participants localized a "hidden" target whose position on a touch screen was sampled from a location-contingent bimodal generative model with different variances around each mode. Over repeated exposure to this task, participants learned the a priori locations of the target (i.e., the bimodal generative model), and integrated this learned knowledge with uncertain sensory information on a trial-by-trial basis in a manner consistent with the predictions of Bayes-optimal behavior. In particular, participants rapidly learned the locations of the two modes of the generative model, but the relative variances of the modes were learned much more slowly. Taken together, our results suggest that human performance in a more complex localization task, which requires the integration of sensory information with learned knowledge of a bimodal generative model, is consistent with the predictions of Bayes-optimal behavior, but involves a much longer time-course than in simpler tasks.

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.

Inferencing Processes After Right Hemisphere Brain Damage: Effects of Contextual Bias

PubMed Central

Blake, Margaret Lehman

2009-01-01

Purpose Comprehension deficits associated with right hemisphere brain damage (RHD) have been attributed to an inability to use context, but there is little direct evidence to support the claim. This study evaluated the effect of varying contextual bias on predictive inferencing by adults with RHD. Method Fourteen adults with no brain damage (NBD) and 14 with RHD read stories constructed with either high predictability or low predictability of a specific outcome. Reading time for a sentence that disconfirmed the target outcome was measured and compared with a control story context. Results Adults with RHD evidenced activation of predictive inferences only for highly predictive conditions, whereas NBD adults generated inferences in both high- and low-predictability stories. Adults with RHD were more likely than those with NBD to require additional time to integrate inferences in high-predictability conditions. The latter finding was related to working memory for the RHD group. Results are interpreted in light of previous findings obtained using the same stimuli. Conclusions RHD does not abolish the ability to use context. Evidence of predictive inferencing is influenced by task and strength of inference activation. Treatment considerations and cautions regarding interpreting results from one methodology are discussed. PMID:19252126

Moving beyond qualitative evaluations of Bayesian models of cognition.

PubMed

Hemmer, Pernille; Tauber, Sean; Steyvers, Mark

2015-06-01

Bayesian models of cognition provide a powerful way to understand the behavior and goals of individuals from a computational point of view. Much of the focus in the Bayesian cognitive modeling approach has been on qualitative model evaluations, where predictions from the models are compared to data that is often averaged over individuals. In many cognitive tasks, however, there are pervasive individual differences. We introduce an approach to directly infer individual differences related to subjective mental representations within the framework of Bayesian models of cognition. In this approach, Bayesian data analysis methods are used to estimate cognitive parameters and motivate the inference process within a Bayesian cognitive model. We illustrate this integrative Bayesian approach on a model of memory. We apply the model to behavioral data from a memory experiment involving the recall of heights of people. A cross-validation analysis shows that the Bayesian memory model with inferred subjective priors predicts withheld data better than a Bayesian model where the priors are based on environmental statistics. In addition, the model with inferred priors at the individual subject level led to the best overall generalization performance, suggesting that individual differences are important to consider in Bayesian models of cognition.

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

Mental Models of Invisible Logical Networks

NASA Technical Reports Server (NTRS)

Sanderson, P.

1984-01-01

Subjects were required to discover the structure of a logical network whose links were invisible. Network structure had to be inferred from the behavior of the components after a failure. It was hypothesized that since such failure diagnosis tasks often draw on spatial processes, a good deal of spatial complexity in the network should affect network discovery. Results show that the ability to discover the linkages in the network is directly related to the spatial complexity of the pathway described by the linkages. This effect was generally independent of the amount of evidence available to subjects about the existence of the link. These results raise the question of whether inferences about spatially complex pathways were simply not made, or whether they were made but not retained because of a high load on memory resources.

Modeling gene regulatory networks: A network simplification algorithm

NASA Astrophysics Data System (ADS)

Ferreira, Luiz Henrique O.; de Castro, Maria Clicia S.; da Silva, Fabricio A. B.

2016-12-01

Boolean networks have been used for some time to model Gene Regulatory Networks (GRNs), which describe cell functions. Those models can help biologists to make predictions, prognosis and even specialized treatment when some disturb on the GRN lead to a sick condition. However, the amount of information related to a GRN can be huge, making the task of inferring its boolean network representation quite a challenge. The method shown here takes into account information about the interactome to build a network, where each node represents a protein, and uses the entropy of each node as a key to reduce the size of the network, allowing the further inferring process to focus only on the main protein hubs, the ones with most potential to interfere in overall network behavior.

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

Symptomatology and social inference: a theory of mind study of schizophrenia and psychotic affective disorder.

PubMed

Marjoram, Dominic; Gardner, Clare; Burns, Jonathan; Miller, Patrick; Lawrie, Stephen M; Johnstone, Eve C

2005-11-01

There is evidence that certain patients with schizophrenia have deficits in theory of mind (ToM) capabilities. It is, however, unclear whether these are symptom or diagnosis-specific. A ToM hinting task was given to 15 patients with a DSM-IV diagnosis of schizophrenia, 15 patients with affective disorder and 15 healthy controls. Severity of the current psychopathology was measured using the Krawiecka standardised scale of psychotic symptoms (Krawiecka, Goldberg, & Vaughan, 1977); IQ was estimated via the Ammons and Ammons Quick Test (Ammons & Ammons, 1962). The group with schizophrenia performed significantly worse than the affective and control groups. Poor performance on the hinting task was found to be significantly related to the presence of positive symptoms (instead of negative ones) and specifically related to delusions and hallucinations. These findings remained when covariance for potentially confounding variables was applied. Individuals with high levels of delusions and hallucinations performed significantly worse on this ToM task, regardless of diagnosis, implying ToM impairment is not exclusive to schizophrenia but is evident in other forms of psychosis. Between-group analyses showed the schizophrenia group had a significantly poorer performance on this task than the others.

Toward an implicit measure of emotions: ratings of abstract images reveal distinct emotional states.

PubMed

Bartoszek, Gregory; Cervone, Daniel

2017-11-01

Although implicit tests of positive and negative affect exist, implicit measures of distinct emotional states are scarce. Three experiments examined whether a novel implicit emotion-assessment task, the rating of emotion expressed in abstract images, would reveal distinct emotional states. In Experiment 1, participants exposed to a sadness-inducing story inferred more sadness, and less happiness, in abstract images. In Experiment 2, an anger-provoking interaction increased anger ratings. In Experiment 3, compared to neutral images, spider images increased fear ratings in spider-fearful participants but not in controls. In each experiment, the implicit task indicated elevated levels of the target emotion and did not indicate elevated levels of non-target negative emotions; the task thus differentiated among emotional states of the same valence. Correlations also supported the convergent and discriminant validity of the implicit task. Supporting the possibility that heuristic processes underlie the ratings, group differences were stronger among those who responded relatively quickly.

Understanding of thought bubbles as mental representations in children with autism: implications for theory of mind.

PubMed

Kerr, Sharyn; Durkin, Kevin

2004-12-01

Standard false belief tasks indicate that normally developing children do not fully develop a theory of mind until the age of 4 years and that children with autism have an impaired theory of mind. Recent evidence, however, suggests that children as young as 3 years of age understand that thought bubbles depict mental representations and that these can be false. Twelve normally developing children and 11 children with autism were tested on a standard false belief task and a number of tasks that employed thought bubbles to represent mental states. While the majority of normally developing children and children with autism failed the standard false belief task, they understood that (i) thought bubbles represent thought, (ii) thought bubbles can be used to infer an unknown reality, (iii) thoughts can be different, and (iv) thoughts can be false. These results indicate that autistic children with a relatively low verbal mental age may be capable of understanding mental representations.

General and specialized brain correlates for analogical reasoning: A meta-analysis of functional imaging studies.

PubMed

Hobeika, Lucie; Diard-Detoeuf, Capucine; Garcin, Béatrice; Levy, Richard; Volle, Emmanuelle

2016-05-01

Reasoning by analogy allows us to link distinct domains of knowledge and to transfer solutions from one domain to another. Analogical reasoning has been studied using various tasks that have generally required the consideration of the relationships between objects and their integration to infer an analogy schema. However, these tasks varied in terms of the level and the nature of the relationships to consider (e.g., semantic, visuospatial). The aim of this study was to identify the cerebral network involved in analogical reasoning and its specialization based on the domains of information and task specificity. We conducted a coordinate-based meta-analysis of 27 experiments that used analogical reasoning tasks. The left rostrolateral prefrontal cortex was one of the regions most consistently activated across the studies. A comparison between semantic and visuospatial analogy tasks showed both domain-oriented regions in the inferior and middle frontal gyri and a domain-general region, the left rostrolateral prefrontal cortex, which was specialized for analogy tasks. A comparison of visuospatial analogy to matrix problem tasks revealed that these two relational reasoning tasks engage, at least in part, distinct right and left cerebral networks, particularly separate areas within the left rostrolateral prefrontal cortex. These findings highlight several cognitive and cerebral differences between relational reasoning tasks that can allow us to make predictions about the respective roles of distinct brain regions or networks. These results also provide new, testable anatomical hypotheses about reasoning disorders that are induced by brain damage. Hum Brain Mapp 37:1953-1969, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Learning and retention through predictive inference and classification.

PubMed

Sakamoto, Yasuaki; Love, Bradley C

2010-12-01

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 classification were compared for fifth-grade students using class-related materials. Making inferences about properties of category members and receiving feedback led to the acquisition of both queried (i.e., tested) properties and nonqueried properties that were correlated with a queried property (e.g., even if not queried, students learned about a species' habitat because it correlated with a queried property, like the species' size). In contrast, classifying items according to their species and receiving feedback led to knowledge of only the property most diagnostic of category membership. After multiple-day delay, the fifth-graders who learned through inference selectively retained information about the queried properties, and the fifth-graders who learned through classification retained information about the diagnostic property, indicating a role for explicit evaluation in establishing memories. Overall, inference learning resulted in fewer errors, better retention, and more liking of the categories than did classification learning. Experiment 2 revealed that querying a property only a few times was enough to manifest the full benefits of inference learning in undergraduate students. These results suggest that classroom teaching should emphasize reasoning from the category to multiple properties rather than from a set of properties to the category. (PsycINFO Database Record (c) 2010 APA, all rights reserved).

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…

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

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…

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).

An Intelligent Man-Machine Interface—Multi-Robot Control Adapted for Task Engagement Based on Single-Trial Detectability of P300

PubMed Central

Kirchner, Elsa A.; Kim, Su K.; Tabie, Marc; Wöhrle, Hendrik; Maurus, Michael; Kirchner, Frank

2016-01-01

Advanced man-machine interfaces (MMIs) are being developed for teleoperating robots at remote and hardly accessible places. Such MMIs make use of a virtual environment and can therefore make the operator immerse him-/herself into the environment of the robot. In this paper, we present our developed MMI for multi-robot control. Our MMI can adapt to changes in task load and task engagement online. Applying our approach of embedded Brain Reading we improve user support and efficiency of interaction. The level of task engagement was inferred from the single-trial detectability of P300-related brain activity that was naturally evoked during interaction. With our approach no secondary task is needed to measure task load. It is based on research results on the single-stimulus paradigm, distribution of brain resources and its effect on the P300 event-related component. It further considers effects of the modulation caused by a delayed reaction time on the P300 component evoked by complex responses to task-relevant messages. We prove our concept using single-trial based machine learning analysis, analysis of averaged event-related potentials and behavioral analysis. As main results we show (1) a significant improvement of runtime needed to perform the interaction tasks compared to a setting in which all subjects could easily perform the tasks. We show that (2) the single-trial detectability of the event-related potential P300 can be used to measure the changes in task load and task engagement during complex interaction while also being sensitive to the level of experience of the operator and (3) can be used to adapt the MMI individually to the different needs of users without increasing total workload. Our online adaptation of the proposed MMI is based on a continuous supervision of the operator's cognitive resources by means of embedded Brain Reading. Operators with different qualifications or capabilities receive only as many tasks as they can perform to avoid mental overload as well as mental underload. PMID:27445742

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.

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

Task-Related Edge Density (TED)—A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain

PubMed Central

Lohmann, Gabriele; Stelzer, Johannes; Zuber, Verena; Buschmann, Tilo; Margulies, Daniel; Bartels, Andreas; Scheffler, Klaus

2016-01-01

The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach “Task-related Edge Density” (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function. PMID:27341204

Task-Related Edge Density (TED)-A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain.

PubMed

Lohmann, Gabriele; Stelzer, Johannes; Zuber, Verena; Buschmann, Tilo; Margulies, Daniel; Bartels, Andreas; Scheffler, Klaus

2016-01-01

The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach "Task-related Edge Density" (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function.

Does the Component Processes Task Assess Text-Based Inferences Important for Reading Comprehension? A Path Analysis in Primary School Children

PubMed Central

Wassenburg, Stephanie I.; de Koning, Björn B.; de Vries, Meinou H.; van der Schoot, Menno

2016-01-01

Using a component processes task (CPT) that differentiates between higher-level cognitive processes of reading comprehension provides important advantages over commonly used general reading comprehension assessments. The present study contributes to further development of the CPT by evaluating the relative contributions of its components (text memory, text inferencing, and knowledge integration) and working memory to general reading comprehension within a single study using path analyses. Participants were 173 third- and fourth-grade children. As hypothesized, knowledge integration was the only component of the CPT that directly contributed to reading comprehension, indicating that the text-inferencing component did not assess inferential processes related to reading comprehension. Working memory was a significant predictor of reading comprehension over and above the component processes. Future research should focus on finding ways to ensure that the text-inferencing component taps into processes important for reading comprehension. PMID:27378989

Preschoolers’ Novel Noun Extensions: Shape in Spite of Knowing Better

PubMed Central

Saalbach, Henrik; Schalk, Lennart

2011-01-01

We examined the puzzling research findings that when extending novel nouns, preschoolers rely on shape similarity (rather than categorical relations) while in other task contexts (e.g., property induction) they rely on categorical relations. Taking into account research on children’s word learning, categorization, and inductive inference we assume that preschoolers have both a shape-based and a category-based word extension strategy available and can switch between these two depending on which information is easily available. To this end, we tested preschoolers on two versions of a novel-noun label extension task. First, we paralleled the standard extension task commonly used by previous research. In this case, as expected, preschoolers predominantly selected same-shape items. Second, we supported preschoolers’ retrieval of item-related information from memory by asking them simple questions about each item prior to the label extension task. Here, they switched to a category-based strategy, thus, predominantly selecting same-category items. Finally, we revealed that this shape-to-category shift is specific to the word learning context as we did not find it in a non-lexical classification task. These findings support our assumption that preschoolers’ decision about word extension change in accordance with the availability of information (from task context or by memory retrieval). We conclude by suggesting that preschoolers’ noun extensions can be conceptualized within the framework of heuristic decision-making. This provides an ecologically plausible processing account with respect to which information is selected and how this information is integrated to act as a guideline for decision-making when novel words have to be generalized. PMID:22073036

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…

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.

Database Search Engines: Paradigms, Challenges and Solutions.

PubMed

Verheggen, Kenneth; Martens, Lennart; Berven, Frode S; Barsnes, Harald; Vaudel, Marc

2016-01-01

The first step in identifying proteins from mass spectrometry based shotgun proteomics data is to infer peptides from tandem mass spectra, a task generally achieved using database search engines. In this chapter, the basic principles of database search engines are introduced with a focus on open source software, and the use of database search engines is demonstrated using the freely available SearchGUI interface. This chapter also discusses how to tackle general issues related to sequence database searching and shows how to minimize their impact.

Words are not enough: how preschoolers' integration of perspective and emotion informs their referential understanding.

PubMed

Graham, Susan A; San Juan, Valerie; Khu, Melanie

2017-05-01

When linguistic information alone does not clarify a speaker's intended meaning, skilled communicators can draw on a variety of cues to infer communicative intent. In this paper, we review research examining the developmental emergence of preschoolers' sensitivity to a communicative partner's perspective. We focus particularly on preschoolers' tendency to use cues both within the communicative context (i.e. a speaker's visual access to information) and within the speech signal itself (i.e. emotional prosody) to make on-line inferences about communicative intent. Our review demonstrates that preschoolers' ability to use visual and emotional cues of perspective to guide language interpretation is not uniform across tasks, is sometimes related to theory of mind and executive function skills, and, at certain points of development, is only revealed by implicit measures of language processing.

The effect of methylphenidate and rearing environment on behavioral inhibition in adult male rats.

PubMed

Hill, Jade C; Covarrubias, Pablo; Terry, Joel; Sanabria, Federico

2012-01-01

The ability to withhold reinforced responses-behavioral inhibition-is impaired in various psychiatric conditions including Attention Deficit Hyperactivity Disorder (ADHD). Methodological and analytical limitations have constrained our understanding of the effects of pharmacological and environmental factors on behavioral inhibition. To determine the effects of acute methylphenidate (MPH) administration and rearing conditions (isolated vs. pair-housed) on behavioral inhibition in adult rats. Inhibitory capacity was evaluated using two response-withholding tasks, differential reinforcement of low rates (DRL) and fixed minimum interval (FMI) schedules of reinforcement. Both tasks made sugar pellets contingent on intervals longer than 6 s between consecutive responses. Inferences on inhibitory and timing capacities were drawn from the distribution of withholding times (interresponse times, or IRTs). MPH increased the number of intervals produced in both tasks. Estimates of behavioral inhibition increased with MPH dose in FMI and with social isolation in DRL. Nonetheless, burst responding in DRL and the divergence of DRL data relative to past studies, among other limitations, undermined the reliability of DRL data as the basis for inferences on behavioral inhibition. Inhibitory capacity was more precisely estimated from FMI than from DRL performance. Based on FMI data, MPH, but not a socially enriched environment, appears to improve inhibitory capacity. The highest dose of MPH tested, 8 mg/kg, did not reduce inhibitory capacity but reduced the responsiveness to waiting contingencies. These results support the use of the FMI schedule, complemented with appropriate analytic techniques, for the assessment of behavioral inhibition in animal models.

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…

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.

Functional neuroanatomy of intuitive physical inference

PubMed Central

Mikhael, John G.; Tenenbaum, Joshua B.; Kanwisher, Nancy

2016-01-01

To engage with the world—to understand the scene in front of us, plan actions, and predict what will happen next—we must have an intuitive grasp of the world’s physical structure and dynamics. How do the objects in front of us rest on and support each other, how much force would be required to move them, and how will they behave when they fall, roll, or collide? Despite the centrality of physical inferences in daily life, little is known about the brain mechanisms recruited to interpret the physical structure of a scene and predict how physical events will unfold. Here, in a series of fMRI experiments, we identified a set of cortical regions that are selectively engaged when people watch and predict the unfolding of physical events—a “physics engine” in the brain. These brain regions are selective to physical inferences relative to nonphysical but otherwise highly similar scenes and tasks. However, these regions are not exclusively engaged in physical inferences per se or, indeed, even in scene understanding; they overlap with the domain-general “multiple demand” system, especially the parts of that system involved in action planning and tool use, pointing to a close relationship between the cognitive and neural mechanisms involved in parsing the physical content of a scene and preparing an appropriate action. PMID:27503892

Reflective Random Indexing and indirect inference: a scalable method for discovery of implicit connections.

PubMed

Cohen, Trevor; Schvaneveldt, Roger; Widdows, Dominic

2010-04-01

The discovery of implicit connections between terms that do not occur together in any scientific document underlies the model of literature-based knowledge discovery first proposed by Swanson. Corpus-derived statistical models of semantic distance such as Latent Semantic Analysis (LSA) have been evaluated previously as methods for the discovery of such implicit connections. However, LSA in particular is dependent on a computationally demanding method of dimension reduction as a means to obtain meaningful indirect inference, limiting its ability to scale to large text corpora. In this paper, we evaluate the ability of Random Indexing (RI), a scalable distributional model of word associations, to draw meaningful implicit relationships between terms in general and biomedical language. Proponents of this method have achieved comparable performance to LSA on several cognitive tasks while using a simpler and less computationally demanding method of dimension reduction than LSA employs. In this paper, we demonstrate that the original implementation of RI is ineffective at inferring meaningful indirect connections, and evaluate Reflective Random Indexing (RRI), an iterative variant of the method that is better able to perform indirect inference. RRI is shown to lead to more clearly related indirect connections and to outperform existing RI implementations in the prediction of future direct co-occurrence in the MEDLINE corpus. 2009 Elsevier Inc. All rights reserved.

Functional neuroanatomy of intuitive physical inference.

PubMed

Fischer, Jason; Mikhael, John G; Tenenbaum, Joshua B; Kanwisher, Nancy

2016-08-23

To engage with the world-to understand the scene in front of us, plan actions, and predict what will happen next-we must have an intuitive grasp of the world's physical structure and dynamics. How do the objects in front of us rest on and support each other, how much force would be required to move them, and how will they behave when they fall, roll, or collide? Despite the centrality of physical inferences in daily life, little is known about the brain mechanisms recruited to interpret the physical structure of a scene and predict how physical events will unfold. Here, in a series of fMRI experiments, we identified a set of cortical regions that are selectively engaged when people watch and predict the unfolding of physical events-a "physics engine" in the brain. These brain regions are selective to physical inferences relative to nonphysical but otherwise highly similar scenes and tasks. However, these regions are not exclusively engaged in physical inferences per se or, indeed, even in scene understanding; they overlap with the domain-general "multiple demand" system, especially the parts of that system involved in action planning and tool use, pointing to a close relationship between the cognitive and neural mechanisms involved in parsing the physical content of a scene and preparing an appropriate action.

Enhanced visual processing contributes to matrix reasoning in autism

PubMed Central

Soulières, Isabelle; Dawson, Michelle; Samson, Fabienne; Barbeau, Elise B.; Sahyoun, Cherif; Strangman, Gary E.; Zeffiro, Thomas A.; Mottron, Laurent

2009-01-01

Recent behavioral investigations have revealed that autistics perform more proficiently on Raven's Standard Progressive Matrices (RSPM) than would be predicted by their Wechsler intelligence scores. A widely-used test of fluid reasoning and intelligence, the RSPM assays abilities to flexibly infer rules, manage goal hierarchies, and perform high-level abstractions. The neural substrates for these abilities are known to encompass a large frontoparietal network, with different processing models placing variable emphasis on the specific roles of the prefrontal or posterior regions. We used functional magnetic resonance imaging to explore the neural bases of autistics' RSPM problem solving. Fifteen autistic and eighteen non-autistic participants, matched on age, sex, manual preference and Wechsler IQ, completed 60 self-paced randomly-ordered RSPM items along with a visually similar 60-item pattern matching comparison task. Accuracy and response times did not differ between groups in the pattern matching task. In the RSPM task, autistics performed with similar accuracy, but with shorter response times, compared to their non-autistic controls. In both the entire sample and a subsample of participants additionally matched on RSPM performance to control for potential response time confounds, neural activity was similar in both groups for the pattern matching task. However, for the RSPM task, autistics displayed relatively increased task-related activity in extrastriate areas (BA18), and decreased activity in the lateral prefrontal cortex (BA9) and the medial posterior parietal cortex (BA7). Visual processing mechanisms may therefore play a more prominent role in reasoning in autistics. PMID:19530215

A Test of Transitive Inferences in Free-Flying Honeybees: Unsuccessful Performance Due to Memory Constraints

ERIC Educational Resources Information Center

Benard, Julie; Giurfa, Martin

2004-01-01

We asked whether honeybees, "Apis mellifera," could solve a transitive inference problem. Individual free-flying bees were conditioned with four overlapping premise pairs of five visual patterns in a multiple discrimination task (A+ vs. B-, B+ vs. C-, C+ vs. D-, D+ vs. E-, where + and - indicate sucrose reward or absence of it,…

Transitive Inference of Social Dominance by Human Infants

ERIC Educational Resources Information Center

Gazes, Regina Paxton; Hampton, Robert R.; Lourenco, Stella F.

2017-01-01

It is surprising that there are inconsistent findings of transitive inference (TI) in young infants given that non-linguistic species succeed on TI tests. To conclusively test for TI in infants, we developed a task within the social domain, with which infants are known to show sophistication. We familiarized 10- to 13-month-olds (M = 11.53 months)…

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

Spatial language and converseness.

PubMed

Burigo, Michele; Coventry, Kenny R; Cangelosi, Angelo; Lynott, Dermot

2016-12-01

Typical spatial language sentences consist of describing the location of an object (the located object) in relation to another object (the reference object) as in "The book is above the vase". While it has been suggested that the properties of the located object (the book) are not translated into language because they are irrelevant when exchanging location information, it has been shown that the orientation of the located object affects the production and comprehension of spatial descriptions. In line with the claim that spatial language apprehension involves inferences about relations that hold between objects it has been suggested that during spatial language apprehension people use the orientation of the located object to evaluate whether the logical property of converseness (e.g., if "the book is above the vase" is true, then also "the vase is below the book" must be true) holds across the objects' spatial relation. In three experiments using sentence acceptability rating tasks we tested this hypothesis and demonstrated that when converseness is violated people's acceptability ratings of a scene's description are reduced indicating that people do take into account geometric properties of the located object and use it to infer logical spatial relations.

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.

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

Whatever Gave You That Idea? False Memories Following Equivalence Training: A Behavioral Account of the Misinformation Effect

PubMed Central

Challies, Danna M; Hunt, Maree; Garry, Maryanne; Harper, David N

2011-01-01

The misinformation effect is a term used in the cognitive psychological literature to describe both experimental and real-world instances in which misleading information is incorporated into an account of an historical event. In many real-world situations, it is not possible to identify a distinct source of misinformation, and it appears that the witness may have inferred a false memory by integrating information from a variety of sources. In a stimulus equivalence task, a small number of trained relations between some members of a class of arbitrary stimuli result in a large number of untrained, or emergent relations, between all members of the class. Misleading information was introduced into a simple memory task between a learning phase and a recognition test by means of a match-to-sample stimulus equivalence task that included both stimuli from the original learning task and novel stimuli. At the recognition test, participants given equivalence training were more likely to misidentify patterns than those who were not given such training. The misinformation effect was distinct from the effects of prior stimulus exposure, or partial stimulus control. In summary, stimulus equivalence processes may underlie some real-world manifestations of the misinformation effect. PMID:22084495

Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining

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

Hero, Alfred O.; Rajaratnam, Bala

When can reliable inference be drawn in the ‘‘Big Data’’ context? This article presents a framework for answering this fundamental question in the context of correlation mining, with implications for general large-scale inference. In large-scale data applications like genomics, connectomics, and eco-informatics, the data set is often variable rich but sample starved: a regime where the number n of acquired samples (statistical replicates) is far fewer than the 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, hasmore » received relatively less attention, especially in the setting when the sample size n is fixed, and the dimension p grows without bound. To address this 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 the variable dimension is fixed and the sample size goes to infinity; 2) the mixed asymptotic regime where both variable dimension and sample size go to infinity at comparable rates; and 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-scale 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 that 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.« less

Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining

PubMed Central

Hero, Alfred O.; Rajaratnam, Bala

2015-01-01

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. PMID:27087700

Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining

DOE PAGES

Hero, Alfred O.; Rajaratnam, Bala

2015-12-09

When can reliable inference be drawn in the ‘‘Big Data’’ context? This article presents a framework for answering this fundamental question in the context of correlation mining, with implications for general large-scale inference. In large-scale data applications like genomics, connectomics, and eco-informatics, the data set is often variable rich but sample starved: a regime where the number n of acquired samples (statistical replicates) is far fewer than the 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, hasmore » received relatively less attention, especially in the setting when the sample size n is fixed, and the dimension p grows without bound. To address this 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 the variable dimension is fixed and the sample size goes to infinity; 2) the mixed asymptotic regime where both variable dimension and sample size go to infinity at comparable rates; and 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-scale 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 that 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.« less

On how the brain decodes vocal cues about speaker confidence.

PubMed

Jiang, Xiaoming; Pell, Marc D

2015-05-01

In speech communication, listeners must accurately decode vocal cues that refer to the speaker's mental state, such as their confidence or 'feeling of knowing'. However, the time course and neural mechanisms associated with online inferences about speaker confidence are unclear. Here, we used event-related potentials (ERPs) to examine the temporal neural dynamics underlying a listener's ability to infer speaker confidence from vocal cues during speech processing. We recorded listeners' real-time brain responses while they evaluated statements wherein the speaker's tone of voice conveyed one of three levels of confidence (confident, close-to-confident, unconfident) or were spoken in a neutral manner. Neural responses time-locked to event onset show that the perceived level of speaker confidence could be differentiated at distinct time points during speech processing: unconfident expressions elicited a weaker P2 than all other expressions of confidence (or neutral-intending utterances), whereas close-to-confident expressions elicited a reduced negative response in the 330-500 msec and 550-740 msec time window. Neutral-intending expressions, which were also perceived as relatively confident, elicited a more delayed, larger sustained positivity than all other expressions in the 980-1270 msec window for this task. These findings provide the first piece of evidence of how quickly the brain responds to vocal cues signifying the extent of a speaker's confidence during online speech comprehension; first, a rough dissociation between unconfident and confident voices occurs as early as 200 msec after speech onset. At a later stage, further differentiation of the exact level of speaker confidence (i.e., close-to-confident, very confident) is evaluated via an inferential system to determine the speaker's meaning under current task settings. These findings extend three-stage models of how vocal emotion cues are processed in speech comprehension (e.g., Schirmer & Kotz, 2006) by revealing how a speaker's mental state (i.e., feeling of knowing) is simultaneously inferred from vocal expressions. Copyright © 2015 Elsevier Ltd. All rights reserved.

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…

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.

A developmental study of risky decisions on the cake gambling task: age and gender analyses of probability estimation and reward evaluation.

PubMed

Van Leijenhorst, Linda; Westenberg, P Michiel; Crone, Eveline A

2008-01-01

Decision making, or the process of choosing between competing courses of actions, is highly sensitive to age-related change, showing development throughout adolescence. In this study, we tested whether the development of decision making under risk is related to changes in risk-estimation abilities. Participants (N = 93) between ages 8-30 performed a child friendly gambling task, the Cake Gambling task, which was inspired by the Cambridge Gambling Task (Rogers et al., 1999), which has previously been shown to be sensitive to orbitofrontal cortex (OFC) damage. The task allowed comparisons of the contributions to risk perception of (1) the ability to estimate probabilities and (2) evaluate rewards. Adult performance patterns were highly similar to those found in previous reports, showing increased risk taking with increases in the probability of winning and the magnitude of potential reward. Behavioral patterns in children and adolescents did not differ from adult patterns, showing a similar ability for probability estimation and reward evaluation. These data suggest that participants 8 years and older perform like adults in a gambling task, previously shown to depend on the OFC in which all the information needed to make an advantageous decision is given on each trial and no information needs to be inferred from previous behavior. Interestingly, at all ages, females were more risk-averse than males. These results suggest that the increase in real-life risky behavior that is seen in adolescence is not a consequence of changes in risk perception abilities. The findings are discussed in relation to theories about the protracted development of the prefrontal cortex.

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

Fast half-sibling population reconstruction: theory and algorithms.

PubMed

Dexter, Daniel; Brown, Daniel G

2013-07-12

Kinship inference is the task of identifying genealogically related individuals. Kinship information is important for determining mating structures, notably in endangered populations. Although many solutions exist for reconstructing full sibling relationships, few exist for half-siblings. We consider the problem of determining whether a proposed half-sibling population reconstruction is valid under Mendelian inheritance assumptions. We show that this problem is NP-complete and provide a 0/1 integer program that identifies the minimum number of individuals that must be removed from a population in order for the reconstruction to become valid. We also present SibJoin, a heuristic-based clustering approach based on Mendelian genetics, which is strikingly fast. The software is available at http://github.com/ddexter/SibJoin.git+. Our SibJoin algorithm is reasonably accurate and thousands of times faster than existing algorithms. The heuristic is used to infer a half-sibling structure for a population which was, until recently, too large to evaluate.

Subspecialization in the human posterior medial cortex

PubMed Central

Bzdok, Danilo; Heeger, Adrian; Langner, Robert; Laird, Angela R.; Fox, Peter T.; Palomero-Gallagher, Nicola; Vogt, Brent A.; Zilles, Karl; Eickhoff, Simon B.

2014-01-01

The posterior medial cortex (PMC) is particularly poorly understood. Its neural activity changes have been related to highly disparate mental processes. We therefore investigated PMC properties with a data-driven exploratory approach. First, we subdivided the PMC by whole-brain coactivation profiles. Second, functional connectivity of the ensuing PMC regions was compared by task-constrained meta-analytic coactivation mapping (MACM) and task-unconstrained resting-state correlations (RSFC). Third, PMC regions were functionally described by forward/reverse functional inference. A precuneal cluster was mostly connected to the intraparietal sulcus, frontal eye fields, and right temporo-parietal junction; associated with attention and motor tasks. A ventral posterior cingulate cortex (PCC) cluster was mostly connected to the ventromedial prefrontal cortex and middle left inferior parietal cortex (IPC); associated with facial appraisal and language tasks. A dorsal PCC cluster was mostly connected to the dorsomedial prefrontal cortex, anterior/posterior IPC, posterior midcingulate cortex, and left dorsolateral prefrontal cortex; associated with delay discounting. A cluster in the retrosplenial cortex was mostly connected to the anterior thalamus and hippocampus. Furthermore, all PMC clusters were congruently coupled with the default mode network according to task-constrained but not task-unconstrained connectivity. We thus identified distinct regions in the PMC and characterized their neural networks and functional implications. PMID:25462801

Interfering with memory for faces: The cost of doing two things at once.

PubMed

Wammes, Jeffrey D; Fernandes, Myra A

2016-01-01

We inferred the processes critical for episodic retrieval of faces by measuring susceptibility to memory interference from different distracting tasks. Experiment 1 examined recognition of studied faces under full attention (FA) or each of two divided attention (DA) conditions requiring concurrent decisions to auditorily presented letters. Memory was disrupted in both DA relative to FA conditions, a result contrary to a material-specific account of interference effects. Experiment 2 investigated whether the magnitude of interference depended on competition between concurrent tasks for common processing resources. Studied faces were presented either upright (configurally processed) or inverted (featurally processed). Recognition was completed under FA, or DA with one of two face-based distracting tasks requiring either featural or configural processing. We found an interaction: memory for upright faces was lower under DA when the distracting task required configural than featural processing, while the reverse was true for memory of inverted faces. Across experiments, the magnitude of memory interference was similar (a 19% or 20% decline from FA) regardless of whether the materials in the distracting task overlapped with the to-be-remembered information. Importantly, interference was significantly larger (42%) when the processing demands of the distracting and target retrieval task overlapped, suggesting a processing-specific account of memory interference.

Humanoid infers Archimedes' principle: understanding physical relations and object affordances through cumulative learning experiences

PubMed Central

2016-01-01

Emerging studies indicate that several species such as corvids, apes and children solve ‘The Crow and the Pitcher’ task (from Aesop's Fables) in diverse conditions. Hidden beneath this fascinating paradigm is a fundamental question: by cumulatively interacting with different objects, how can an agent abstract the underlying cause–effect relations to predict and creatively exploit potential affordances of novel objects in the context of sought goals? Re-enacting this Aesop's Fable task on a humanoid within an open-ended ‘learning–prediction–abstraction’ loop, we address this problem and (i) present a brain-guided neural framework that emulates rapid one-shot encoding of ongoing experiences into a long-term memory and (ii) propose four task-agnostic learning rules (elimination, growth, uncertainty and status quo) that correlate predictions from remembered past experiences with the unfolding present situation to gradually abstract the underlying causal relations. Driven by the proposed architecture, the ensuing robot behaviours illustrated causal learning and anticipation similar to natural agents. Results further demonstrate that by cumulatively interacting with few objects, the predictions of the robot in case of novel objects converge close to the physical law, i.e. the Archimedes principle: this being independent of both the objects explored during learning and the order of their cumulative exploration. PMID:27466440

Humanoid infers Archimedes' principle: understanding physical relations and object affordances through cumulative learning experiences.

PubMed

Bhat, Ajaz Ahmad; Mohan, Vishwanathan; Sandini, Giulio; Morasso, Pietro

2016-07-01

Emerging studies indicate that several species such as corvids, apes and children solve 'The Crow and the Pitcher' task (from Aesop's Fables) in diverse conditions. Hidden beneath this fascinating paradigm is a fundamental question: by cumulatively interacting with different objects, how can an agent abstract the underlying cause-effect relations to predict and creatively exploit potential affordances of novel objects in the context of sought goals? Re-enacting this Aesop's Fable task on a humanoid within an open-ended 'learning-prediction-abstraction' loop, we address this problem and (i) present a brain-guided neural framework that emulates rapid one-shot encoding of ongoing experiences into a long-term memory and (ii) propose four task-agnostic learning rules (elimination, growth, uncertainty and status quo) that correlate predictions from remembered past experiences with the unfolding present situation to gradually abstract the underlying causal relations. Driven by the proposed architecture, the ensuing robot behaviours illustrated causal learning and anticipation similar to natural agents. Results further demonstrate that by cumulatively interacting with few objects, the predictions of the robot in case of novel objects converge close to the physical law, i.e. the Archimedes principle: this being independent of both the objects explored during learning and the order of their cumulative exploration. © 2016 The Author(s).

The Sense of Agency during Continuous Action: Performance Is More Important than Action-Feedback Association

PubMed Central

Wen, Wen; Yamashita, Atsushi; Asama, Hajime

2015-01-01

The sense of agency refers to the feeling that one is controlling events through one’s own behavior. This study examined how task performance and the delay of events influence one’s sense of agency during continuous action accompanied by a goal. The participants were instructed to direct a moving dot into a square as quickly as possible by pressing the left and right keys on a keyboard to control the direction in which the dot traveled. The interval between the key press and response of the dot (i.e., direction change) was manipulated to vary task difficulty. Moreover, in the assisted condition, the computer ignored participants’ erroneous commands, resulting in improved task performance but a weaker association between the participants’ commands and actual movements of the dot relative to the condition in which all of the participants’ commands were executed (i.e., self-control condition). The results showed that participants’ sense of agency increased with better performance in the assisted condition relative to the self-control condition, even though a large proportion of their commands were not executed. We concluded that, when the action-feedback association was uncertain, cognitive inference was more dominant relative to the process of comparing predicted and perceived information in the judgment of agency. PMID:25893992

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

A Comparative Study of Average, Linked Mastoid, and REST References for ERP Components Acquired during fMRI

PubMed Central

Yang, Ping; Fan, Chenggui; Wang, Min; Li, Ling

2017-01-01

In simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) studies, average reference (AR), and digitally linked mastoid (LM) are popular re-referencing techniques in event-related potential (ERP) analyses. However, they may introduce their own physiological signals and alter the EEG/ERP outcome. A reference electrode standardization technique (REST) that calculated a reference point at infinity was proposed to solve this problem. To confirm the advantage of REST in ERP analyses of synchronous EEG-fMRI studies, we compared the reference effect of AR, LM, and REST on task-related ERP results of a working memory task during an fMRI scan. As we hypothesized, we found that the adopted reference did not change the topography map of ERP components (N1 and P300 in the present study), but it did alter the task-related effect on ERP components. LM decreased or eliminated the visual working memory (VWM) load effect on P300, and the AR distorted the distribution of VWM location-related effect at left posterior electrodes as shown in the statistical parametric scalp mapping (SPSM) of N1. ERP cortical source estimates, which are independent of the EEG reference choice, were used as the golden standard to infer the relative utility of different references on the ERP task-related effect. By comparison, REST reference provided a more integrated and reasonable result. These results were further confirmed by the results of fMRI activations and a corresponding EEG-only study. Thus, we recommend the REST, especially with a realistic head model, as the optimal reference method for ERP data analysis in simultaneous EEG-fMRI studies. PMID:28529472

A Comparative Study of Average, Linked Mastoid, and REST References for ERP Components Acquired during fMRI.

PubMed

Yang, Ping; Fan, Chenggui; Wang, Min; Li, Ling

2017-01-01

In simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) studies, average reference (AR), and digitally linked mastoid (LM) are popular re-referencing techniques in event-related potential (ERP) analyses. However, they may introduce their own physiological signals and alter the EEG/ERP outcome. A reference electrode standardization technique (REST) that calculated a reference point at infinity was proposed to solve this problem. To confirm the advantage of REST in ERP analyses of synchronous EEG-fMRI studies, we compared the reference effect of AR, LM, and REST on task-related ERP results of a working memory task during an fMRI scan. As we hypothesized, we found that the adopted reference did not change the topography map of ERP components (N1 and P300 in the present study), but it did alter the task-related effect on ERP components. LM decreased or eliminated the visual working memory (VWM) load effect on P300, and the AR distorted the distribution of VWM location-related effect at left posterior electrodes as shown in the statistical parametric scalp mapping (SPSM) of N1. ERP cortical source estimates, which are independent of the EEG reference choice, were used as the golden standard to infer the relative utility of different references on the ERP task-related effect. By comparison, REST reference provided a more integrated and reasonable result. These results were further confirmed by the results of fMRI activations and a corresponding EEG-only study. Thus, we recommend the REST, especially with a realistic head model, as the optimal reference method for ERP data analysis in simultaneous EEG-fMRI studies.

Effect of posttraumatic stress on study time in a task measuring four component processes underlying text-level reading.

PubMed

Sullivan, Michael P; Griffiths, Gina G; Moore Sohlberg, Mckay

2014-10-01

To investigate the effect of combat-related posttraumatic stress disorder (PTSD) on 4 components underlying text-level reading comprehension. A group of 17 veterans with PTSD and 17 matched control participants took part. An experimental task required participants to read and study 3-sentence paragraphs describing semantic features associated with real and unreal objects. Each paragraph was followed by true-false statements that assessed knowledge access, text memory, inference, and integration. The results revealed that the PTSD group took significantly longer than the control group to study the paragraphs. Although there was no group difference in test statement accuracy, the PTSD group also took significantly longer to respond to the test statements. Overall, the results provide evidence for the control theory of attention but suggest that more direct measures of task-irrelevant processing during text-level reading are needed. More important, the results begin to lay a foundation for developing not only diagnostic but also intervention strategies.

Transfer learning for visual categorization: a survey.

PubMed

Shao, Ling; Zhu, Fan; Li, Xuelong

2015-05-01

Regular machine learning and data mining techniques study the training data for future inferences under a major assumption that the future data are within the same feature space or have the same distribution as the training data. However, due to the limited availability of human labeled training data, training data that stay in the same feature space or have the same distribution as the future data cannot be guaranteed to be sufficient enough to avoid the over-fitting problem. In real-world applications, apart from data in the target domain, related data in a different domain can also be included to expand the availability of our prior knowledge about the target future data. Transfer learning addresses such cross-domain learning problems by extracting useful information from data in a related domain and transferring them for being used in target tasks. In recent years, with transfer learning being applied to visual categorization, some typical problems, e.g., view divergence in action recognition tasks and concept drifting in image classification tasks, can be efficiently solved. In this paper, we survey state-of-the-art transfer learning algorithms in visual categorization applications, such as object recognition, image classification, and human action recognition.

Working Memory and Processing Efficiency in Children's Reasoning.

ERIC Educational Resources Information Center

Halford, Graeme S.; And Others

A series of studies was conducted to determine whether children's reasoning is capacity-limited and whether any such capacity, if it exists, is based on the working memory system. An N-term series (transitive inference) was used as the primary task in an interference paradigm. A concurrent short-term memory load was employed as the secondary task.…

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…

P values are only an index to evidence: 20th- vs. 21st-century statistical science.

PubMed

Burnham, K P; Anderson, D R

2014-03-01

Early statistical methods focused on pre-data probability statements (i.e., data as random variables) such as P values; these are not really inferences nor are P values evidential. Statistical science clung to these principles throughout much of the 20th century as a wide variety of methods were developed for special cases. Looking back, it is clear that the underlying paradigm (i.e., testing and P values) was weak. As Kuhn (1970) suggests, new paradigms have taken the place of earlier ones: this is a goal of good science. New methods have been developed and older methods extended and these allow proper measures of strength of evidence and multimodel inference. It is time to move forward with sound theory and practice for the difficult practical problems that lie ahead. Given data the useful foundation shifts to post-data probability statements such as model probabilities (Akaike weights) or related quantities such as odds ratios and likelihood intervals. These new methods allow formal inference from multiple models in the a prior set. These quantities are properly evidential. The past century was aimed at finding the "best" model and making inferences from it. The goal in the 21st century is to base inference on all the models weighted by their model probabilities (model averaging). Estimates of precision can include model selection uncertainty leading to variances conditional on the model set. The 21st century will be about the quantification of information, proper measures of evidence, and multi-model inference. Nelder (1999:261) concludes, "The most important task before us in developing statistical science is to demolish the P-value culture, which has taken root to a frightening extent in many areas of both pure and applied science and technology".

Subcortical roles in lexical task processing: Inferences from thalamic and subthalamic event-related potentials.

PubMed

Tiedt, Hannes O; Ehlen, Felicitas; Krugel, Lea K; Horn, Andreas; Kühn, Andrea A; Klostermann, Fabian

2017-01-01

Subcortical functions for language capacities are poorly defined, but may be investigated in the context of deep brain stimulation. Here, we studied event-related potentials recorded from electrodes in the subthalamic nucleus (STN) and the thalamic ventral intermediate nucleus (VIM) together with surface-EEG. Participants completed a lexical decision task (LDT), which required the differentiation of acoustically presented words from pseudo-words by button press. Target stimuli were preceded by prime-words. In recordings from VIM, a slow potential shift apparent at the lower electrode contacts persisted during target stimulus presentation (equally for words and pseudo-words). In contrast, recordings from STN electrodes showed a short local activation on prime-words but not target-stimuli. In both depth-recording regions, further components related to contralateral motor responses to target words were evident. On scalp level, mid-central activations on (pseudo)lexical stimuli were obtained, in line with the expression of N400 potentials. The prolonged activity recorded from VIM, exclusively accompanying the relevant LDT phase, is in line with the idea of thalamic "selective engagement" for supporting the realization of the behavioral focus demanded by the task. In contrast, the phasic prime related activity rather indicates "procedural" STN functions, for example, for trial sequencing or readiness inhibition of prepared target reactions. Hum Brain Mapp 38:370-383, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

EEG measures reveal dual-task interference in postural performance in young adults

PubMed Central

Woollacott, Marjorie

2014-01-01

The study used a dual-task (DT) postural paradigm (two tasks performed at once) that included electroencephalography (EEG) to examine cortical interference when a visual working memory (VWM) task was paired with a postural task. The change detection task was used, as it requires storage of information without updating or manipulation and predicts VWM capacity. Ground reaction forces (GRFs) (horizontal and vertical), EMG, and EEG elements, time locked to support surface perturbations, were used to infer the active neural processes underlying the automatic control of balance in 14 young adults. A significant reduction was seen between single task (ST) and DT conditions in VWM capacity (F(1,13) = 6.175, p < 0.05, r = 0.6) and event-related potential (ERP) N1 component amplitude over the L motor (p < 0.001) and R sensory (p < 0.05) cortical areas. In addition, a significant increase in the COP trajectory peak (pkcopx) was seen in the DT versus ST condition. Modulation of VWM capacity as well as ERP amplitude and pkcopx in DT conditions provided evidence of an interference pattern, suggesting that the two modalities shared a similar set of attentional resources. The results provide direct evidence of the competition for central processing attentional resources between the two modalities, through the reduction in amplitude of the ERP evoked by the postural perturbation. PMID:25273924

Interfering with free recall of words: Detrimental effects of phonological competition.

PubMed

Fernandes, Myra A; Wammes, Jeffrey D; Priselac, Sandra; Moscovitch, Morris

2016-09-01

We examined the effect of different distracting tasks, performed concurrently during memory retrieval, on recall of a list of words. By manipulating the type of material and processing (semantic, orthographic, and phonological) required in the distracting task, and comparing the magnitude of memory interference produced, we aimed to infer the kind of representation upon which retrieval of words depends. In Experiment 1, identifying odd digits concurrently during free recall disrupted memory, relative to a full attention condition, when the numbers were presented orthographically (e.g. nineteen), but not numerically (e.g. 19). In Experiment 2, a distracting task that required phonological-based decisions to either word or picture material produced large, but equivalent effects on recall of words. In Experiment 3, phonological-based decisions to pictures in a distracting task disrupted recall more than when the same pictures required semantically-based size estimations. In Experiment 4, a distracting task that required syllable decisions to line drawings interfered significantly with recall, while an equally difficult semantically-based color-decision task about the same line drawings, did not. Together, these experiments demonstrate that the degree of memory interference experienced during recall of words depends primarily on whether the distracting task competes for phonological representations or processes, and less on competition for semantic or orthographic or material-specific representations or processes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Transitive inference in two lemur species (Eulemur macaco and Eulemur fulvus).

PubMed

Tromp, D; Meunier, H; Roeder, J J

2015-03-01

When confronted with tasks involving reasoning instead of simple learning through trial and error, lemurs appeared to be less competent than simians. Our study aims to investigate lemurs' capability for transitive inference, a form of deductive reasoning in which the subject deduces logical conclusions from preliminary information. Transitive inference may have an adaptative function, especially in species living in large, complex social groups and is proposed to play a major role in rank estimation and establishment of dominance hierarchies. We proposed to test the capacities of reasoning using transitive inference in two species of lemurs, the brown lemur (Eulemur fulvus) and the black lemur (Eulemur macaco), both living in multimale-multifemale societies. For that purpose, we designed an original setup providing, for the first time in this kind of cognitive task, pictures of conspecifics' faces as stimuli. Subjects were trained to differentiate six photographs of unknown conspecifics named randomly from A to F to establish the order A > B > C > D > E > F and select consistently the highest-ranking photograph in five adjacent pairs AB, BC, CD, DE, and EF. Then lemurs were presented with the same adjacent pairs and three new and non-adjacent pairs BD, BE, CE. The results showed that all subjects correctly selected the highest-ranking photograph in every non-adjacent pair, reflecting lemurs' capacity for transitive inference. Our results are discussed in the context of the still debated current theories about the mechanisms underlying this specific capacity. © 2014 Wiley Periodicals, Inc.

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).

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.

Stimulus discriminability may bias value-based probabilistic learning.

PubMed

Schutte, Iris; Slagter, Heleen A; Collins, Anne G E; Frank, Michael J; Kenemans, J Leon

2017-01-01

Reinforcement learning tasks are often used to assess participants' tendency to learn more from the positive or more from the negative consequences of one's action. However, this assessment often requires comparison in learning performance across different task conditions, which may differ in the relative salience or discriminability of the stimuli associated with more and less rewarding outcomes, respectively. To address this issue, in a first set of studies, participants were subjected to two versions of a common probabilistic learning task. The two versions differed with respect to the stimulus (Hiragana) characters associated with reward probability. The assignment of character to reward probability was fixed within version but reversed between versions. We found that performance was highly influenced by task version, which could be explained by the relative perceptual discriminability of characters assigned to high or low reward probabilities, as assessed by a separate discrimination experiment. Participants were more reliable in selecting rewarding characters that were more discriminable, leading to differences in learning curves and their sensitivity to reward probability. This difference in experienced reinforcement history was accompanied by performance biases in a test phase assessing ability to learn from positive vs. negative outcomes. In a subsequent large-scale web-based experiment, this impact of task version on learning and test measures was replicated and extended. Collectively, these findings imply a key role for perceptual factors in guiding reward learning and underscore the need to control stimulus discriminability when making inferences about individual differences in reinforcement learning.

Causal Inference for fMRI Time Series Data with Systematic Errors of Measurement in a Balanced On/Off Study of Social Evaluative Threat.

PubMed

Sobel, Michael E; Lindquist, Martin A

2014-07-01

Functional magnetic resonance imaging (fMRI) has facilitated major advances in understanding human brain function. Neuroscientists are interested in using fMRI to study the effects of external stimuli on brain activity and causal relationships among brain regions, but have not stated what is meant by causation or defined the effects they purport to estimate. Building on Rubin's causal model, we construct a framework for causal inference using blood oxygenation level dependent (BOLD) fMRI time series data. In the usual statistical literature on causal inference, potential outcomes, assumed to be measured without systematic error, are used to define unit and average causal effects. However, in general the potential BOLD responses are measured with stimulus dependent systematic error. Thus we define unit and average causal effects that are free of systematic error. In contrast to the usual case of a randomized experiment where adjustment for intermediate outcomes leads to biased estimates of treatment effects (Rosenbaum, 1984), here the failure to adjust for task dependent systematic error leads to biased estimates. We therefore adjust for systematic error using measured "noise covariates" , using a linear mixed model to estimate the effects and the systematic error. Our results are important for neuroscientists, who typically do not adjust for systematic error. They should also prove useful to researchers in other areas where responses are measured with error and in fields where large amounts of data are collected on relatively few subjects. To illustrate our approach, we re-analyze data from a social evaluative threat task, comparing the findings with results that ignore systematic error.

[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.

Metacognition and abstract reasoning.

PubMed

Markovits, Henry; Thompson, Valerie A; Brisson, Janie

2015-05-01

The nature of people's meta-representations of deductive reasoning is critical to understanding how people control their own reasoning processes. We conducted two studies to examine whether people have a metacognitive representation of abstract validity and whether familiarity alone acts as a separate metacognitive cue. In Study 1, participants were asked to make a series of (1) abstract conditional inferences, (2) concrete conditional inferences with premises having many potential alternative antecedents and thus specifically conducive to the production of responses consistent with conditional logic, or (3) concrete problems with premises having relatively few potential alternative antecedents. Participants gave confidence ratings after each inference. Results show that confidence ratings were positively correlated with logical performance on abstract problems and concrete problems with many potential alternatives, but not with concrete problems with content less conducive to normative responses. Confidence ratings were higher with few alternatives than for abstract content. Study 2 used a generation of contrary-to-fact alternatives task to improve levels of abstract logical performance. The resulting increase in logical performance was mirrored by increases in mean confidence ratings. Results provide evidence for a metacognitive representation based on logical validity, and show that familiarity acts as a separate metacognitive cue.

QuEST for malware type-classification

NASA Astrophysics Data System (ADS)

Vaughan, Sandra L.; Mills, Robert F.; Grimaila, Michael R.; Peterson, Gilbert L.; Oxley, Mark E.; Dube, Thomas E.; Rogers, Steven K.

2015-05-01

Current cyber-related security and safety risks are unprecedented, due in no small part to information overload and skilled cyber-analyst shortages. Advances in decision support and Situation Awareness (SA) tools are required to support analysts in risk mitigation. Inspired by human intelligence, research in Artificial Intelligence (AI) and Computational Intelligence (CI) have provided successful engineering solutions in complex domains including cyber. Current AI approaches aggregate large volumes of data to infer the general from the particular, i.e. inductive reasoning (pattern-matching) and generally cannot infer answers not previously programmed. Whereas humans, rarely able to reason over large volumes of data, have successfully reached the top of the food chain by inferring situations from partial or even partially incorrect information, i.e. abductive reasoning (pattern-completion); generating a hypothetical explanation of observations. In order to achieve an engineering advantage in computational decision support and SA we leverage recent research in human consciousness, the role consciousness plays in decision making, modeling the units of subjective experience which generate consciousness, qualia. This paper introduces a novel computational implementation of a Cognitive Modeling Architecture (CMA) which incorporates concepts of consciousness. We apply our model to the malware type-classification task. The underlying methodology and theories are generalizable to many domains.

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.

Temporal Annotation in the Clinical Domain

PubMed Central

Styler, William F.; Bethard, Steven; Finan, Sean; Palmer, Martha; Pradhan, Sameer; de Groen, Piet C; Erickson, Brad; Miller, Timothy; Lin, Chen; Savova, Guergana; Pustejovsky, James

2014-01-01

This article discusses the requirements of a formal specification for the annotation of temporal information in clinical narratives. We discuss the implementation and extension of ISO-TimeML for annotating a corpus of clinical notes, known as the THYME corpus. To reflect the information task and the heavily inference-based reasoning demands in the domain, a new annotation guideline has been developed, “the THYME Guidelines to ISO-TimeML (THYME-TimeML)”. To clarify what relations merit annotation, we distinguish between linguistically-derived and inferentially-derived temporal orderings in the text. We also apply a top performing TempEval 2013 system against this new resource to measure the difficulty of adapting systems to the clinical domain. The corpus is available to the community and has been proposed for use in a SemEval 2015 task. PMID:29082229

Neural basis of attributional style in schizophrenia.

PubMed

Park, Kyung-Min; Kim, Jae-Jin; Ku, Jeonghun; Kim, So Young; Lee, Hyeong Rae; Kim, Sun I; Yoon, Kang-Jun

2009-07-31

Attributional style means how people typically infer the causes of emotional behaviors. No study has shown neural basis of attributional style in schizophrenia, although it was suggested as a major area of social cognition research of schizophrenia. Fifteen patients with schizophrenia and 16 healthy controls underwent functional magnetic resonance imaging while performing three (happy, angry, and neutral) conditions of attribution task. Each condition included inferring situational causes of an avatar' (virtual character) emotional or neutral behavior. In the between-groups contrast maps of the happy conditions, the patient group compared to the control group showed decreased activations in the inferior frontal (BA 44) and the ventral premotor cortex (BA 6), in which the % signal changes were associated with negative symptoms. In the angry conditions, the patient group compared to the control group exhibited increased activations in the precuneus/posterior cingulate cortex (Pcu/PCC) (BA 7/31), in which the % signal changes were related to positive symptoms. In conclusion, patients with schizophrenia may have functional deficits in mirror neuron system when attributing positive behaviors, which may be related to a lack of inner simulation and empathy and negative symptoms. In contrast, patients may have increased activation in the Pcu/PCC related to self-representations while attributing negative behaviors, which may be related to failures in self- and source-monitoring and positive symptoms.

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…

Revised associative inference paradigm confirms relational memory impairment in schizophrenia

PubMed Central

Armstrong, Kristan; Williams, Lisa E.; Heckers, Stephan

2013-01-01

Objective Patients with schizophrenia have widespread cognitive impairments, with selective deficits in relational memory. We previously reported a differential relational memory deficit in schizophrenia using the Associative Inference Paradigm (AIP), a task suggested by the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) initiative to examine relational memory. However, the AIP had limited feasibility for testing in schizophrenia due to high attrition of schizophrenia patients during training. Here we developed and tested a revised version of the AIP to improve feasibility. Method 30 healthy control and 37 schizophrenia subjects received 3 study-test sessions on 3 sets of paired associates: H-F1 (house paired with face), H-F2 (same house paired with new face), and F3-F4 (two novel faces). After training, subjects were tested on the trained, non-inferential Face-Face pairs (F3-F4) and novel, inferential Face-Face pairs (F1-F2), constructed from the faces of the trained House-Face pairs. Results Schizophrenia patients were significantly more impaired on the inferential F1-F2 pairs than the non-inferential F3-F4 pairs, providing evidence for a differential relational memory deficit. Only 8 percent of schizophrenia patients were excluded from testing due to poor training performance. Conclusions The revised AIP confirmed the previous finding of a relational memory deficit in a larger and more representative sample of schizophrenia patients. PMID:22612578

Revised associative inference paradigm confirms relational memory impairment in schizophrenia.

PubMed

Armstrong, Kristan; Williams, Lisa E; Heckers, Stephan

2012-07-01

Patients with schizophrenia have widespread cognitive impairments, with selective deficits in relational memory. We previously reported a differential relational memory deficit in schizophrenia using the Associative Inference Paradigm (AIP), a task suggested by the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) initiative to examine relational memory. However, the AIP had limited feasibility for testing in schizophrenia because of high attrition of schizophrenia patients during training. Here we developed and tested a revised version of the AIP to improve feasibility. 30 healthy control and 37 schizophrenia subjects received 3 study-test sessions on 3 sets of paired associates: H-F1 (house paired with face), H-F2 (same house paired with new face), and F3-F4 (two novel faces). After training, subjects were tested on the trained, noninferential Face-Face pairs (F3-F4) and novel, inferential Face-Face pairs (F1-F2), constructed from the faces of the trained House-Face pairs. Schizophrenia patients were significantly more impaired on the inferential F1-F2 pairs than the noninferential F3-F4 pairs, providing evidence for a differential relational memory deficit. Only 8% of schizophrenia patients were excluded from testing because of poor training performance. The revised AIP confirmed the previous finding of a relational memory deficit in a larger and more representative sample of schizophrenia patients.

Measuring and modeling the interaction among reward size, delay to reward, and satiation level on motivation in monkeys.

PubMed

Minamimoto, Takafumi; La Camera, Giancarlo; Richmond, Barry J

2009-01-01

Motivation is usually inferred from the likelihood or the intensity with which behavior is carried out. It is sensitive to external factors (e.g., the identity, amount, and timing of a rewarding outcome) and internal factors (e.g., hunger or thirst). We trained macaque monkeys to perform a nonchoice instrumental task (a sequential red-green color discrimination) while manipulating two external factors: reward size and delay-to-reward. We also inferred the state of one internal factor, level of satiation, by monitoring the accumulated reward. A visual cue indicated the forthcoming reward size and delay-to-reward in each trial. The fraction of trials completed correctly by the monkeys increased linearly with reward size and was hyperbolically discounted by delay-to-reward duration, relations that are similar to those found in free operant and choice tasks. The fraction of correct trials also decreased progressively as a function of the satiation level. Similar (albeit noiser) relations were obtained for reaction times. The combined effect of reward size, delay-to-reward, and satiation level on the proportion of correct trials is well described as a multiplication of the effects of the single factors when each factor is examined alone. These results provide a quantitative account of the interaction of external and internal factors on instrumental behavior, and allow us to extend the concept of subjective value of a rewarding outcome, usually confined to external factors, to account also for slow changes in the internal drive of the subject.

Measuring and Modeling the Interaction Among Reward Size, Delay to Reward, and Satiation Level on Motivation in Monkeys

PubMed Central

Minamimoto, Takafumi; La Camera, Giancarlo; Richmond, Barry J.

2009-01-01

Motivation is usually inferred from the likelihood or the intensity with which behavior is carried out. It is sensitive to external factors (e.g., the identity, amount, and timing of a rewarding outcome) and internal factors (e.g., hunger or thirst). We trained macaque monkeys to perform a nonchoice instrumental task (a sequential red-green color discrimination) while manipulating two external factors: reward size and delay-to-reward. We also inferred the state of one internal factor, level of satiation, by monitoring the accumulated reward. A visual cue indicated the forthcoming reward size and delay-to-reward in each trial. The fraction of trials completed correctly by the monkeys increased linearly with reward size and was hyperbolically discounted by delay-to-reward duration, relations that are similar to those found in free operant and choice tasks. The fraction of correct trials also decreased progressively as a function of the satiation level. Similar (albeit noiser) relations were obtained for reaction times. The combined effect of reward size, delay-to-reward, and satiation level on the proportion of correct trials is well described as a multiplication of the effects of the single factors when each factor is examined alone. These results provide a quantitative account of the interaction of external and internal factors on instrumental behavior, and allow us to extend the concept of subjective value of a rewarding outcome, usually confined to external factors, to account also for slow changes in the internal drive of the subject. PMID:18987119

Prevalence of work-related musculoskeletal injuries among South Indian hand screen-printing workers.

PubMed

Shankar, S; Naveen Kumar, R; Mohankumar, P; Jayaraman, Srinivasan

2017-01-01

Hand screen-printing (HSP) plays a predominant role in textile industries in developing countries. Workers from HSP industry were mostly affected by musculoskeletal injury due to monotonous, and prolonged work nature and poor workplace environment. The present study aims to investigate the prevalence of work-related musculoskeletal disorder (MSD) symptoms and risk factors associated among the HSP industry workers. Cochran's sample size for categorical data was used to select 385 HSP workers of 1000 samples from various provinces of Tamil Nadu, INDIA. Modified Nordic based questionnaire was used to assess the musculoskeletal injuries and risk factors among HSP workers. The statistical analysis revealed that 62.5% workers are prone to MSD symptoms with lower back (75.1%), shoulder (66.2%), knees (58.7%), and ankle/feet (55.6%). Age, experience, marital status, stress in the job were the risk factors which significantly (p < 0.05) associated with the reported MSDs. Further, this study result infers that the subjects with higher age and experience are exposed higher levels of MSD prevalence of 85.5% and 92.0% respectively in past 12 months than other groups. Among the different work categories in HSP task, the workers reported with the maximum discomfort during printing work (63.1%) with Odds ratio as 10.38 and 95% CI is 6.18-17.4. than the material handling and drying task. Study results infer that HSP workers are prone to lower back and shoulder pain followed by knees and ankle feet regions. Socio-demographic factors, awkward posture and repetitive movements contribute to cause MSD among hand screen-printing workers.

Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment.

PubMed

Lee, Wei-Po; Hsiao, Yu-Ting; Hwang, Wei-Che

2014-01-16

To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks. This work presents a practical framework to infer large gene networks, by developing and parallelizing a hybrid GA-PSO optimization method. Our parallel method is extended to work with the Hadoop MapReduce programming model and is executed in different cloud computing environments. To evaluate the proposed approach, we use a well-known open-source software GeneNetWeaver to create several yeast S. cerevisiae sub-networks and use them to produce gene profiles. Experiments have been conducted and the results have been analyzed. They show that our parallel approach can be successfully used to infer networks with desired behaviors and the computation time can be largely reduced. Parallel population-based algorithms can effectively determine network parameters and they perform better than the widely-used sequential algorithms in gene network inference. These parallel algorithms can be distributed to the cloud computing environment to speed up the computation. By coupling the parallel model population-based optimization method and the parallel computational framework, high quality solutions can be obtained within relatively short time. This integrated approach is a promising way for inferring large networks.

Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment

PubMed Central

2014-01-01

Background To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks. Results This work presents a practical framework to infer large gene networks, by developing and parallelizing a hybrid GA-PSO optimization method. Our parallel method is extended to work with the Hadoop MapReduce programming model and is executed in different cloud computing environments. To evaluate the proposed approach, we use a well-known open-source software GeneNetWeaver to create several yeast S. cerevisiae sub-networks and use them to produce gene profiles. Experiments have been conducted and the results have been analyzed. They show that our parallel approach can be successfully used to infer networks with desired behaviors and the computation time can be largely reduced. Conclusions Parallel population-based algorithms can effectively determine network parameters and they perform better than the widely-used sequential algorithms in gene network inference. These parallel algorithms can be distributed to the cloud computing environment to speed up the computation. By coupling the parallel model population-based optimization method and the parallel computational framework, high quality solutions can be obtained within relatively short time. This integrated approach is a promising way for inferring large networks. PMID:24428926

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

Do detour tasks provide accurate assays of inhibitory control?

PubMed Central

Whiteside, Mark A.; Laker, Philippa R.; Beardsworth, Christine E.

2018-01-01

Transparent Cylinder and Barrier tasks are used to purportedly assess inhibitory control in a variety of animals. However, we suspect that performances on these detour tasks are influenced by non-cognitive traits, which may result in inaccurate assays of inhibitory control. We therefore reared pheasants under standardized conditions and presented each bird with two sets of similar tasks commonly used to measure inhibitory control. We recorded the number of times subjects incorrectly attempted to access a reward through transparent barriers, and their latencies to solve each task. Such measures are commonly used to infer the differential expression of inhibitory control. We found little evidence that their performances were consistent across the two different Putative Inhibitory Control Tasks (PICTs). Improvements in performance across trials showed that pheasants learned the affordances of each specific task. Critically, prior experience of transparent tasks, either Barrier or Cylinder, also improved subsequent inhibitory control performance on a novel task, suggesting that they also learned the general properties of transparent obstacles. Individual measures of persistence, assayed in a third task, were positively related to their frequency of incorrect attempts to solve the transparent inhibitory control tasks. Neophobia, Sex and Body Condition had no influence on individual performance. Contrary to previous studies of primates, pheasants with poor performance on PICTs had a wider dietary breadth assayed using a free-choice task. Our results demonstrate that in systems or taxa where prior experience and differences in development cannot be accounted for, individual differences in performance on commonly used detour-dependent PICTS may reveal more about an individual's prior experience of transparent objects, or their motivation to acquire food, than providing a reliable measure of their inhibitory control. PMID:29593115

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…

Error analyses reveal contrasting deficits in "theory of mind": neuropsychological evidence from a 3-option false belief task.

PubMed

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

2007-06-18

Perspective taking is a crucial ability that guides our social interactions. In this study, we show how the specific patterns of errors of brain-damaged patients in perspective taking tasks can help us further understand the factors contributing to perspective taking abilities. Previous work [e.g., Samson, D., Apperly, I. A., Chiavarino, C., & Humphreys, G. W. (2004). Left temporoparietal junction is necessary for representing someone else's belief. Nature Neuroscience, 7, 499-500; Samson, D., Apperly, I. A., Kathirgamanathan, U., & Humphreys, G. W. (2005). Seeing it my way: A case of a selective deficit in inhibiting self-perspective. Brain, 128, 1102-1111] distinguished two components of perspective taking: the ability to inhibit our own perspective and the ability to infer someone else's perspective. We assessed these components using a new nonverbal false belief task which provided different response options to detect three types of response strategies that participants might be using: a complete and spared belief reasoning strategy, a reality-based response selection strategy in which participants respond from their own perspective, and a simplified mentalising strategy in which participants avoid responding from their own perspective but rely on inaccurate cues to infer the other person's belief. One patient, with a self-perspective inhibition deficit, almost always used the reality-based response strategy; in contrast, the other patient, with a deficit in taking other perspectives, tended to use the simplified mentalising strategy without necessarily transposing her own perspective. We discuss the extent to which the pattern of performance of both patients could relate to their executive function deficit and how it can inform us on the cognitive and neural components involved in belief reasoning.

Age-Related Brain Activation Changes during Rule Repetition in Word-Matching.

PubMed

Methqal, Ikram; Pinsard, Basile; Amiri, Mahnoush; Wilson, Maximiliano A; Monchi, Oury; Provost, Jean-Sebastien; Joanette, Yves

2017-01-01

Objective: The purpose of this study was to explore the age-related brain activation changes during a word-matching semantic-category-based task, which required either repeating or changing a semantic rule to be applied. In order to do so, a word-semantic rule-based task was adapted from the Wisconsin Sorting Card Test, involving the repeated feedback-driven selection of given pairs of words based on semantic category-based criteria. Method: Forty healthy adults (20 younger and 20 older) performed a word-matching task while undergoing a fMRI scan in which they were required to pair a target word with another word from a group of three words. The required pairing is based on three word-pair semantic rules which correspond to different levels of semantic control demands: functional relatedness, moderately typical-relatedness (which were considered as low control demands), and atypical-relatedness (high control demands). The sorting period consisted of a continuous execution of the same sorting rule and an inferred trial-by-trial feedback was given. Results: Behavioral performance revealed increases in response times and decreases of correct responses according to the level of semantic control demands (functional vs. typical vs. atypical) for both age groups (younger and older) reflecting graded differences in the repetition of the application of a given semantic rule. Neuroimaging findings of significant brain activation showed two main results: (1) Greater task-related activation changes for the repetition of the application of atypical rules relative to typical and functional rules, and (2) Changes (older > younger) in the inferior prefrontal regions for functional rules and more extensive and bilateral activations for typical and atypical rules. Regarding the inter-semantic rules comparison, only task-related activation differences were observed for functional > typical (e.g., inferior parietal and temporal regions bilaterally) and atypical > typical (e.g., prefrontal, inferior parietal, posterior temporal, and subcortical regions). Conclusion: These results suggest that healthy cognitive aging relies on the adaptive changes of inferior prefrontal resources involved in the repetitive execution of semantic rules, thus reflecting graded differences in support of task demands.

Structural encoding processes contribute to individual differences in face and object cognition: Inferences from psychometric test performance and event-related brain potentials.

PubMed

Nowparast Rostami, Hadiseh; Sommer, Werner; Zhou, Changsong; Wilhelm, Oliver; Hildebrandt, Andrea

2017-10-01

The enhanced N1 component in event-related potentials (ERP) to face stimuli, termed N170, is considered to indicate the structural encoding of faces. Previously, individual differences in the latency of the N170 have been related to face and object cognition abilities. By orthogonally manipulating content domain (faces vs objects) and task demands (easy/speed vs difficult/accuracy) in both psychometric and EEG tasks, we investigated the uniqueness of the processes underlying face cognition as compared with object cognition and the extent to which the N1/N170 component can explain individual differences in face and object cognition abilities. Data were recorded from N = 198 healthy young adults. Structural equation modeling (SEM) confirmed that the accuracies of face perception (FP) and memory are specific abilities above general object cognition; in contrast, the speed of face processing was not differentiable from the speed of object cognition. Although there was considerable domain-general variance in the N170 shared with the N1, there was significant face-specific variance in the N170. The brain-behavior relationship showed that faster face-specific processes for structural encoding of faces are associated with higher accuracy in both perceiving and memorizing faces. Moreover, in difficult task conditions, qualitatively different processes are additionally needed for recognizing face and object stimuli as compared with easy tasks. The difficulty-dependent variance components in the N170 amplitude were related with both face and object memory (OM) performance. We discuss implications for understanding individual differences in face cognition. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.'

What's skill got to do with it? Vehicle automation and driver mental workload.

PubMed

Young, M S; Stanton, N A

2007-08-01

Previous research has found that vehicle automation systems can reduce driver mental workload, with implications for attentional resources that can be detrimental to performance. The present paper considers how the development of automaticity within the driving task may influence performance in underload situations. Driver skill and vehicle automation were manipulated in a driving simulator, with four levels of each variable. Mental workload was assessed using a secondary task measure and eye movements were recorded to infer attentional capacity. The effects of automation on driver mental workload were quite robust across skill levels, but the most intriguing findings were from the eye movement data. It was found that, with little exception, attentional capacity and mental workload were directly related at all levels of driver skill, consistent with earlier studies. The results are discussed with reference to applied theories of cognition and the design of automation.

Sensitivity to value-driven attention is predicted by how we learn from value.

PubMed

Jahfari, Sara; Theeuwes, Jan

2017-04-01

Reward learning is known to influence the automatic capture of attention. This study examined how the rate of learning, after high- or low-value reward outcomes, can influence future transfers into value-driven attentional capture. Participants performed an instrumental learning task that was directly followed by an attentional capture task. A hierarchical Bayesian reinforcement model was used to infer individual differences in learning from high or low reward. Results showed a strong relationship between high-reward learning rates (or the weight that is put on learning after a high reward) and the magnitude of attentional capture with high-reward colors. Individual differences in learning from high or low rewards were further related to performance differences when high- or low-value distractors were present. These findings provide novel insight into the development of value-driven attentional capture by showing how information updating after desired or undesired outcomes can influence future deployments of automatic attention.

Don't Fear Optimality: Sampling for Probabilistic-Logic Sequence Models

NASA Astrophysics Data System (ADS)

Thon, Ingo

One of the current challenges in artificial intelligence is modeling dynamic environments that change due to the actions or activities undertaken by people or agents. The task of inferring hidden states, e.g. the activities or intentions of people, based on observations is called filtering. Standard probabilistic models such as Dynamic Bayesian Networks are able to solve this task efficiently using approximative methods such as particle filters. However, these models do not support logical or relational representations. The key contribution of this paper is the upgrade of a particle filter algorithm for use with a probabilistic logical representation through the definition of a proposal distribution. The performance of the algorithm depends largely on how well this distribution fits the target distribution. We adopt the idea of logical compilation into Binary Decision Diagrams for sampling. This allows us to use the optimal proposal distribution which is normally prohibitively slow.

Geometry of the Gene Expression Space of Individual Cells

PubMed Central

Korem, Yael; Szekely, Pablo; Hart, Yuval; Sheftel, Hila; Hausser, Jean; Mayo, Avi; Rothenberg, Michael E.; Kalisky, Tomer; Alon, Uri

2015-01-01

There is a revolution in the ability to analyze gene expression of single cells in a tissue. To understand this data we must comprehend how cells are distributed in a high-dimensional gene expression space. One open question is whether cell types form discrete clusters or whether gene expression forms a continuum of states. If such a continuum exists, what is its geometry? Recent theory on evolutionary trade-offs suggests that cells that need to perform multiple tasks are arranged in a polygon or polyhedron (line, triangle, tetrahedron and so on, generally called polytopes) in gene expression space, whose vertices are the expression profiles optimal for each task. Here, we analyze single-cell data from human and mouse tissues profiled using a variety of single-cell technologies. We fit the data to shapes with different numbers of vertices, compute their statistical significance, and infer their tasks. We find cases in which single cells fill out a continuum of expression states within a polyhedron. This occurs in intestinal progenitor cells, which fill out a tetrahedron in gene expression space. The four vertices of this tetrahedron are each enriched with genes for a specific task related to stemness and early differentiation. A polyhedral continuum of states is also found in spleen dendritic cells, known to perform multiple immune tasks: cells fill out a tetrahedron whose vertices correspond to key tasks related to maturation, pathogen sensing and communication with lymphocytes. A mixture of continuum-like distributions and discrete clusters is found in other cell types, including bone marrow and differentiated intestinal crypt cells. This approach can be used to understand the geometry and biological tasks of a wide range of single-cell datasets. The present results suggest that the concept of cell type may be expanded. In addition to discreet clusters in gene-expression space, we suggest a new possibility: a continuum of states within a polyhedron, in which the vertices represent specialists at key tasks. PMID:26161936

Social cognition in schizophrenia: cognitive and affective factors.

PubMed

Ziv, Ido; Leiser, David; Levine, Joseph

2011-01-01

Social cognition refers to how people conceive, perceive, and draw inferences about mental and emotional states of others in the social world. Previous studies suggest that the concept of social cognition involves several abilities, including those related to affect and cognition. The present study analyses the deficits of individuals with schizophrenia in two areas of social cognition: Theory of Mind (ToM) and emotion recognition and processing. Examining the impairment of these abilities in patients with schizophrenia has the potential to elucidate the neurophysiological regions involved in social cognition and may also have the potential to aid rehabilitation. Two experiments were conducted. Both included the same five tasks: first- and second-level false-belief ToM tasks, emotion inferencing, understanding of irony, and matrix reasoning (a WAIS-R subtest). The matrix reasoning task was administered to evaluate and control for the association of the other tasks with analytic reasoning skills. Experiment 1 involved factor analysis of the task performance of 75 healthy participants. Experiment 2 compared 30 patients with schizophrenia to an equal number of matched controls. Results. (1) The five tasks were clearly divided into two factors corresponding to the two areas of social cognition, ToM and emotion recognition and processing. (2) Schizophrenics' performance was impaired on all tasks, particularly on those loading heavily on the analytic component (matrix reasoning and second-order ToM). (3) Matrix reasoning, second-level ToM (ToM2), and irony were found to distinguish patients from controls, even when all other tasks that revealed significant impairment in the patients' performance were taken into account. The two areas of social cognition examined are related to distinct factors. The mechanism for answering ToM questions (especially ToM2) depends on analytic reasoning capabilities, but the difficulties they present to individuals with schizophrenia are due to other components as well. The impairment in social cognition in schizophrenia stems from deficiencies in several mechanisms, including the ability to think analytically and to process emotion information and cues.

Children and adults integrate talker and verb information in online processing.

PubMed Central

Borovsky, Arielle; Creel, Sarah

2015-01-01

Children seem able to efficiently interpret a variety of linguistic cues during speech comprehension, yet have difficulty interpreting sources of non-linguistic and paralinguistic information that accompany speech. The current study asked whether (paralinguistic) voice-activated role knowledge is rapidly interpreted in coordination with a linguistic cue (a sentential action) during speech comprehension in an eye-tracked sentence comprehension task with children (aged 3-10) and college-aged adults. Participants were initially familiarized with two talkers who identified their respective roles (e.g. PRINCESS and PIRATE) before hearing a previously-introduced talker name an action and object (“I want to hold the sword,” in the pirate's voice). As the sentence was spoken, eye-movements were recorded to four objects that varied in relationship to the sentential talker and action (Target: SWORD, Talker-Related: SHIP, Action-Related: WAND, and Unrelated: CARRIAGE). The task was to select the named image. Even young child listeners rapidly combined inferences about talker identity with the action, allowing them to fixate on the Target before it was mentioned, although there were developmental and vocabulary differences on this task. Results suggest that children, like adults, store real-world knowledge of a talker's role and actively use this information to interpret speech. PMID:24611671

Reverse Engineering Cellular Networks with Information Theoretic Methods

PubMed Central

Villaverde, Alejandro F.; Ross, John; Banga, Julio R.

2013-01-01

Building mathematical models of cellular networks lies at the core of systems biology. It involves, among other tasks, the reconstruction of the structure of interactions between molecular components, which is known as network inference or reverse engineering. Information theory can help in the goal of extracting as much information as possible from the available data. A large number of methods founded on these concepts have been proposed in the literature, not only in biology journals, but in a wide range of areas. Their critical comparison is difficult due to the different focuses and the adoption of different terminologies. Here we attempt to review some of the existing information theoretic methodologies for network inference, and clarify their differences. While some of these methods have achieved notable success, many challenges remain, among which we can mention dealing with incomplete measurements, noisy data, counterintuitive behaviour emerging from nonlinear relations or feedback loops, and computational burden of dealing with large data sets. PMID:24709703

Gauging Variational Inference

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

Chertkov, Michael; Ahn, Sungsoo; Shin, Jinwoo

Computing partition function is the most important statistical inference task arising in applications of Graphical Models (GM). Since it is computationally intractable, approximate methods have been used to resolve the issue in practice, where meanfield (MF) and belief propagation (BP) are arguably the most popular and successful approaches of a variational type. In this paper, we propose two new variational schemes, coined Gauged-MF (G-MF) and Gauged-BP (G-BP), improving MF and BP, respectively. Both provide lower bounds for the partition function by utilizing the so-called gauge transformation which modifies factors of GM while keeping the partition function invariant. Moreover, we provemore » that both G-MF and G-BP are exact for GMs with a single loop of a special structure, even though the bare MF and BP perform badly in this case. Our extensive experiments, on complete GMs of relatively small size and on large GM (up-to 300 variables) confirm that the newly proposed algorithms outperform and generalize MF and BP.« less

Mapping annotations with textual evidence using an scLDA model.

PubMed

Jin, Bo; Chen, Vicky; Chen, Lujia; Lu, Xinghua

2011-01-01

Most of the knowledge regarding genes and proteins is stored in biomedical literature as free text. Extracting information from complex biomedical texts demands techniques capable of inferring biological concepts from local text regions and mapping them to controlled vocabularies. To this end, we present a sentence-based correspondence latent Dirichlet allocation (scLDA) model which, when trained with a corpus of PubMed documents with known GO annotations, performs the following tasks: 1) learning major biological concepts from the corpus, 2) inferring the biological concepts existing within text regions (sentences), and 3) identifying the text regions in a document that provides evidence for the observed annotations. When applied to new gene-related documents, a trained scLDA model is capable of predicting GO annotations and identifying text regions as textual evidence supporting the predicted annotations. This study uses GO annotation data as a testbed; the approach can be generalized to other annotated data, such as MeSH and MEDLINE documents.

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.

3-D model-based Bayesian classification

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

Soenneland, L.; Tenneboe, P.; Gehrmann, T.

1994-12-31

The challenging task of the interpreter is to integrate different pieces of information and combine them into an earth model. The sophistication level of this earth model might vary from the simplest geometrical description to the most complex set of reservoir parameters related to the geometrical description. Obviously the sophistication level also depend on the completeness of the available information. The authors describe the interpreter`s task as a mapping between the observation space and the model space. The information available to the interpreter exists in observation space and the task is to infer a model in model-space. It is well-knownmore » that this inversion problem is non-unique. Therefore any attempt to find a solution depend son constraints being added in some manner. The solution will obviously depend on which constraints are introduced and it would be desirable to allow the interpreter to modify the constraints in a problem-dependent manner. They will present a probabilistic framework that gives the interpreter the tools to integrate the different types of information and produce constrained solutions. The constraints can be adapted to the problem at hand.« less

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.

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

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.

Neural mechanisms underlying valence inferences to sound: The role of the right angular gyrus.

PubMed

Bravo, Fernando; Cross, Ian; Hawkins, Sarah; Gonzalez, Nadia; Docampo, Jorge; Bruno, Claudio; Stamatakis, Emmanuel Andreas

2017-07-28

We frequently infer others' intentions based on non-verbal auditory cues. Although the brain underpinnings of social cognition have been extensively studied, no empirical work has yet examined the impact of musical structure manipulation on the neural processing of emotional valence during mental state inferences. We used a novel sound-based theory-of-mind paradigm in which participants categorized stimuli of different sensory dissonance level in terms of positive/negative valence. Whilst consistent with previous studies which propose facilitated encoding of consonances, our results demonstrated that distinct levels of consonance/dissonance elicited differential influences on the right angular gyrus, an area implicated in mental state attribution and attention reorienting processes. Functional and effective connectivity analyses further showed that consonances modulated a specific inhibitory interaction from associative memory to mental state attribution substrates. Following evidence suggesting that individuals with autism may process social affective cues differently, we assessed the relationship between participants' task performance and self-reported autistic traits in clinically typical adults. Higher scores on the social cognition scales of the AQ were associated with deficits in recognising positive valence in consonant sound cues. These findings are discussed with respect to Bayesian perspectives on autistic perception, which highlight a functional failure to optimize precision in relation to prior beliefs. Copyright © 2017 Elsevier Ltd. All rights reserved.

Adaptive decision making in a dynamic environment: a test of a sequential sampling model of relative judgment.

PubMed

Vuckovic, Anita; Kwantes, Peter J; Neal, Andrew

2013-09-01

Research has identified a wide range of factors that influence performance in relative judgment tasks. However, the findings from this research have been inconsistent. Studies have varied with respect to the identification of causal variables and the perceptual and decision-making mechanisms underlying performance. Drawing on the ecological rationality approach, we present a theory of the judgment and decision-making processes involved in a relative judgment task that explains how people judge a stimulus and adapt their decision process to accommodate their own uncertainty associated with those judgments. Undergraduate participants performed a simulated air traffic control conflict detection task. Across two experiments, we systematically manipulated variables known to affect performance. In the first experiment, we manipulated the relative distances of aircraft to a common destination while holding aircraft speeds constant. In a follow-up experiment, we introduced a direct manipulation of relative speed. We then fit a sequential sampling model to the data, and used the best fitting parameters to infer the decision-making processes responsible for performance. Findings were consistent with the theory that people adapt to their own uncertainty by adjusting their criterion and the amount of time they take to collect evidence in order to make a more accurate decision. From a practical perspective, the paper demonstrates that one can use a sequential sampling model to understand performance in a dynamic environment, allowing one to make sense of and interpret complex patterns of empirical findings that would otherwise be difficult to interpret using standard statistical analyses. PsycINFO Database Record (c) 2013 APA, all rights reserved.

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

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 effects of selective and divided attention on sensory precision and integration.

PubMed

Odegaard, Brian; Wozny, David R; Shams, Ladan

2016-02-12

In our daily lives, our capacity to selectively attend to stimuli within or across sensory modalities enables enhanced perception of the surrounding world. While previous research on selective attention has studied this phenomenon extensively, two important questions still remain unanswered: (1) how selective attention to a single modality impacts sensory integration processes, and (2) the mechanism by which selective attention improves perception. We explored how selective attention impacts performance in both a spatial task and a temporal numerosity judgment task, and employed a Bayesian Causal Inference model to investigate the computational mechanism(s) impacted by selective attention. We report three findings: (1) in the spatial domain, selective attention improves precision of the visual sensory representations (which were relatively precise), but not the auditory sensory representations (which were fairly noisy); (2) in the temporal domain, selective attention improves the sensory precision in both modalities (both of which were fairly reliable to begin with); (3) in both tasks, selective attention did not exert a significant influence over the tendency to integrate sensory stimuli. Therefore, it may be postulated that a sensory modality must possess a certain inherent degree of encoding precision in order to benefit from selective attention. It also appears that in certain basic perceptual tasks, the tendency to integrate crossmodal signals does not depend significantly on selective attention. We conclude with a discussion of how these results relate to recent theoretical considerations of selective attention. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A neurocomputational system for relational reasoning.

PubMed

Knowlton, Barbara J; Morrison, Robert G; Hummel, John E; Holyoak, Keith J

2012-07-01

The representation and manipulation of structured relations is central to human reasoning. Recent work in computational modeling and neuroscience has set the stage for developing more detailed neurocomputational models of these abilities. Several key neural findings appear to dovetail with computational constraints derived from a model of analogical processing, 'Learning and Inference with Schemas and Analogies' (LISA). These include evidence that (i) coherent oscillatory activity in the gamma and theta bands enables long-distance communication between the prefrontal cortex and posterior brain regions where information is stored; (ii) neurons in prefrontal cortex can rapidly learn to represent abstract concepts; (iii) a rostral-caudal abstraction gradient exists in the PFC; and (iv) the inferior frontal gyrus exerts inhibitory control over task-irrelevant information. Copyright © 2012. Published by Elsevier Ltd.

Instance-based categorization: automatic versus intentional forms of retrieval.

PubMed

Neal, A; Hesketh, B; Andrews, S

1995-03-01

Two experiments are reported which attempt to disentangle the relative contribution of intentional and automatic forms of retrieval to instance-based categorization. A financial decision-making task was used in which subjects had to decide whether a bank would approve loans for a series of applicants. Experiment 1 found that categorization was sensitive to instance-specific knowledge, even when subjects had practiced using a simple rule. L. L. Jacoby's (1991) process-dissociation procedure was adapted for use in Experiment 2 to infer the relative contribution of intentional and automatic retrieval processes to categorization decisions. The results provided (1) strong evidence that intentional retrieval processes influence categorization, and (2) some preliminary evidence suggesting that automatic retrieval processes may also contribute to categorization decisions.

Sieve-based relation extraction of gene regulatory networks from biological literature

PubMed Central

2015-01-01

Background Relation extraction is an essential procedure in literature mining. It focuses on extracting semantic relations between parts of text, called mentions. Biomedical literature includes an enormous amount of textual descriptions of biological entities, their interactions and results of related experiments. To extract them in an explicit, computer readable format, these relations were at first extracted manually from databases. Manual curation was later replaced with automatic or semi-automatic tools with natural language processing capabilities. The current challenge is the development of information extraction procedures that can directly infer more complex relational structures, such as gene regulatory networks. Results We develop a computational approach for extraction of gene regulatory networks from textual data. Our method is designed as a sieve-based system and uses linear-chain conditional random fields and rules for relation extraction. With this method we successfully extracted the sporulation gene regulation network in the bacterium Bacillus subtilis for the information extraction challenge at the BioNLP 2013 conference. To enable extraction of distant relations using first-order models, we transform the data into skip-mention sequences. We infer multiple models, each of which is able to extract different relationship types. Following the shared task, we conducted additional analysis using different system settings that resulted in reducing the reconstruction error of bacterial sporulation network from 0.73 to 0.68, measured as the slot error rate between the predicted and the reference network. We observe that all relation extraction sieves contribute to the predictive performance of the proposed approach. Also, features constructed by considering mention words and their prefixes and suffixes are the most important features for higher accuracy of extraction. Analysis of distances between different mention types in the text shows that our choice of transforming data into skip-mention sequences is appropriate for detecting relations between distant mentions. Conclusions Linear-chain conditional random fields, along with appropriate data transformations, can be efficiently used to extract relations. The sieve-based architecture simplifies the system as new sieves can be easily added or removed and each sieve can utilize the results of previous ones. Furthermore, sieves with conditional random fields can be trained on arbitrary text data and hence are applicable to broad range of relation extraction tasks and data domains. PMID:26551454

Sieve-based relation extraction of gene regulatory networks from biological literature.

PubMed

Žitnik, Slavko; Žitnik, Marinka; Zupan, Blaž; Bajec, Marko

2015-01-01

Relation extraction is an essential procedure in literature mining. It focuses on extracting semantic relations between parts of text, called mentions. Biomedical literature includes an enormous amount of textual descriptions of biological entities, their interactions and results of related experiments. To extract them in an explicit, computer readable format, these relations were at first extracted manually from databases. Manual curation was later replaced with automatic or semi-automatic tools with natural language processing capabilities. The current challenge is the development of information extraction procedures that can directly infer more complex relational structures, such as gene regulatory networks. We develop a computational approach for extraction of gene regulatory networks from textual data. Our method is designed as a sieve-based system and uses linear-chain conditional random fields and rules for relation extraction. With this method we successfully extracted the sporulation gene regulation network in the bacterium Bacillus subtilis for the information extraction challenge at the BioNLP 2013 conference. To enable extraction of distant relations using first-order models, we transform the data into skip-mention sequences. We infer multiple models, each of which is able to extract different relationship types. Following the shared task, we conducted additional analysis using different system settings that resulted in reducing the reconstruction error of bacterial sporulation network from 0.73 to 0.68, measured as the slot error rate between the predicted and the reference network. We observe that all relation extraction sieves contribute to the predictive performance of the proposed approach. Also, features constructed by considering mention words and their prefixes and suffixes are the most important features for higher accuracy of extraction. Analysis of distances between different mention types in the text shows that our choice of transforming data into skip-mention sequences is appropriate for detecting relations between distant mentions. Linear-chain conditional random fields, along with appropriate data transformations, can be efficiently used to extract relations. The sieve-based architecture simplifies the system as new sieves can be easily added or removed and each sieve can utilize the results of previous ones. Furthermore, sieves with conditional random fields can be trained on arbitrary text data and hence are applicable to broad range of relation extraction tasks and data domains.

Social inference and social anxiety: evidence of a fear-congruent self-referential learning bias.

PubMed

Button, Katherine S; Browning, Michael; Munafò, Marcus R; Lewis, Glyn

2012-12-01

Fears of negative evaluation characterise social anxiety, and preferential processing of fear-relevant information is implicated in maintaining symptoms. Little is known, however, about the relationship between social anxiety and the process of inferring negative evaluation. The ability to use social information to learn what others think about one, referred to here as self-referential learning, is fundamental for effective social interaction. The aim of this research was to examine whether social anxiety is associated with self-referential learning. 102 Females with either high (n = 52) or low (n = 50) self-reported social anxiety completed a novel probabilistic social learning task. Using trial and error, the task required participants to learn two self-referential rules, 'I am liked' and 'I am disliked'. Participants across the sample were better at learning the positive rule 'I am liked' than the negative rule 'I am disliked', β = -6.4, 95% CI [-8.0, -4.7], p < 0.001. This preference for learning positive self-referential information was strongest in the lowest socially anxious and was abolished in the most symptomatic participants. Relative to the low group, the high anxiety group were better at learning they were disliked and worse at learning they were liked, social anxiety by rule interaction β = 3.6; 95% CI [+0.3, +7.0], p = 0.03. The specificity of the results to self-referential processing requires further research. Healthy individuals show a robust preference for learning that they are liked relative to disliked. This positive self-referential bias is reduced in social anxiety in a way that would be expected to exacerbate anxiety symptoms. Copyright © 2012 Elsevier Ltd. All rights reserved.

BiSet: Semantic Edge Bundling with Biclusters for Sensemaking.

PubMed

Sun, Maoyuan; Mi, Peng; North, Chris; Ramakrishnan, Naren

2016-01-01

Identifying coordinated relationships is an important task in data analytics. For example, an intelligence analyst might want to discover three suspicious people who all visited the same four cities. Existing techniques that display individual relationships, such as between lists of entities, require repetitious manual selection and significant mental aggregation in cluttered visualizations to find coordinated relationships. In this paper, we present BiSet, a visual analytics technique to support interactive exploration of coordinated relationships. In BiSet, we model coordinated relationships as biclusters and algorithmically mine them from a dataset. Then, we visualize the biclusters in context as bundled edges between sets of related entities. Thus, bundles enable analysts to infer task-oriented semantic insights about potentially coordinated activities. We make bundles as first class objects and add a new layer, "in-between", to contain these bundle objects. Based on this, bundles serve to organize entities represented in lists and visually reveal their membership. Users can interact with edge bundles to organize related entities, and vice versa, for sensemaking purposes. With a usage scenario, we demonstrate how BiSet supports the exploration of coordinated relationships in text analytics.

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

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

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.

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.

Plasma level-dependent effects of methylphenidate on task-related functional magnetic resonance imaging signal changes.

PubMed

Müller, Ulrich; Suckling, J; Zelaya, F; Honey, G; Faessel, H; Williams, S C R; Routledge, C; Brown, J; Robbins, T W; Bullmore, E T

2005-08-01

Methylphenidate (MPH) is a dopamine and noradrenaline enhancing drug used to treat attentional deficits. Understanding of its cognition-enhancing effects and the neurobiological mechanisms involved, especially in elderly people, is currently incomplete. The aim of this study was to investigate the relationship between MPH plasma levels and brain activation during visuospatial attention and movement preparation. Twelve healthy elderly volunteers were scanned twice using functional magnetic resonance imaging (fMRI) after oral administration of MPH 20 mg or placebo in a within-subject design. The cognitive paradigm was a four-choice reaction time task presented at two levels of difficulty (with and without spatial cue). Plasma MPH levels were measured at six time points between 30 and 205 min after dosing. FMRI data were analysed using a linear model to estimate physiological response to the task and nonparametric permutation tests for inference. Lateral premotor and medial posterior parietal cortical activation was increased by MPH, on average, over both levels of task difficulty. There was considerable intersubject variability in the pharmacokinetics of MPH. Greater area under the plasma concentration-time curve was positively correlated with strength of activation in motor and premotor cortex, temporoparietal cortex and caudate nucleus during the difficult version of the task. This is the first pharmacokinetic/pharmacodynamic study to find an association between plasma levels of MPH and its modulatory effects on brain activation measured using fMRI. The results suggest that catecholaminergic mechanisms may be important in brain adaptivity to task difficulty and in task-specific recruitment of spatial attention systems.

Self-Control of Haptic Assistance for Motor Learning: Influences of Frequency and Opinion of Utility

PubMed Central

Williams, Camille K.; Tseung, Victrine; Carnahan, Heather

2017-01-01

Studies of self-controlled practice have shown benefits when learners controlled feedback schedule, use of assistive devices and task difficulty, with benefits attributed to information processing and motivational advantages of self-control. Although haptic assistance serves as feedback, aids task performance and modifies task difficulty, researchers have yet to explore whether self-control over haptic assistance could be beneficial for learning. We explored whether self-control of haptic assistance would be beneficial for learning a tracing task. Self-controlled participants selected practice blocks on which they would receive haptic assistance, while participants in a yoked group received haptic assistance on blocks determined by a matched self-controlled participant. We inferred learning from performance on retention tests without haptic assistance. From qualitative analysis of open-ended questions related to rationales for/experiences of the haptic assistance that was chosen/provided, themes emerged regarding participants’ views of the utility of haptic assistance for performance and learning. Results showed that learning was directly impacted by the frequency of haptic assistance for self-controlled participants only and view of haptic assistance. Furthermore, self-controlled participants’ views were significantly associated with their requested haptic assistance frequency. We discuss these findings as further support for the beneficial role of self-controlled practice for motor learning. PMID:29255438

Detecting fast, online reasoning processes in clinical decision making.

PubMed

Flores, Amanda; Cobos, Pedro L; López, Francisco J; Godoy, Antonio

2014-06-01

In an experiment that used the inconsistency paradigm, experienced clinical psychologists and psychology students performed a reading task using clinical reports and a diagnostic judgment task. The clinical reports provided information about the symptoms of hypothetical clients who had been previously diagnosed with a specific mental disorder. Reading times of inconsistent target sentences were slower than those of control sentences, demonstrating an inconsistency effect. The results also showed that experienced clinicians gave different weights to different symptoms according to their relevance when fluently reading the clinical reports provided, despite the fact that all the symptoms were of equal diagnostic value according to the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; American Psychiatric Association, 2000). The diagnostic judgment task yielded a similar pattern of results. In contrast to previous findings, the results of the reading task may be taken as direct evidence of the intervention of reasoning processes that occur very early, rapidly, and online. We suggest that these processes are based on the representation of mental disorders and that these representations are particularly suited to fast retrieval from memory and to making inferences. They may also be related to the clinicians' causal reasoning. The implications of these results for clinician training are also discussed.

The cognitive demands of second order manual control: Applications of the event related brain potential

NASA Technical Reports Server (NTRS)

Wickens, C.; Gill, R.; Kramer, A.; Ross, W.; Donchin, E.

1981-01-01

Three experiments are described in which tracking difficulty is varied in the presence of a covert tone discrimination task. Event related brain potentials (ERPs) elicited by the tones are employed as an index of the resource demands of tracking. The ERP measure reflected the control order variation, and this variable was thereby assumed to compete for perceptual/central processing resources. A fine-grained analysis of the results suggested that the primary demands of second order tracking involve the central processing operations of maintaining a more complex internal model of the dynamic system, rather than the perceptual demands of higher derivative perception. Experiment 3 varied tracking bandwidth in random input tracking, and the ERP was unaffected. Bandwidth was then inferred to compete for response-related processing resources that are independent of the ERP.

Methods for Dissecting Motivation and Related Psychological Processes in Rodents.

PubMed

Ward, Ryan D

2016-01-01

Motivational impairments are increasingly recognized as being critical to functional deficits and decreased quality of life in patients diagnosed with psychiatric disease. Accordingly, much preclinical research has focused on identifying psychological and neurobiological processes which underlie motivation . Inferring motivation from changes in overt behavioural responding in animal models, however, is complicated, and care must be taken to ensure that the observed change is accurately characterized as a change in motivation , and not due to some other, task-related process. This chapter discusses current methods for assessing motivation and related psychological processes in rodents. Using an example from work characterizing the motivational impairments in an animal model of the negative symptoms of schizophrenia, we highlight the importance of careful and rigorous experimental dissection of motivation and the related psychological processes when characterizing motivational deficits in rodent models . We suggest that such work is critical to the successful translation of preclinical findings to therapeutic benefits for patients.

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.

Face puzzle—two new video-based tasks for measuring explicit and implicit aspects of facial emotion recognition

PubMed Central

Kliemann, Dorit; Rosenblau, Gabriela; Bölte, Sven; Heekeren, Hauke R.; Dziobek, Isabel

2013-01-01

Recognizing others' emotional states is crucial for effective social interaction. While most facial emotion recognition tasks use explicit prompts that trigger consciously controlled processing, emotional faces are almost exclusively processed implicitly in real life. Recent attempts in social cognition suggest a dual process perspective, whereby explicit and implicit processes largely operate independently. However, due to differences in methodology the direct comparison of implicit and explicit social cognition has remained a challenge. Here, we introduce a new tool to comparably measure implicit and explicit processing aspects comprising basic and complex emotions in facial expressions. We developed two video-based tasks with similar answer formats to assess performance in respective facial emotion recognition processes: Face Puzzle, implicit and explicit. To assess the tasks' sensitivity to atypical social cognition and to infer interrelationship patterns between explicit and implicit processes in typical and atypical development, we included healthy adults (NT, n = 24) and adults with autism spectrum disorder (ASD, n = 24). Item analyses yielded good reliability of the new tasks. Group-specific results indicated sensitivity to subtle social impairments in high-functioning ASD. Correlation analyses with established implicit and explicit socio-cognitive measures were further in favor of the tasks' external validity. Between group comparisons provide first hints of differential relations between implicit and explicit aspects of facial emotion recognition processes in healthy compared to ASD participants. In addition, an increased magnitude of between group differences in the implicit task was found for a speed-accuracy composite measure. The new Face Puzzle tool thus provides two new tasks to separately assess explicit and implicit social functioning, for instance, to measure subtle impairments as well as potential improvements due to social cognitive interventions. PMID:23805122

Multi-Tasking and Choice of Training Data Influencing Parietal ERP Expression and Single-Trial Detection-Relevance for Neuroscience and Clinical Applications.

PubMed

Kirchner, Elsa A; Kim, Su Kyoung

2018-01-01

Event-related potentials (ERPs) are often used in brain-computer interfaces (BCIs) for communication or system control for enhancing or regaining control for motor-disabled persons. Especially results from single-trial EEG classification approaches for BCIs support correlations between single-trial ERP detection performance and ERP expression. Hence, BCIs can be considered as a paradigm shift contributing to new methods with strong influence on both neuroscience and clinical applications. Here, we investigate the relevance of the choice of training data and classifier transfer for the interpretability of results from single-trial ERP detection. In our experiments, subjects performed a visual-motor oddball task with motor-task relevant infrequent ( targets ), motor-task irrelevant infrequent ( deviants ), and motor-task irrelevant frequent ( standards ) stimuli. Under dual-task condition, a secondary senso-motor task was performed, compared to the simple-task condition. For evaluation, average ERP analysis and single-trial detection analysis with different numbers of electrodes were performed. Further, classifier transfer was investigated between simple and dual task. Parietal positive ERPs evoked by target stimuli (but not by deviants) were expressed stronger under dual-task condition, which is discussed as an increase of task emphasis and brain processes involved in task coordination and change of task set. Highest classification performance was found for targets irrespective whether all 62, 6 or 2 parietal electrodes were used. Further, higher detection performance of targets compared to standards was achieved under dual-task compared to simple-task condition in case of training on data from 2 parietal electrodes corresponding to results of ERP average analysis. Classifier transfer between tasks improves classification performance in case that training took place on more varying examples (from dual task). In summary, we showed that P300 and overlaying parietal positive ERPs can successfully be detected while subjects are performing additional ongoing motor activity. This supports single-trial detection of ERPs evoked by target events to, e.g., infer a patient's attentional state during therapeutic intervention.

Multi-Tasking and Choice of Training Data Influencing Parietal ERP Expression and Single-Trial Detection—Relevance for Neuroscience and Clinical Applications

PubMed Central

Kirchner, Elsa A.; Kim, Su Kyoung

2018-01-01

Event-related potentials (ERPs) are often used in brain-computer interfaces (BCIs) for communication or system control for enhancing or regaining control for motor-disabled persons. Especially results from single-trial EEG classification approaches for BCIs support correlations between single-trial ERP detection performance and ERP expression. Hence, BCIs can be considered as a paradigm shift contributing to new methods with strong influence on both neuroscience and clinical applications. Here, we investigate the relevance of the choice of training data and classifier transfer for the interpretability of results from single-trial ERP detection. In our experiments, subjects performed a visual-motor oddball task with motor-task relevant infrequent (targets), motor-task irrelevant infrequent (deviants), and motor-task irrelevant frequent (standards) stimuli. Under dual-task condition, a secondary senso-motor task was performed, compared to the simple-task condition. For evaluation, average ERP analysis and single-trial detection analysis with different numbers of electrodes were performed. Further, classifier transfer was investigated between simple and dual task. Parietal positive ERPs evoked by target stimuli (but not by deviants) were expressed stronger under dual-task condition, which is discussed as an increase of task emphasis and brain processes involved in task coordination and change of task set. Highest classification performance was found for targets irrespective whether all 62, 6 or 2 parietal electrodes were used. Further, higher detection performance of targets compared to standards was achieved under dual-task compared to simple-task condition in case of training on data from 2 parietal electrodes corresponding to results of ERP average analysis. Classifier transfer between tasks improves classification performance in case that training took place on more varying examples (from dual task). In summary, we showed that P300 and overlaying parietal positive ERPs can successfully be detected while subjects are performing additional ongoing motor activity. This supports single-trial detection of ERPs evoked by target events to, e.g., infer a patient's attentional state during therapeutic intervention. PMID:29636660

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

Neural networks related to dysfunctional face processing in autism spectrum disorder

PubMed Central

Nickl-Jockschat, Thomas; Rottschy, Claudia; Thommes, Johanna; Schneider, Frank; Laird, Angela R.; Fox, Peter T.; Eickhoff, Simon B.

2016-01-01

One of the most consistent neuropsychological findings in autism spectrum disorders (ASD) is a reduced interest in and impaired processing of human faces. We conducted an activation likelihood estimation meta-analysis on 14 functional imaging studies on neural correlates of face processing enrolling a total of 164 ASD patients. Subsequently, normative whole-brain functional connectivity maps for the identified regions of significant convergence were computed for the task-independent (resting-state) and task-dependent (co-activations) state in healthy subjects. Quantitative functional decoding was performed by reference to the BrainMap database. Finally, we examined the overlap of the delineated network with the results of a previous meta-analysis on structural abnormalities in ASD as well as with brain regions involved in human action observation/imitation. We found a single cluster in the left fusiform gyrus showing significantly reduced activation during face processing in ASD across all studies. Both task-dependent and task-independent analyses indicated significant functional connectivity of this region with the temporo-occipital and lateral occipital cortex, the inferior frontal and parietal cortices, the thalamus and the amygdala. Quantitative reverse inference then indicated an association of these regions mainly with face processing, affective processing, and language-related tasks. Moreover, we found that the cortex in the region of right area V5 displaying structural changes in ASD patients showed consistent connectivity with the region showing aberrant responses in the context of face processing. Finally, this network was also implicated in the human action observation/imitation network. In summary, our findings thus suggest a functionally and structurally disturbed network of occipital regions related primarily to face (but potentially also language) processing, which interact with inferior frontal as well as limbic regions and may be the core of aberrant face processing and reduced interest in faces in ASD. PMID:24869925

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

Effect of education on listening comprehension of sentences on healthy elderly: analysis of number of correct responses and task execution time.

PubMed

Silagi, Marcela Lima; Rabelo, Camila Maia; Schochat, Eliane; Mansur, Letícia Lessa

2017-11-13

To analyze the effect of education on sentence listening comprehension on cognitively healthy elderly. A total of 111 healthy elderly, aged 60-80 years of both genders were divided into two groups according to educational level: low education (0-8 years of formal education) and high education (≥9 years of formal education). The participants were assessed using the Revised Token Test, an instrument that supports the evaluation of auditory comprehension of orders with different working memory and syntactic complexity demands. The indicators used for performance analysis were the number of correct responses (accuracy analysis) and task execution time (temporal analysis) in the different blocks. The low educated group had a lower number of correct responses than the high educated group on all blocks of the test. In the temporal analysis, participants with low education had longer execution time for commands on the first four blocks related to working memory. However, the two groups had similar execution time for blocks more related to syntactic comprehension. Education influenced sentence listening comprehension on elderly. Temporal analysis allowed to infer over the relationship between comprehension and other cognitive abilities, and to observe that the low educated elderly did not use effective compensation strategies to improve their performances on the task. Therefore, low educational level, associated with aging, may potentialize the risks for language decline.

Processing inferences derived from event-related potential measures in a monitoring task

NASA Technical Reports Server (NTRS)

Horst, R. L.; Munson, R. C.; Ruchkin, D. S.

1985-01-01

Event-related potentials (ERPs) were recorded from the scalp of subjects as they monitored changing digital readouts for values that went 'out-of-bounds'. Workload was manipulated by varying the number of readouts that were monitored concurrently. The ERPs elicited by changes in the readouts showed long latency positivities that increased in amplitude, not only with the number of readouts monitored, but also with the number of monitored readouts that were 'in danger' of going out-of-bounds. No effects were found due to the number of nonmonitored readouts 'in danger'. This evidence indicates that subjects (1) selectively attended to the monitored readouts and (2) processed the monitored readouts differently as the readouts approached the out-of-bounds levels to which an overt response was required.

Human motor transfer is determined by the scaling of size and accuracy of movement.

PubMed

Kwon, Oh-Sang; Zelaznik, Howard N; Chiu, George; Pizlo, Zygmunt

2011-01-01

A transfer of training design was used to examine the role of the Index of Difficulty (ID) on transfer of learning in a sequential Fitts's law task. Specifically, the role of the ratio between the accuracy and size of movement (ID) in transfer was examined. Transfer of skilled movement is better when both the size and accuracy of movement are changed by the same factor (ID is constant) than when only size or accuracy is changed. The authors infer that the size-accuracy ratio is capturing the control strategies employed during practice and thus promotes efficient transfer. Furthermore, efficient transfer is not dependent on maintaining relative timing invariance and thus the authors provide further evidence that relative timing is not an essential feature of movement control.

Generative Inferences Based on Learned Relations

ERIC Educational Resources Information Center

Chen, Dawn; Lu, Hongjing; Holyoak, Keith J.

2017-01-01

A key property of relational representations is their "generativity": From partial descriptions of relations between entities, additional inferences can be drawn about other entities. A major theoretical challenge is to demonstrate how the capacity to make generative inferences could arise as a result of learning relations from…

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 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.

Separable Processes Before, During, and After the N400 Elicited by Previously Inferred and New Information: Evidence from Time-Frequency Decompositions

PubMed Central

Steele, Vaughn R.; Bernat, Edward M.; van den Broek, Paul; Collins, Paul F.; Patrick, Christopher J.; Marsolek, Chad J.

2012-01-01

Successful comprehension during reading often requires inferring information not explicitly presented. This information is readily accessible when subsequently encountered, and a neural correlate of this is an attenuation of the N400 event-related potential (ERP). We used ERPs and time-frequency (TF) analysis to investigate neural correlates of processing inferred information after a causal coherence inference had been generated during text comprehension. Participants read short texts, some of which promoted inference generation. After each text, they performed lexical decisions to target words that were unrelated or inference-related to the preceding text. Consistent with previous findings, inference-related words elicited an attenuated N400 relative to unrelated words. TF analyses revealed unique contributions to the N400 from activity occurring at 1–6 Hz (theta) and 0–2 Hz (delta), supporting the view that multiple, sequential processes underlie the N400. PMID:23165117

Eye movement during recall reduces objective memory performance: An extended replication.

PubMed

Leer, Arne; Engelhard, Iris M; Lenaert, Bert; Struyf, Dieter; Vervliet, Bram; Hermans, Dirk

2017-05-01

Eye Movement Desensitization and Reprocessing (EMDR) therapy for posttraumatic stress disorder involves making eye movements (EMs) during recall of a traumatic image. Experimental studies have shown that the dual task decreases self-reported memory vividness and emotionality. However valuable, these data are prone to demand effects and little can be inferred about the mechanism(s) underlying the observed effects. The current research aimed to fill this lacuna by providing two objective tests of memory performance. Experiment I involved a stimulus discrimination task. Findings were that EM during stimulus recall not only reduces self-reported memory vividness, but also slows down reaction time in a task that requires participants to discriminate the stimulus from perceptually similar stimuli. Experiment II involved a fear conditioning paradigm. It was shown that EM during recall of a threatening stimulus intensifies fearful responding to a perceptually similar yet non-threat-related stimulus, as evidenced by increases in danger expectancies and skin conductance responses. The latter result was not corroborated by startle EMG data. Together, the findings suggest that the EM manipulation renders stimulus attributes less accessible for future recall. Copyright © 2017 Elsevier Ltd. All rights reserved.

When cognition kicks in: working memory and speech understanding in noise.

PubMed

Rönnberg, Jerker; Rudner, Mary; Lunner, Thomas; Zekveld, Adriana A

2010-01-01

Perceptual load and cognitive load can be separately manipulated and dissociated in their effects on speech understanding in noise. The Ease of Language Understanding model assumes a theoretical position where perceptual task characteristics interact with the individual's implicit capacities to extract the phonological elements of speech. Phonological precision and speed of lexical access are important determinants for listening in adverse conditions. If there are mismatches between the phonological elements perceived and phonological representations in long-term memory, explicit working memory (WM)-related capacities will be continually invoked to reconstruct and infer the contents of the ongoing discourse. Whether this induces a high cognitive load or not will in turn depend on the individual's storage and processing capacities in WM. Data suggest that modulated noise maskers may serve as triggers for speech maskers and therefore induce a WM, explicit mode of processing. Individuals with high WM capacity benefit more than low WM-capacity individuals from fast amplitude compression at low or negative input speech-to-noise ratios. The general conclusion is that there is an overarching interaction between the focal purpose of processing in the primary listening task and the extent to which a secondary, distracting task taps into these processes.

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.

Relating UMLS semantic types and task-based ontology to computer-interpretable clinical practice guidelines.

PubMed

Kumar, Anand; Ciccarese, Paolo; Quaglini, Silvana; Stefanelli, Mario; Caffi, Ezio; Boiocchi, Lorenzo

2003-01-01

Medical knowledge in clinical practice guideline (GL) texts is the source of task-based computer-interpretable clinical guideline models (CIGMs). We have used Unified Medical Language System (UMLS) semantic types (STs) to understand the percentage of GL text which belongs to a particular ST. We also use UMLS semantic network together with the CIGM-specific ontology to derive a semantic meaning behind the GL text. In order to achieve this objective, we took nine GL texts from the National Guideline Clearinghouse (NGC) and marked up the text dealing with a particular ST. The STs we took into consideration were restricted taking into account the requirements of a task-based CIGM. We used DARPA Agent Markup Language and Ontology Inference Layer (DAML + OIL) to create the UMLS and CIGM specific semantic network. For the latter, as a bench test, we used the 1999 WHO-International Society of Hypertension Guidelines for the Management of Hypertension. We took into consideration the UMLS STs closest to the clinical tasks. The percentage of the GL text dealing with the ST "Health Care Activity" and subtypes "Laboratory Procedure", "Diagnostic Procedure" and "Therapeutic or Preventive Procedure" were measured. The parts of text belonging to other STs or comments were separated. A mapping of terms belonging to other STs was done to the STs under "HCA" for representation in DAML + OIL. As a result, we found that the three STs under "HCA" were the predominant STs present in the GL text. In cases where the terms of related STs existed, they were mapped into one of the three STs. The DAML + OIL representation was able to describe the hierarchy in task-based CIGMs. To conclude, we understood that the three STs could be used to represent the semantic network of the task-bases CIGMs. We identified some mapping operators which could be used for the mapping of other STs into these.

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.

Brain signatures of moral sensitivity in adolescents with early social deprivation.

PubMed

Escobar, María Josefina; Huepe, David; Decety, Jean; Sedeño, Lucas; Messow, Marie Kristin; Baez, Sandra; Rivera-Rei, Álvaro; Canales-Johnson, Andrés; Morales, Juan Pablo; Gómez, David Maximiliano; Schröeder, Johannes; Manes, Facundo; López, Vladimir; Ibánez, Agustín

2014-06-19

The present study examined neural responses associated with moral sensitivity in adolescents with a background of early social deprivation. Using high-density electroencephalography (hdEEG), brain activity was measured during an intentional inference task, which assesses rapid moral decision-making regarding intentional or unintentional harm to people and objects. We compared the responses to this task in a socially deprived group (DG) with that of a control group (CG). The event-related potentials (ERPs) results showed atypical early and late frontal cortical markers associated with attribution of intentionality during moral decision-making in DG (especially regarding intentional harm to people). The source space of the hdEEG showed reduced activity for DG compared with CG in the right prefrontal cortex, bilaterally in the ventromedial prefrontal cortex (vmPFC), and right insula. Moreover, the reduced response in vmPFC for DG was predicted by higher rates of externalizing problems. These findings demonstrate the importance of the social environment in early moral development, supporting a prefrontal maturation model of social deprivation.

Distributed Cooperation Solution Method of Complex System Based on MAS

NASA Astrophysics Data System (ADS)

Weijin, Jiang; Yuhui, Xu

To adapt the model in reconfiguring fault diagnosing to dynamic environment and the needs of solving the tasks of complex system fully, the paper introduced multi-Agent and related technology to the complicated fault diagnosis, an integrated intelligent control system is studied in this paper. Based on the thought of the structure of diagnostic decision and hierarchy in modeling, based on multi-layer decomposition strategy of diagnosis task, a multi-agent synchronous diagnosis federation integrated different knowledge expression modes and inference mechanisms are presented, the functions of management agent, diagnosis agent and decision agent are analyzed, the organization and evolution of agents in the system are proposed, and the corresponding conflict resolution algorithm in given, Layered structure of abstract agent with public attributes is build. System architecture is realized based on MAS distributed layered blackboard. The real world application shows that the proposed control structure successfully solves the fault diagnose problem of the complex plant, and the special advantage in the distributed domain.

The influence of social cognition on ego disturbances in patients with schizophrenia.

PubMed

Schimansky, Jenny; Rössler, Wulf; Haker, Helene

2012-01-01

Subjects experiencing ego disturbances can be classified as a distinct subgroup of schizophrenia patients. These symptoms imply a disturbance in the ego-world boundary, which in turn implies aberrations in the perception, processing and understanding of social information. This paper provides a comparison of a group of schizophrenia patients and a group of healthy controls on a range of social-cognitive tasks. Furthermore, it analyzes the relationship between ego disturbances and social-cognitive as well as clinical variables in the schizophrenia subsample. Two groups - 40 schizophrenia patients and 39 healthy subjects - were compared. In the source monitoring task, subjects performed simple computer mouse movements and evaluated the partially manipulated visual feedback as either self- or other-generated. In a second step, participants indicated the confidence of their decision on a 4-point rating scale. In an emotion-recognition task, subjects had to identify 6 basic emotions in the prosody of spoken sentences. In the 'reading-the-mind-in-the-eyes' test, subjects had to infer mental states from pictures that depicted others' eyes. In an attribution task, subjects were presented with descriptions of social events and asked to attribute the cause of the event either to a person, an object or a situation. Additionally, all subjects were tested for cognitive functioning levels. The schizophrenia patient group performed significantly worse on all social-cognitive tasks than the healthy control group. Correlation analysis showed that ego disturbances were related to deficits in person attribution and lower levels of confidence in the source monitoring task. Also, ego disturbances were related to higher PANSS positive scores and a higher number of hospitalizations. Stepwise regression analysis revealed that social-cognitive variables explained 48.0% of the variance in the ego-disturbance score and represented the best predictors for ego disturbances. One particular clinical variable, namely the number of hospitalizations, additionally explained 13.8% of the variance. Our findings suggest that ego disturbances are related to deficits in the social-cognitive domain, and, to a lesser extent, to clinical variables such as the number of hospitalizations. Copyright © 2012 S. Karger AG, Basel.

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-evoked brain functional magnetic susceptibility mapping by independent component analysis (χICA).

PubMed

Chen, Zikuan; Calhoun, Vince D

2016-03-01

Conventionally, independent component analysis (ICA) is performed on an fMRI magnitude dataset to analyze brain functional mapping (AICA). By solving the inverse problem of fMRI, we can reconstruct the brain magnetic susceptibility (χ) functional states. Upon the reconstructed χ dataspace, we propose an ICA-based brain functional χ mapping method (χICA) to extract task-evoked brain functional map. A complex division algorithm is applied to a timeseries of fMRI phase images to extract temporal phase changes (relative to an OFF-state snapshot). A computed inverse MRI (CIMRI) model is used to reconstruct a 4D brain χ response dataset. χICA is implemented by applying a spatial InfoMax ICA algorithm to the reconstructed 4D χ dataspace. With finger-tapping experiments on a 7T system, the χICA-extracted χ-depicted functional map is similar to the SPM-inferred functional χ map by a spatial correlation of 0.67 ± 0.05. In comparison, the AICA-extracted magnitude-depicted map is correlated with the SPM magnitude map by 0.81 ± 0.05. The understanding of the inferiority of χICA to AICA for task-evoked functional map is an ongoing research topic. For task-evoked brain functional mapping, we compare the data-driven ICA method with the task-correlated SPM method. In particular, we compare χICA with AICA for extracting task-correlated timecourses and functional maps. χICA can extract a χ-depicted task-evoked brain functional map from a reconstructed χ dataspace without the knowledge about brain hemodynamic responses. The χICA-extracted brain functional χ map reveals a bidirectional BOLD response pattern that is unavailable (or different) from AICA. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

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

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.

Object words modulate the activity of the mirror neuron system during action imitation.

PubMed

Wu, Haiyan; Tang, Honghong; Ge, Yue; Yang, Suyong; Mai, Xiaoqin; Luo, Yue-Jia; Liu, Chao

2017-11-01

Although research has demonstrated that the mirror neuron system (MNS) plays a crucial role in both action imitation and action-related semantic processing, whether action-related words can inversely modulate the MNS activity remains unclear. Here, three types of task-irrelevant words (body parts, verbs, and manufactured objects) were presented to examine the modulation effect of these words on the MNS activity during action observation and imitation. Twenty-two participants were recruited for the fMRI scanning and remaining data from 19 subjects were reported here. Brain activity results showed that word types elicited different modulation effects over nodes of the MNS (i.e., the right inferior frontal gyrus, premotor cortex, inferior parietal lobule, and STS), especially during the imitation stage. Compared with other word conditions, action imitation following manufactured objects words induced stronger activation in these brain regions during the imitation stage. These results were consistent in both task-dependent and -independent ROI analysis. Our findings thus provide evidence for the unique effect of object words on the MNS during imitation of action, which may also confirm the key role of goal inference in action imitation.

MEG and fMRI Fusion for Non-Linear Estimation of Neural and BOLD Signal Changes

PubMed Central

Plis, Sergey M.; Calhoun, Vince D.; Weisend, Michael P.; Eichele, Tom; Lane, Terran

2010-01-01

The combined analysis of magnetoencephalography (MEG)/electroencephalography and functional magnetic resonance imaging (fMRI) measurements can lead to improvement in the description of the dynamical and spatial properties of brain activity. In this paper we empirically demonstrate this improvement using simulated and recorded task related MEG and fMRI activity. Neural activity estimates were derived using a dynamic Bayesian network with continuous real valued parameters by means of a sequential Monte Carlo technique. In synthetic data, we show that MEG and fMRI fusion improves estimation of the indirectly observed neural activity and smooths tracking of the blood oxygenation level dependent (BOLD) response. In recordings of task related neural activity the combination of MEG and fMRI produces a result with greater signal-to-noise ratio, that confirms the expectation arising from the nature of the experiment. The highly non-linear model of the BOLD response poses a difficult inference problem for neural activity estimation; computational requirements are also high due to the time and space complexity. We show that joint analysis of the data improves the system's behavior by stabilizing the differential equations system and by requiring fewer computational resources. PMID:21120141

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.

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.

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.

Self-recognition, theory-of-mind, and self-awareness: what side are you on?

PubMed

Morin, Alain

2011-05-01

A fashionable view in comparative psychology states that primates possess self-awareness because they exhibit mirror self-recognition (MSR), which in turn makes it possible to infer mental states in others ("theory-of-mind"; ToM). In cognitive neuroscience, an increasingly popular position holds that the right hemisphere represents the centre of self-awareness because MSR and ToM tasks presumably increase activity in that hemisphere. These two claims are critically assessed here as follows: (1) MSR should not be equated with full-blown self-awareness, as it most probably only requires kinaesthetic self-knowledge and does not involve access to one's mental events; (2) ToM and self-awareness are fairly independent and should also not be taken as equivalent notions; (3) MSR and ToM tasks engage medial and left brain areas; (4) other self-awareness tasks besides MSR and ToM tasks (e.g., self-description, autobiography) mostly recruit medial and left brain areas; (5) and recent neuropsychological evidence implies that inner speech (produced by the left hemisphere) plays a significant role in self-referential activity. The main conclusions reached based on this analysis are that (a) organisms that display MSR most probably do not possess introspective self-awareness, and (b) self-related processes most likely engage a distributed network of brain regions situated in both hemispheres.

Emotional and cognitive social processes are impaired in Parkinson's disease and are related to behavioral disorders.

PubMed

Narme, Pauline; Mouras, Harold; Roussel, Martine; Duru, Cécile; Krystkowiak, Pierre; Godefroy, Olivier

2013-03-01

Parkinson's disease (PD) is associated with behavioral disorders that can affect social functioning but are poorly understood. Since emotional and cognitive social processes are known to be crucial in social relationships, impairment of these processes may account for the emergence of behavioral disorders. We used a systematic battery of tests to assess emotional processes and social cognition in PD patients and relate our findings to conventional neuropsychological data (especially behavioral disorders). Twenty-three PD patients and 46 controls (matched for age and educational level) were included in the study and underwent neuropsychological testing, including an assessment of the behavioral and cognitive components of executive function. Emotional and cognitive social processes were assessed with the Interpersonal Reactivity Index caregiver-administered questionnaire (as a measure of empathy), a facial emotion recognition task and two theory of mind (ToM) tasks. When compared with controls, PD patients showed low levels of empathy (p = .006), impaired facial emotion recognition (which persisted after correction for perceptual abilities) (p = .001), poor performance in a second-order ToM task (p = .008) that assessed both cognitive (p = .004) and affective (p = .03) inferences and, lastly, frequent dysexecutive behavioral disorders (in over 40% of the patients). Overall, impaired emotional and cognitive social functioning was observed in 17% of patients and was related to certain cognitive dysexecutive disorders. In terms of behavioral dysexecutive disorders, social behavior disorders were related to impaired emotional and cognitive social functioning (p = .04) but were independent of cognitive impairments. Emotional and cognitive social processes were found to be impaired in Parkinson's disease. This impairment may account for the emergence of social behavioral disorders. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Predicting couple therapy outcomes based on speech acoustic features

PubMed Central

Nasir, Md; Baucom, Brian Robert; Narayanan, Shrikanth

2017-01-01

Automated assessment and prediction of marital outcome in couples therapy is a challenging task but promises to be a potentially useful tool for clinical psychologists. Computational approaches for inferring therapy outcomes using observable behavioral information obtained from conversations between spouses offer objective means for understanding relationship dynamics. In this work, we explore whether the acoustics of the spoken interactions of clinically distressed spouses provide information towards assessment of therapy outcomes. The therapy outcome prediction task in this work includes detecting whether there was a relationship improvement or not (posed as a binary classification) as well as discerning varying levels of improvement or decline in the relationship status (posed as a multiclass recognition task). We use each interlocutor’s acoustic speech signal characteristics such as vocal intonation and intensity, both independently and in relation to one another, as cues for predicting the therapy outcome. We also compare prediction performance with one obtained via standardized behavioral codes characterizing the relationship dynamics provided by human experts as features for automated classification. Our experiments, using data from a longitudinal clinical study of couples in distressed relations, showed that predictions of relationship outcomes obtained directly from vocal acoustics are comparable or superior to those obtained using human-rated behavioral codes as prediction features. In addition, combining direct signal-derived features with manually coded behavioral features improved the prediction performance in most cases, indicating the complementarity of relevant information captured by humans and machine algorithms. Additionally, considering the vocal properties of the interlocutors in relation to one another, rather than in isolation, showed to be important for improving the automatic prediction. This finding supports the notion that behavioral outcome, like many other behavioral aspects, is closely related to the dynamics and mutual influence of the interlocutors during their interaction and their resulting behavioral patterns. PMID:28934302

Separable processes before, during, and after the N400 elicited by previously inferred and new information: evidence from time-frequency decompositions.

PubMed

Steele, Vaughn R; Bernat, Edward M; van den Broek, Paul; Collins, Paul F; Patrick, Christopher J; Marsolek, Chad J

2013-01-25

Successful comprehension during reading often requires inferring information not explicitly presented. This information is readily accessible when subsequently encountered, and a neural correlate of this is an attenuation of the N400 event-related potential (ERP). We used ERPs and time-frequency (TF) analysis to investigate neural correlates of processing inferred information after a causal coherence inference had been generated during text comprehension. Participants read short texts, some of which promoted inference generation. After each text, they performed lexical decisions to target words that were unrelated or inference-related to the preceding text. Consistent with previous findings, inference-related words elicited an attenuated N400 relative to unrelated words. TF analyses revealed unique contributions to the N400 from activity occurring at 1-6 Hz (theta) and 0-2 Hz (delta), supporting the view that multiple, sequential processes underlie the N400. Copyright © 2012 Elsevier B.V. All rights reserved.

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.

Are great apes able to reason from multi-item samples to populations of food items?

PubMed

Eckert, Johanna; Rakoczy, Hannes; Call, Josep

2017-10-01

Inductive learning from limited observations is a cognitive capacity of fundamental importance. In humans, it is underwritten by our intuitive statistics, the ability to draw systematic inferences from populations to randomly drawn samples and vice versa. According to recent research in cognitive development, human intuitive statistics develops early in infancy. Recent work in comparative psychology has produced first evidence for analogous cognitive capacities in great apes who flexibly drew inferences from populations to samples. In the present study, we investigated whether great apes (Pongo abelii, Pan troglodytes, Pan paniscus, Gorilla gorilla) also draw inductive inferences in the opposite direction, from samples to populations. In two experiments, apes saw an experimenter randomly drawing one multi-item sample from each of two populations of food items. The populations differed in their proportion of preferred to neutral items (24:6 vs. 6:24) but apes saw only the distribution of food items in the samples that reflected the distribution of the respective populations (e.g., 4:1 vs. 1:4). Based on this observation they were then allowed to choose between the two populations. Results show that apes seemed to make inferences from samples to populations and thus chose the population from which the more favorable (4:1) sample was drawn in Experiment 1. In this experiment, the more attractive sample not only contained proportionally but also absolutely more preferred food items than the less attractive sample. Experiment 2, however, revealed that when absolute and relative frequencies were disentangled, apes performed at chance level. Whether these limitations in apes' performance reflect true limits of cognitive competence or merely performance limitations due to accessory task demands is still an open question. © 2017 Wiley Periodicals, Inc.

Network inference from functional experimental data (Conference Presentation)

NASA Astrophysics Data System (ADS)

Desrosiers, Patrick; Labrecque, Simon; Tremblay, Maxime; Bélanger, Mathieu; De Dorlodot, Bertrand; Côté, Daniel C.

2016-03-01

Functional connectivity maps of neuronal networks are critical tools to understand how neurons form circuits, how information is encoded and processed by neurons, how memory is shaped, and how these basic processes are altered under pathological conditions. Current light microscopy allows to observe calcium or electrical activity of thousands of neurons simultaneously, yet assessing comprehensive connectivity maps directly from such data remains a non-trivial analytical task. There exist simple statistical methods, such as cross-correlation and Granger causality, but they only detect linear interactions between neurons. Other more involved inference methods inspired by information theory, such as mutual information and transfer entropy, identify more accurately connections between neurons but also require more computational resources. We carried out a comparative study of common connectivity inference methods. The relative accuracy and computational cost of each method was determined via simulated fluorescence traces generated with realistic computational models of interacting neurons in networks of different topologies (clustered or non-clustered) and sizes (10-1000 neurons). To bridge the computational and experimental works, we observed the intracellular calcium activity of live hippocampal neuronal cultures infected with the fluorescent calcium marker GCaMP6f. The spontaneous activity of the networks, consisting of 50-100 neurons per field of view, was recorded from 20 to 50 Hz on a microscope controlled by a homemade software. We implemented all connectivity inference methods in the software, which rapidly loads calcium fluorescence movies, segments the images, extracts the fluorescence traces, and assesses the functional connections (with strengths and directions) between each pair of neurons. We used this software to assess, in real time, the functional connectivity from real calcium imaging data in basal conditions, under plasticity protocols, and epileptic conditions.

Inferring deep-brain activity from cortical activity using functional near-infrared spectroscopy

PubMed Central

Liu, Ning; Cui, Xu; Bryant, Daniel M.; Glover, Gary H.; Reiss, Allan L.

2015-01-01

Functional near-infrared spectroscopy (fNIRS) is an increasingly popular technology for studying brain function because it is non-invasive, non-irradiating and relatively inexpensive. Further, fNIRS potentially allows measurement of hemodynamic activity with high temporal resolution (milliseconds) and in naturalistic settings. However, in comparison with other imaging modalities, namely fMRI, fNIRS has a significant drawback: limited sensitivity to hemodynamic changes in deep-brain regions. To overcome this limitation, we developed a computational method to infer deep-brain activity using fNIRS measurements of cortical activity. Using simultaneous fNIRS and fMRI, we measured brain activity in 17 participants as they completed three cognitive tasks. A support vector regression (SVR) learning algorithm was used to predict activity in twelve deep-brain regions using information from surface fNIRS measurements. We compared these predictions against actual fMRI-measured activity using Pearson’s correlation to quantify prediction performance. To provide a benchmark for comparison, we also used fMRI measurements of cortical activity to infer deep-brain activity. When using fMRI-measured activity from the entire cortex, we were able to predict deep-brain activity in the fusiform cortex with an average correlation coefficient of 0.80 and in all deep-brain regions with an average correlation coefficient of 0.67. The top 15% of predictions using fNIRS signal achieved an accuracy of 0.7. To our knowledge, this study is the first to investigate the feasibility of using cortical activity to infer deep-brain activity. This new method has the potential to extend fNIRS applications in cognitive and clinical neuroscience research. PMID:25798327

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

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…

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.

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.

Belief and sign, true and false: the unique of false belief reasoning.

PubMed

Zhang, Ting; Zhang, Qin; Li, Yiyuan; Long, Changquan; Li, Hong

2013-11-01

For a long time, a controversy has been proposed that whether the process of theory of mind is a result of domain-specific or domain-general changes (Wellman in The handbook of childhood cognitive development. Blackwell Publication, New Jersey, 2011). This event-related potential study explored the neural time course of domain-general and domain-specific components in belief reasoning. Fourteen participants completed location transfer false belief (FB), true belief (TB), false sign (FS) and true sign (TS) tasks, in which two pictures told a story related to a dog that ran from a green into a red box. In the TB and FB tasks, a boy saw or did not see the transfer of the dog, respectively. In the FS and TS tasks, an arrow that pointed to the green box either altered its direction to the red box or did not alter following the transfer of the dog. Participants then inferred where the boy thought of, or the arrow indicated the location of the dog. FB and TB reasoning elicited lower N2 amplitudes than FS and TS reasoning, which is associated with domain-general components, the detection, and classification. The late slow wave (LSW) for FB was more positive at frontal, central, and parietal sites than FS because of the domain-specific component involved in FB reasoning. However, the LSW was less positive for TB than for FB but did not differ from the TS condition, which implies that mental representation might not be involved in TB reasoning.

Orientation Transfer in Vernier and Stereoacuity Training.

PubMed

Snell, Nathaniel; Kattner, Florian; Rokers, Bas; Green, C Shawn

2015-01-01

Human performance on various visual tasks can be improved substantially via training. However, the enhancements are frequently specific to relatively low-level stimulus dimensions. While such specificity has often been thought to be indicative of a low-level neural locus of learning, recent research suggests that these same effects can be accounted for by changes in higher-level areas--in particular in the way higher-level areas read out information from lower-level areas in the service of highly practiced decisions. Here we contrast the degree of orientation transfer seen after training on two different tasks--vernier acuity and stereoacuity. Importantly, while the decision rule that could improve vernier acuity (i.e. a discriminant in the image plane) would not be transferable across orientations, the simplest rule that could be learned to solve the stereoacuity task (i.e. a discriminant in the depth plane) would be insensitive to changes in orientation. Thus, given a read-out hypothesis, more substantial transfer would be expected as a result of stereoacuity than vernier acuity training. To test this prediction, participants were trained (7500 total trials) on either a stereoacuity (N = 9) or vernier acuity (N = 7) task with the stimuli in either a vertical or horizontal configuration (balanced across participants). Following training, transfer to the untrained orientation was assessed. As predicted, evidence for relatively orientation specific learning was observed in vernier trained participants, while no evidence of specificity was observed in stereo trained participants. These results build upon the emerging view that perceptual learning (even very specific learning effects) may reflect changes in inferences made by high-level areas, rather than necessarily fully reflecting changes in the receptive field properties of low-level areas.

Neural correlates of pragmatic language comprehension in autism spectrum disorders.

PubMed

Tesink, C M J Y; Buitelaar, J K; Petersson, K M; van der Gaag, R J; Kan, C C; Tendolkar, I; Hagoort, P

2009-07-01

Difficulties with pragmatic aspects of communication are universal across individuals with autism spectrum disorders (ASDs). Here we focused on an aspect of pragmatic language comprehension that is relevant to social interaction in daily life: the integration of speaker characteristics inferred from the voice with the content of a message. Using functional magnetic resonance imaging (fMRI), we examined the neural correlates of the integration of voice-based inferences about the speaker's age, gender or social background, and sentence content in adults with ASD and matched control participants. Relative to the control group, the ASD group showed increased activation in right inferior frontal gyrus (RIFG; Brodmann area 47) for speaker-incongruent sentences compared to speaker-congruent sentences. Given that both groups performed behaviourally at a similar level on a debriefing interview outside the scanner, the increased activation in RIFG for the ASD group was interpreted as being compensatory in nature. It presumably reflects spill-over processing from the language dominant left hemisphere due to higher task demands faced by the participants with ASD when integrating speaker characteristics and the content of a spoken sentence. Furthermore, only the control group showed decreased activation for speaker-incongruent relative to speaker-congruent sentences in right ventral medial prefrontal cortex (vMPFC; Brodmann area 10), including right anterior cingulate cortex (ACC; Brodmann area 24/32). Since vMPFC is involved in self-referential processing related to judgments and inferences about self and others, the absence of such a modulation in vMPFC activation in the ASD group possibly points to atypical default self-referential mental activity in ASD. Our results show that in ASD compensatory mechanisms are necessary in implicit, low-level inferential processes in spoken language understanding. This indicates that pragmatic language problems in ASD are not restricted to high-level inferential processes, but encompass the most basic aspects of pragmatic language processing.

Relational trustworthiness: how status affects intra-organizational inequality in job autonomy.

PubMed

Campos-Castillo, Celeste; Ewoodzie, Kwesi

2014-03-01

Recent accounts of trustworthiness have moved away from treating it as a stable, individual-level attribute toward viewing it as a variable situated in a relational context, but have not been formalized or supported empirically. We extend status characteristics theory (SCT) to develop formal propositions about relational trustworthiness. We posit that members of task- and collectively oriented groups (non-consciously) infer three qualities from their relative status that are commonly used to determine an individual's trustworthiness: ability, benevolence, and integrity. We apply our formalization to clarify ambiguities regarding intra-organizational job autonomy inequality, thereby linking SCT to broader disparities rooted in job autonomy. We analyze data from a vignette experiment and the General Social Survey to test incrementally how well our propositions generalize across different settings and populations. Results generally support our proposed links between status and intra-organizational job autonomy. We discuss implications for SCT in understanding broader patterns of inequalities. Copyright © 2013 Elsevier Inc. All rights reserved.

The Influence of Concreteness of Concepts on the Integration of Novel Words into the Semantic Network

PubMed Central

Ding, Jinfeng; Liu, Wenjuan; Yang, Yufang

2017-01-01

On the basis of previous studies revealing a processing advantage of concrete words over abstract words, the current study aimed to further explore the influence of concreteness on the integration of novel words into semantic memory with the event related potential (ERP) technique. In the experiment during the learning phase participants read two-sentence contexts and inferred the meaning of novel words. The novel words were two-character non-words in Chinese language. Their meaning was either a concrete or abstract known concept which could be inferred from the contexts. During the testing phase participants performed a lexical decision task in which the learned novel words served as primes for either their corresponding concepts, semantically related or unrelated targets. For the concrete novel words, the semantically related words belonged to the same semantic categories with their corresponding concepts. For the abstract novel words, the semantically related words were synonyms of their corresponding concepts. The unrelated targets were real words which were concrete or abstract for the concrete or abstract novel words respectively. The ERP results showed that the corresponding concepts and the semantically related words elicited smaller N400s than the unrelated words. The N400 effect was not modulated by the concreteness of the concepts. In addition, the concrete corresponding concepts elicited a smaller late positive component (LPC) than the concrete unrelated words. This LPC effect was absent for the abstract words. The results indicate that although both concrete and abstract novel words can be acquired and linked to their related words in the semantic network after a short learning phase, the concrete novel words are learned better. Our findings support the (extended) dual coding theory and broaden our understanding of adult word learning and changes in concept organization. PMID:29255440

The Influence of Concreteness of Concepts on the Integration of Novel Words into the Semantic Network.

PubMed

Ding, Jinfeng; Liu, Wenjuan; Yang, Yufang

2017-01-01

On the basis of previous studies revealing a processing advantage of concrete words over abstract words, the current study aimed to further explore the influence of concreteness on the integration of novel words into semantic memory with the event related potential (ERP) technique. In the experiment during the learning phase participants read two-sentence contexts and inferred the meaning of novel words. The novel words were two-character non-words in Chinese language. Their meaning was either a concrete or abstract known concept which could be inferred from the contexts. During the testing phase participants performed a lexical decision task in which the learned novel words served as primes for either their corresponding concepts, semantically related or unrelated targets. For the concrete novel words, the semantically related words belonged to the same semantic categories with their corresponding concepts. For the abstract novel words, the semantically related words were synonyms of their corresponding concepts. The unrelated targets were real words which were concrete or abstract for the concrete or abstract novel words respectively. The ERP results showed that the corresponding concepts and the semantically related words elicited smaller N400s than the unrelated words. The N400 effect was not modulated by the concreteness of the concepts. In addition, the concrete corresponding concepts elicited a smaller late positive component (LPC) than the concrete unrelated words. This LPC effect was absent for the abstract words. The results indicate that although both concrete and abstract novel words can be acquired and linked to their related words in the semantic network after a short learning phase, the concrete novel words are learned better. Our findings support the (extended) dual coding theory and broaden our understanding of adult word learning and changes in concept organization.

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…

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

The role of Broca's area in speech perception: evidence from aphasia revisited.

PubMed

Hickok, Gregory; Costanzo, Maddalena; Capasso, Rita; Miceli, Gabriele

2011-12-01

Motor theories of speech perception have been re-vitalized as a consequence of the discovery of mirror neurons. Some authors have even promoted a strong version of the motor theory, arguing that the motor speech system is critical for perception. Part of the evidence that is cited in favor of this claim is the observation from the early 1980s that individuals with Broca's aphasia, and therefore inferred damage to Broca's area, can have deficits in speech sound discrimination. Here we re-examine this issue in 24 patients with radiologically confirmed lesions to Broca's area and various degrees of associated non-fluent speech production. Patients performed two same-different discrimination tasks involving pairs of CV syllables, one in which both CVs were presented auditorily, and the other in which one syllable was auditorily presented and the other visually presented as an orthographic form; word comprehension was also assessed using word-to-picture matching tasks in both auditory and visual forms. Discrimination performance on the all-auditory task was four standard deviations above chance, as measured using d', and was unrelated to the degree of non-fluency in the patients' speech production. Performance on the auditory-visual task, however, was worse than, and not correlated with, the all-auditory task. The auditory-visual task was related to the degree of speech non-fluency. Word comprehension was at ceiling for the auditory version (97% accuracy) and near ceiling for the orthographic version (90% accuracy). We conclude that the motor speech system is not necessary for speech perception as measured both by discrimination and comprehension paradigms, but may play a role in orthographic decoding or in auditory-visual matching of phonological forms. 2011 Elsevier Inc. All rights reserved.

The brain-sex theory of occupational choice: a counterexample.

PubMed

Esgate, Anthony; Flynn, Maria

2005-02-01

The brain-sex theory of occupational choice suggests that males and females in male-typical careers show a male pattern of cognitive ability in terms of better spatial than verbal performance on cognitive tests with the reverse pattern for females and males in female-typical careers. These differences are thought to result from patterns of cerebral functional lateralisation. This study sought such occupationally related effects using synonym generation (verbal ability) and mental rotation (spatial ability) tasks used previously. It also used entrants to these careers as participants to examine whether patterns of cognitive abilities might predate explicit training and practice. Using a population of entrants to sex-differentiated university courses, a moderate occupational effect on the synonym generation task was found, along with a weak (p < .10) sex effect on the mental rotation task. Highest performance on the mental rotation task was by female students in fashion design, a female-dominated occupation which makes substantial visuospatial demands and attracts many students with literacy problems such as dyslexia. This group then appears to be a counterexample to the brain-sex theory. However, methodological issues surrounding previous studies are highlighted: the simple synonym task appears to show limited discrimination of the sexes, leading to questions concerning the legitimacy of inferences about lateralisation based on scores from that test. Moreover, the human figure-based mental rotation task appears to tap the wrong aspect of visuospatial skill, likely to be needed for male-typical courses such as engineering. Since the fashion-design career is also one that attracts disproportionately many male students whose sexual orientation is homosexual, data were examined for evidence of female-typical patterns of cognitive performance among that subgroup. This was not found. This study therefore provides no evidence for the claim that female-pattern cerebral functional lateralisation is likely in gay males.

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

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

Interfering with the neural activity of mirror-related frontal areas impairs mentalistic inferences.

PubMed

Herbet, Guillaume; Lafargue, Gilles; Moritz-Gasser, Sylvie; Bonnetblanc, François; Duffau, Hugues

2015-07-01

According to recently proposed interactive dual-process theories, mentalizing abilities emerge from the coherent interaction between two physically distinct neural systems: (1) the mirror network, coding for the low-level embodied representations involved in pre-reflective sociocognitive processes and (2) the mentalizing network per se, which codes for higher level representations subtending the reflective attribution of psychological states. However, although the latest studies have shown that the core areas forming these two neurocognitive systems do indeed maintain effective connectivity during mentalizing, it is unclear whether an intact mirror system (and, more specifically, its anterior node, namely the posterior inferior frontal cortex) is a prerequisite for accurate mentalistic inferences. Intraoperative brain mapping via direct electrical stimulation offers a unique opportunity to address this issue. Electrical stimulation of the brain creates a "virtual" lesion, which provides functional information on well-defined parts of the cerebral cortex. In the present study, five patients were mapped in real time while they performed a mentalizing task. We found six responsive sites: four in the lateral part of the right pars opercularis and two in the dorsal part of the right pars triangularis. On the subcortical level, two additional sites were located within the white matter connectivity of the pars opercularis. Taken as a whole, our results suggest that the right inferior frontal cortex and its underlying axonal connectivity have a key role in mentalizing. Specifically, our findings support the hypothesis whereby transient, functional disruption of the mirror network influences higher order mentalistic inferences.

A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data

PubMed Central

Calhoun, Vince D.; Liu, Jingyu; Adalı, Tülay

2009-01-01

Independent component analysis (ICA) has become an increasingly utilized approach for analyzing brain imaging data. In contrast to the widely used general linear model (GLM) that requires the user to parameterize the data (e.g. the brain's response to stimuli), ICA, by relying upon a general assumption of independence, allows the user to be agnostic regarding the exact form of the response. In addition, ICA is intrinsically a multivariate approach, and hence each component provides a grouping of brain activity into regions that share the same response pattern thus providing a natural measure of functional connectivity. There are a wide variety of ICA approaches that have been proposed, in this paper we focus upon two distinct methods. The first part of this paper reviews the use of ICA for making group inferences from fMRI data. We provide an overview of current approaches for utilizing ICA to make group inferences with a focus upon the group ICA approach implemented in the GIFT software. In the next part of this paper, we provide an overview of the use of ICA to combine or fuse multimodal data. ICA has proven particularly useful for data fusion of multiple tasks or data modalities such as single nucleotide polymorphism (SNP) data or event-related potentials. As demonstrated by a number of examples in this paper, ICA is a powerful and versatile data-driven approach for studying the brain. PMID:19059344

A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data.

PubMed

Calhoun, Vince D; Liu, Jingyu; Adali, Tülay

2009-03-01

Independent component analysis (ICA) has become an increasingly utilized approach for analyzing brain imaging data. In contrast to the widely used general linear model (GLM) that requires the user to parameterize the data (e.g. the brain's response to stimuli), ICA, by relying upon a general assumption of independence, allows the user to be agnostic regarding the exact form of the response. In addition, ICA is intrinsically a multivariate approach, and hence each component provides a grouping of brain activity into regions that share the same response pattern thus providing a natural measure of functional connectivity. There are a wide variety of ICA approaches that have been proposed, in this paper we focus upon two distinct methods. The first part of this paper reviews the use of ICA for making group inferences from fMRI data. We provide an overview of current approaches for utilizing ICA to make group inferences with a focus upon the group ICA approach implemented in the GIFT software. In the next part of this paper, we provide an overview of the use of ICA to combine or fuse multimodal data. ICA has proven particularly useful for data fusion of multiple tasks or data modalities such as single nucleotide polymorphism (SNP) data or event-related potentials. As demonstrated by a number of examples in this paper, ICA is a powerful and versatile data-driven approach for studying the brain.

Unconscious relational inference recruits the hippocampus.

PubMed

Reber, Thomas P; Luechinger, Roger; Boesiger, Peter; Henke, Katharina

2012-05-02

Relational inference denotes the capacity to encode, flexibly retrieve, and integrate multiple memories to combine past experiences to update knowledge and improve decision-making in new situations. Although relational inference is thought to depend on the hippocampus and consciousness, we now show in young, healthy men that it may occur outside consciousness but still recruits the hippocampus. In temporally distinct and unique subliminal episodes, we presented word pairs that either overlapped ("winter-red", "red-computer") or not. Effects of unconscious relational inference emerged in reaction times recorded during unconscious encoding and in the outcome of decisions made 1 min later at test, when participants judged the semantic relatedness of two supraliminal words. These words were either episodically related through a common word ("winter-computer" related through "red") or unrelated. Hippocampal activity increased during the unconscious encoding of overlapping versus nonoverlapping word pairs and during the unconscious retrieval of episodically related versus unrelated words. Furthermore, hippocampal activity during unconscious encoding predicted the outcome of decisions made at test. Hence, unconscious inference may influence decision-making in new situations.

Measuring Executive Function in Early Childhood: A Case for Formative Measurement

PubMed Central

Willoughby, Michael T.; Blair, Clancy B.

2015-01-01

This study tested whether individual executive function (EF) tasks were better characterized as formative (causal) or reflective (effect) indicators of the latent construct of EF. EF data that were collected as part of the Family Life Project (FLP), a prospective longitudinal study of families who were recruited at the birth of a new child (N = 1292), when children were 3, 4, and 5 years old. Vanishing tetrad tests were used to test the relative fit of models in which EF tasks were used as either formative or reflective indicators of the latent construct of EF in the prediction of intellectual ability (at age 3), attention deficit/hyperactivity disorder symptoms (at ages 3–5 years), and academic achievement (at kindergarten). Results consistently indicated that EF tasks were better represented as formative indicators of the latent construct of EF. Next, individual tasks were combined to form an overall measure of EF ability in ways generally consistent with formative (i.e., creating a composite mean score) and reflective (i.e., creating an EF factor score) measurement. The test-retest reliability and developmental trajectories of EF differed substantially, depending on which overall measure of EF ability was used. In general, the across-time stability of EF was markedly higher, perhaps implausibly high, when represented as a factor score versus composite score. Results are discussed with respect to the ways in which the statistical representation of EF tasks can exert a large impact on inferences regarding the developmental causes, course, and consequences of EF. More generally, these results exemplify how some psychological constructs may not conform to conventional measurement wisdom. PMID:26121388

Neural mechanism for judging the appropriateness of facial affect.

PubMed

Kim, Ji-Woong; Kim, Jae-Jin; Jeong, Bum Seok; Ki, Seon Wan; Im, Dong-Mi; Lee, Soo Jung; Lee, Hong Shick

2005-12-01

Questions regarding the appropriateness of facial expressions in particular situations arise ubiquitously in everyday social interactions. To determine the appropriateness of facial affect, first of all, we should represent our own or the other's emotional state as induced by the social situation. Then, based on these representations, we should infer the possible affective response of the other person. In this study, we identified the brain mechanism mediating special types of social evaluative judgments of facial affect in which the internal reference is related to theory of mind (ToM) processing. Many previous ToM studies have used non-emotional stimuli, but, because so much valuable social information is conveyed through nonverbal emotional channels, this investigation used emotionally salient visual materials to tap ToM. Fourteen right-handed healthy subjects volunteered for our study. We used functional magnetic resonance imaging to examine brain activation during the judgmental task for the appropriateness of facial affects as opposed to gender matching tasks. We identified activation of a brain network, which includes both medial frontal cortex, left temporal pole, left inferior frontal gyrus, and left thalamus during the judgmental task for appropriateness of facial affect compared to the gender matching task. The results of this study suggest that the brain system involved in ToM plays a key role in judging the appropriateness of facial affect in an emotionally laden situation. In addition, our result supports that common neural substrates are involved in performing diverse kinds of ToM tasks irrespective of perceptual modalities and the emotional salience of test materials.

Context Modulates Congruency Effects in Selective Attention to Social Cues.

PubMed

Ravagli, Andrea; Marini, Francesco; Marino, Barbara F M; Ricciardelli, Paola

2018-01-01

Head and gaze directions are used during social interactions as essential cues to infer where someone attends. When head and gaze are oriented toward opposite directions, we need to extract socially meaningful information despite stimulus conflict. Recently, a cognitive and neural mechanism for filtering-out conflicting stimuli has been identified while performing non-social attention tasks. This mechanism is engaged proactively when conflict is anticipated in a high proportion of trials and reactively when conflict occurs infrequently. Here, we investigated whether a similar mechanism is at play for limiting distraction from conflicting social cues during gaze or head direction discrimination tasks in contexts with different probabilities of conflict. Results showed that, for the gaze direction task only (Experiment 1), inverse efficiency (IE) scores for distractor-absent trials (i.e., faces with averted gaze and centrally oriented head) were larger (indicating worse performance) when these trials were intermixed with congruent/incongruent distractor-present trials (i.e., faces with averted gaze and tilted head in the same/opposite direction) relative to when the same distractor-absent trials were shown in isolation. Moreover, on distractor-present trials, IE scores for congruent (vs. incongruent) head-gaze pairs in blocks with rare conflict were larger than in blocks with frequent conflict, suggesting that adaptation to conflict was more efficient than adaptation to infrequent events. However, when the task required discrimination of head orientation while ignoring gaze direction, performance was not impacted by both block-level and current trial congruency (Experiment 2), unless the cognitive load of the task was increased by adding a concurrent task (Experiment 3). Overall, our study demonstrates that during attention to social cues proactive cognitive control mechanisms are modulated by the expectation of conflicting stimulus information at both the block- and trial-sequence level, and by the type of task and cognitive load. This helps to clarify the inherent differences in the distracting potential of head and gaze cues during speeded social attention tasks.

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

Effects of spatial training on transitive inference performance in humans and rhesus monkeys

PubMed Central

Gazes, Regina Paxton; Lazareva, Olga F.; Bergene, Clara N.; Hampton, Robert R.

2015-01-01

It is often suggested that transitive inference (TI; if A>B and B>C then A>C) involves mentally representing overlapping pairs of stimuli in a spatial series. However, there is little direct evidence to unequivocally determine the role of spatial representation in TI. We tested whether humans and rhesus monkeys use spatial representations in TI by training them to organize seven images in a vertical spatial array. Then, we presented subjects with a TI task using these same images. The implied TI order was either congruent or incongruent with the order of the trained spatial array. Humans in the congruent condition learned premise pairs more quickly, and were faster and more accurate in critical probe tests, suggesting that the spatial arrangement of images learned during spatial training influenced subsequent TI performance. Monkeys first trained in the congruent condition also showed higher test trial accuracy when the spatial and inferred orders were congruent. These results directly support the hypothesis that humans solve TI problems by spatial organization, and suggest that this cognitive mechanism for inference may have ancient evolutionary roots. PMID:25546105

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 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.

Intracranial EEG correlates of implicit relational inference within the hippocampus.

PubMed

Reber, T P; Do Lam, A T A; Axmacher, N; Elger, C E; Helmstaedter, C; Henke, K; Fell, J

2016-01-01

Drawing inferences from past experiences enables adaptive behavior in future situations. Inference has been shown to depend on hippocampal processes. Usually, inference is considered a deliberate and effortful mental act which happens during retrieval, and requires the focus of our awareness. Recent fMRI studies hint at the possibility that some forms of hippocampus-dependent inference can also occur during encoding and possibly also outside of awareness. Here, we sought to further explore the feasibility of hippocampal implicit inference, and specifically address the temporal evolution of implicit inference using intracranial EEG. Presurgical epilepsy patients with hippocampal depth electrodes viewed a sequence of word pairs, and judged the semantic fit between two words in each pair. Some of the word pairs entailed a common word (e.g., "winter-red," "red-cat") such that an indirect relation was established in following word pairs (e.g., "winter-cat"). The behavioral results suggested that drawing inference implicitly from past experience is feasible because indirect relations seemed to foster "fit" judgments while the absence of indirect relations fostered "do not fit" judgments, even though the participants were unaware of the indirect relations. A event-related potential (ERP) difference emerging 400 ms post-stimulus was evident in the hippocampus during encoding, suggesting that indirect relations were already established automatically during encoding of the overlapping word pairs. Further ERP differences emerged later post-stimulus (1,500 ms), were modulated by the participants' responses and were evident during encoding and test. Furthermore, response-locked ERP effects were evident at test. These ERP effects could hence be a correlate of the interaction of implicit memory with decision-making. Together, the data map out a time-course in which the hippocampus automatically integrates memories from discrete but related episodes to implicitly influence future decision making. © 2015 Wiley Periodicals, Inc.

Preschoolers’ Delay of Gratification Predicts Their Body Mass 30 Years Later

PubMed Central

Schlam, Tanya R.; Wilson, Nicole L.; Shoda, Yuichi; Mischel, Walter; Ayduk, Ozlem

2012-01-01

Objective To assess whether preschoolers’ performance on a delay of gratification task would predict their body mass index (BMI) 30 years later. Study design In the late 1960s/early 1970s, 4-year-olds from a university-affiliated preschool completed the classic delay of gratification task. As part of a longitudinal study, a subset (N = 164, 57% women) completed a follow-up approximately 30 years later and self-reported their height and weight. Data were analyzed using hierarchical regression. Results Performance on the delay of gratification task accounted for a significant portion of variance in BMI (4%, p < .01), over and above the variance accounted for by sex alone (13%). Each additional minute a preschooler delayed gratification predicted a .2 point reduction in BMI in adulthood. Conclusions Delaying gratification longer at 4 years of age was associated with having a lower BMI three decades later. The study is, however, correlational, and it is therefore not possible to make causal inferences regarding the relation between delay duration and BMI. Identifying children with greater difficulty delaying gratification could help detect children at risk of becoming overweight or obese. Interventions that improve self-control in young children have been developed and might reduce children’s risk of becoming overweight while having positive effects on other outcomes important to society. PMID:22906511

Mind wandering at the fingertips: automatic parsing of subjective states based on response time variability

PubMed Central

Bastian, Mikaël; Sackur, Jérôme

2013-01-01

Research from the last decade has successfully used two kinds of thought reports in order to assess whether the mind is wandering: random thought-probes and spontaneous reports. However, none of these two methods allows any assessment of the subjective state of the participant between two reports. In this paper, we present a step by step elaboration and testing of a continuous index, based on response time variability within Sustained Attention to Response Tasks (N = 106, for a total of 10 conditions). We first show that increased response time variability predicts mind wandering. We then compute a continuous index of response time variability throughout full experiments and show that the temporal position of a probe relative to the nearest local peak of the continuous index is predictive of mind wandering. This suggests that our index carries information about the subjective state of the subject even when he or she is not probed, and opens the way for on-line tracking of mind wandering. Finally we proceed a step further and infer the internal attentional states on the basis of the variability of response times. To this end we use the Hidden Markov Model framework, which allows us to estimate the durations of on-task and off-task episodes. PMID:24046753

Entropic Inference

NASA Astrophysics Data System (ADS)

Caticha, Ariel

2011-03-01

In this tutorial we review the essential arguments behing entropic inference. We focus on the epistemological notion of information and its relation to the Bayesian beliefs of rational agents. The problem of updating from a prior to a posterior probability distribution is tackled through an eliminative induction process that singles out the logarithmic relative entropy as the unique tool for inference. The resulting method of Maximum relative Entropy (ME), includes as special cases both MaxEnt and Bayes' rule, and therefore unifies the two themes of these workshops—the Maximum Entropy and the Bayesian methods—into a single general inference scheme.

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.

EEG signatures of arm isometric exertions in preparation, planning and execution.

PubMed

Nasseroleslami, Bahman; Lakany, Heba; Conway, Bernard A

2014-04-15

The electroencephalographic (EEG) activity patterns in humans during motor behaviour provide insight into normal motor control processes and for diagnostic and rehabilitation applications. While the patterns preceding brisk voluntary movements, and especially movement execution, are well described, there are few EEG studies that address the cortical activation patterns seen in isometric exertions and their planning. In this paper, we report on time and time-frequency EEG signatures in experiments in normal subjects (n=8), using multichannel EEG during motor preparation, planning and execution of directional centre-out arm isometric exertions performed at the wrist in the horizontal plane, in response to instruction-delay visual cues. Our observations suggest that isometric force exertions are accompanied by transient and sustained event-related potentials (ERP) and event-related (de-)synchronisations (ERD/ERS), comparable to those of a movement task. Furthermore, the ERPs and ERD/ERS are also observed during preparation and planning of the isometric task. Comparison of ear-lobe-referenced and surface Laplacian ERPs indicates the contribution of superficial sources in supplementary and pre-motor (FC(z)), parietal (CP(z)) and primary motor cortical areas (C₁ and FC₁) to ERPs (primarily negative peaks in frontal and positive peaks in parietal areas), but contribution of deep sources to sustained time-domain potentials (negativity in planning and positivity in execution). Transient and sustained ERD patterns in μ and β frequency bands of ear-lobe-referenced and surface Laplacian EEG indicate the contribution of both superficial and deep sources to ERD/ERS. As no physical displacement happens during the task, we can infer that the underlying mechanisms of motor-related ERPs and ERD/ERS patterns do not only depend on change in limb coordinate or muscle-length-dependent ascending sensory information and are primary generated by motor preparation, direction-dependent planning and execution of isometric motor tasks. The results contribute to our understanding of the functions of different brain regions during voluntary motor tasks and their activity signatures in EEG can shed light on the relationships between large-scale recordings such as EEG and other recordings such as single unit activity and fMRI in this context. Copyright © 2013 Elsevier Inc. All rights reserved.

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.

A method for inferring the rate of evolution of homologous characters that can potentially improve phylogenetic inference, resolve deep divergence and correct systematic biases.

PubMed

Cummins, Carla A; McInerney, James O

2011-12-01

Current phylogenetic methods attempt to account for evolutionary rate variation across characters in a matrix. This is generally achieved by the use of sophisticated evolutionary models, combined with dense sampling of large numbers of characters. However, systematic biases and superimposed substitutions make this task very difficult. Model adequacy can sometimes be achieved at the cost of adding large numbers of free parameters, with each parameter being optimized according to some criterion, resulting in increased computation times and large variances in the model estimates. In this study, we develop a simple approach that estimates the relative evolutionary rate of each homologous character. The method that we describe uses the similarity between characters as a proxy for evolutionary rate. In this article, we work on the premise that if the character-state distribution of a homologous character is similar to many other characters, then this character is likely to be relatively slowly evolving. If the character-state distribution of a homologous character is not similar to many or any of the rest of the characters in a data set, then it is likely to be the result of rapid evolution. We show that in some test cases, at least, the premise can hold and the inferences are robust. Importantly, the method does not use a "starting tree" to make the inference and therefore is tree independent. We demonstrate that this approach can work as well as a maximum likelihood (ML) approach, though the ML method needs to have a known phylogeny, or at least a very good estimate of that phylogeny. We then demonstrate some uses for this method of analysis, including the improvement in phylogeny reconstruction for both deep-level and recent relationships and overcoming systematic biases such as base composition bias. Furthermore, we compare this approach to two well-established methods for reweighting or removing characters. These other methods are tree-based and we show that they can be systematically biased. We feel this method can be useful for phylogeny reconstruction, understanding evolutionary rate variation, and for understanding selection variation on different characters.

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.

Counterfactual cognitive deficit in persons with Parkinson's disease

PubMed Central

McNamara, P; Durso, R; Brown, A; Lynch, A

2003-01-01

Background: Counterfactuals are mental representations of alternatives to past events. Recent research has shown them to be important for other cognitive processes, such as planning, causal reasoning, problem solving, and decision making—all processes independently linked to the frontal lobes. Objective: To test the hypothesis that counterfactual thinking is impaired in some patients with Parkinson's disease and is linked to frontal dysfunction in these patients. Methods. Measures of counterfactual processing and frontal lobe functioning were administered to 24 persons with Parkinson's disease and 15 age matched healthy controls. Results. Patients with Parkinson's disease spontaneously generated significantly fewer counterfactuals than controls despite showing no differences from controls on a semantic fluency test; they also performed at chance levels on a counterfactual inference test, while age matched controls performed above chance levels on this test. Performance on both the counterfactual generation and inference tests correlated significantly with performance on two tests traditionally linked to frontal lobe functioning (Stroop colour–word interference and Tower of London planning tasks) and one test of pragmatic social communication skills. Conclusions: Counterfactual thinking is impaired in Parkinson's disease. This impairment may be related to frontal lobe dysfunction. PMID:12876235

Making Inferences: Comprehension of Physical Causality, Intentionality, and Emotions in Discourse by High-Functioning Older Children, Adolescents, and Adults with Autism.

PubMed

Bodner, Kimberly E; Engelhardt, Christopher R; Minshew, Nancy J; Williams, Diane L

2015-09-01

Studies investigating inferential reasoning in autism spectrum disorder (ASD) have focused on the ability to make socially-related inferences or inferences more generally. Important variables for intervention planning such as whether inferences depend on physical experiences or the nature of social information have received less consideration. A measure of bridging inferences of physical causation, mental states, and emotional states was administered to older children, adolescents, and adults with and without ASD. The ASD group had more difficulty making inferences, particularly related to emotional understanding. Results suggest that individuals with ASD may not have the stored experiential knowledge that specific inferences depend upon or have difficulties accessing relevant experiences due to linguistic limitations. Further research is needed to tease these elements apart.

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).

Autobiographically significant concepts: more episodic than semantic in nature? An electrophysiological investigation of overlapping types of memory.

PubMed

Renoult, Louis; Davidson, Patrick S R; Schmitz, Erika; Park, Lillian; Campbell, Kenneth; Moscovitch, Morris; Levine, Brian

2015-01-01

A common assertion is that semantic memory emerges from episodic memory, shedding the distinctive contexts associated with episodes over time and/or repeated instances. Some semantic concepts, however, may retain their episodic origins or acquire episodic information during life experiences. The current study examined this hypothesis by investigating the ERP correlates of autobiographically significant (AS) concepts, that is, semantic concepts that are associated with vivid episodic memories. We inferred the contribution of semantic and episodic memory to AS concepts using the amplitudes of the N400 and late positive component, respectively. We compared famous names that easily brought to mind episodic memories (high AS names) against equally famous names that did not bring such recollections to mind (low AS names) on a semantic task (fame judgment) and an episodic task (recognition memory). Compared with low AS names, high AS names were associated with increased amplitude of the late positive component in both tasks. Moreover, in the recognition task, this effect of AS was highly correlated with recognition confidence. In contrast, the N400 component did not differentiate the high versus low AS names but, instead, was related to the amount of general knowledge participants had regarding each name. These results suggest that semantic concepts high in AS, such as famous names, have an episodic component and are associated with similar brain processes to those that are engaged by episodic memory. Studying AS concepts may provide unique insights into how episodic and semantic memory interact.

Improved signal processing approaches in an offline simulation of a hybrid brain–computer interface

PubMed Central

Brunner, Clemens; Allison, Brendan Z.; Krusienski, Dean J.; Kaiser, Vera; Müller-Putz, Gernot R.; Pfurtscheller, Gert; Neuper, Christa

2012-01-01

In a conventional brain–computer interface (BCI) system, users perform mental tasks that yield specific patterns of brain activity. A pattern recognition system determines which brain activity pattern a user is producing and thereby infers the user’s mental task, allowing users to send messages or commands through brain activity alone. Unfortunately, despite extensive research to improve classification accuracy, BCIs almost always exhibit errors, which are sometimes so severe that effective communication is impossible. We recently introduced a new idea to improve accuracy, especially for users with poor performance. In an offline simulation of a “hybrid” BCI, subjects performed two mental tasks independently and then simultaneously. This hybrid BCI could use two different types of brain signals common in BCIs – event-related desynchronization (ERD) and steady-state evoked potentials (SSEPs). This study suggested that such a hybrid BCI is feasible. Here, we re-analyzed the data from our initial study. We explored eight different signal processing methods that aimed to improve classification and further assess both the causes and the extent of the benefits of the hybrid condition. Most analyses showed that the improved methods described here yielded a statistically significant improvement over our initial study. Some of these improvements could be relevant to conventional BCIs as well. Moreover, the number of illiterates could be reduced with the hybrid condition. Results are also discussed in terms of dual task interference and relevance to protocol design in hybrid BCIs. PMID:20153371

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.

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…

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.

Monte Carlo Planning Method Estimates Planning Horizons during Interactive Social Exchange.

PubMed

Hula, Andreas; Montague, P Read; Dayan, Peter

2015-06-01

Reciprocating interactions represent a central feature of all human exchanges. They have been the target of various recent experiments, with healthy participants and psychiatric populations engaging as dyads in multi-round exchanges such as a repeated trust task. Behaviour in such exchanges involves complexities related to each agent's preference for equity with their partner, beliefs about the partner's appetite for equity, beliefs about the partner's model of their partner, and so on. Agents may also plan different numbers of steps into the future. Providing a computationally precise account of the behaviour is an essential step towards understanding what underlies choices. A natural framework for this is that of an interactive partially observable Markov decision process (IPOMDP). However, the various complexities make IPOMDPs inordinately computationally challenging. Here, we show how to approximate the solution for the multi-round trust task using a variant of the Monte-Carlo tree search algorithm. We demonstrate that the algorithm is efficient and effective, and therefore can be used to invert observations of behavioural choices. We use generated behaviour to elucidate the richness and sophistication of interactive inference.

Inverse reasoning processes in obsessive-compulsive disorder.

PubMed

Wong, Shiu F; Grisham, Jessica R

2017-04-01

The inference-based approach (IBA) is one cognitive model that aims to explain the aetiology and maintenance of obsessive-compulsive disorder (OCD). The model proposes that certain reasoning processes lead an individual with OCD to confuse an imagined possibility with an actual probability, a state termed inferential confusion. One such reasoning process is inverse reasoning, in which hypothetical causes form the basis of conclusions about reality. Although previous research has found associations between a self-report measure of inferential confusion and OCD symptoms, evidence of a specific association between inverse reasoning and OCD symptoms is lacking. In the present study, we developed a task-based measure of inverse reasoning in order to investigate whether performance on this task is associated with OCD symptoms in an online sample. The results provide some evidence for the IBA assertion: greater endorsement of inverse reasoning was significantly associated with OCD symptoms, even when controlling for general distress and OCD-related beliefs. Future research is needed to replicate this result in a clinical sample and to investigate a potential causal role for inverse reasoning in OCD. Copyright © 2016 Elsevier Ltd. All rights reserved.

Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature

PubMed Central

Murugesan, Gurusamy; Abdulkadhar, Sabenabanu; Natarajan, Jeyakumar

2017-01-01

Automatic extraction of protein-protein interaction (PPI) pairs from biomedical literature is a widely examined task in biological information extraction. Currently, many kernel based approaches such as linear kernel, tree kernel, graph kernel and combination of multiple kernels has achieved promising results in PPI task. However, most of these kernel methods fail to capture the semantic relation information between two entities. In this paper, we present a special type of tree kernel for PPI extraction which exploits both syntactic (structural) and semantic vectors information known as Distributed Smoothed Tree kernel (DSTK). DSTK comprises of distributed trees with syntactic information along with distributional semantic vectors representing semantic information of the sentences or phrases. To generate robust machine learning model composition of feature based kernel and DSTK were combined using ensemble support vector machine (SVM). Five different corpora (AIMed, BioInfer, HPRD50, IEPA, and LLL) were used for evaluating the performance of our system. Experimental results show that our system achieves better f-score with five different corpora compared to other state-of-the-art systems. PMID:29099838

Dynamic Causal Modeling of Preclinical Autosomal-Dominant Alzheimer's Disease.

PubMed

Penny, Will; Iglesias-Fuster, Jorge; Quiroz, Yakeel T; Lopera, Francisco Javier; Bobes, Maria A

2018-03-16

Dynamic causal modeling (DCM) is a framework for making inferences about changes in brain connectivity using neuroimaging data. We fitted DCMs to high-density EEG data from subjects performing a semantic picture matching task. The subjects are carriers of the PSEN1 mutation, which leads to early onset Alzheimer's disease, but at the time of EEG acquisition in 1999, these subjects were cognitively unimpaired. We asked 1) what is the optimal model architecture for explaining the event-related potentials in this population, 2) which connections are different between this Presymptomatic Carrier (PreC) group and a Non-Carrier (NonC) group performing the same task, and 3) which network connections are predictive of subsequent Mini-Mental State Exam (MMSE) trajectories. We found 1) a model with hierarchical rather than lateral connections between hemispheres to be optimal, 2) that a pathway from right inferotemporal cortex (IT) to left medial temporal lobe (MTL) was preferentially activated by incongruent items for subjects in the PreC group but not the NonC group, and 3) that increased effective connectivity among left MTL, right IT, and right MTL was predictive of subsequent MMSE scores.

Normative and descriptive accounts of the influence of power and contingency on causal judgement.

PubMed

Perales, José C; Shanks, David R

2003-08-01

The power PC theory (Cheng, 1997) is a normative account of causal inference, which predicts that causal judgements are based on the power p of a potential cause, where p is the cause-effect contingency normalized by the base rate of the effect. In three experiments we demonstrate that both cause-effect contingency and effect base-rate independently affect estimates in causal learning tasks. In Experiment 1, causal strength judgements were directly related to power p in a task in which the effect base-rate was manipulated across two positive and two negative contingency conditions. In Experiments 2 and 3 contingency manipulations affected causal estimates in several situations in which power p was held constant, contrary to the power PC theory's predictions. This latter effect cannot be explained by participants' conflation of reliability and causal strength, as Experiment 3 demonstrated independence of causal judgements and confidence. From a descriptive point of view, the data are compatible with Pearce's (1987) model, as well as with several other judgement rules, but not with the Rescorla-Wagner (Rescorla & Wagner, 1972) or power PC models.

Microdevelopment during an activity-based science lesson

NASA Astrophysics Data System (ADS)

Parziale, Jim

1997-11-01

The purpose of this study was to describe the microdevelopment of task-related skills during a classroom science activity. Pairs of fifth and pairs of seventh grade students were videotaped as they constructed marshmallow and toothpick bridges. A skill theory based system of analysis was developed and used to detect the construction of new understandings. Patterns of change observed in these understandings were used to infer three means of self-construction: shifts of focus, bridging mechanisms and distributed cognition. Shift of focus is a mechanism used by students to efficiently explore a web of possibilities, collect ideas and make observations for later coordination as new understandings. Bridging mechanisms are partially built conversational structures that scaffolded the construction of higher level thinking structures. Students used the distributed cognition mechanism to test the adaptiveness of their design ideas without the need to fully coordinate an understandings of these designs. An integrated model of these three mechanisms is proposed specific to this task. This model describes how these mechanisms spontaneously emerged and interacted to support the construction of mental representations.

Monte Carlo Planning Method Estimates Planning Horizons during Interactive Social Exchange

PubMed Central

Hula, Andreas; Montague, P. Read; Dayan, Peter

2015-01-01

Reciprocating interactions represent a central feature of all human exchanges. They have been the target of various recent experiments, with healthy participants and psychiatric populations engaging as dyads in multi-round exchanges such as a repeated trust task. Behaviour in such exchanges involves complexities related to each agent’s preference for equity with their partner, beliefs about the partner’s appetite for equity, beliefs about the partner’s model of their partner, and so on. Agents may also plan different numbers of steps into the future. Providing a computationally precise account of the behaviour is an essential step towards understanding what underlies choices. A natural framework for this is that of an interactive partially observable Markov decision process (IPOMDP). However, the various complexities make IPOMDPs inordinately computationally challenging. Here, we show how to approximate the solution for the multi-round trust task using a variant of the Monte-Carlo tree search algorithm. We demonstrate that the algorithm is efficient and effective, and therefore can be used to invert observations of behavioural choices. We use generated behaviour to elucidate the richness and sophistication of interactive inference. PMID:26053429

Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature.

PubMed

Murugesan, Gurusamy; Abdulkadhar, Sabenabanu; Natarajan, Jeyakumar

2017-01-01

Automatic extraction of protein-protein interaction (PPI) pairs from biomedical literature is a widely examined task in biological information extraction. Currently, many kernel based approaches such as linear kernel, tree kernel, graph kernel and combination of multiple kernels has achieved promising results in PPI task. However, most of these kernel methods fail to capture the semantic relation information between two entities. In this paper, we present a special type of tree kernel for PPI extraction which exploits both syntactic (structural) and semantic vectors information known as Distributed Smoothed Tree kernel (DSTK). DSTK comprises of distributed trees with syntactic information along with distributional semantic vectors representing semantic information of the sentences or phrases. To generate robust machine learning model composition of feature based kernel and DSTK were combined using ensemble support vector machine (SVM). Five different corpora (AIMed, BioInfer, HPRD50, IEPA, and LLL) were used for evaluating the performance of our system. Experimental results show that our system achieves better f-score with five different corpora compared to other state-of-the-art systems.

The nonlinear, complex sequential organization of behavior in schizophrenic patients: neurocognitive strategies and clinical correlations.

PubMed

Paulus, M P; Perry, W; Braff, D L

1999-09-01

Thought disorder is a hallmark of schizophrenia and can be inferred from disorganized behavior. Measures of the sequential organization of behavior are important because they reflect the cognitive processes of the selection and sequencing of behavioral elements, which generate observable and analyzable behavioral patterns. In this context, sequences of choices generated by schizophrenic patients in a two-choice guessing task fluctuate significantly, which reflects an "oscillating dysregulation" between highly predictable and highly unpredictable subsequences within a single test session. In this study, we aimed to clarify the significance of dysregulation by seeing whether demographic, clinical, neuropsychological, and psychological measures predict the degree of dysregulation observed on this two-choice task. Thirty schizophrenic patients repeatedly performed a LEFT or RIGHT key press that was followed by a stimulus, which occurred randomly on the left or right side of the computer screen. Thus, the stimulus location had nothing to do with the key press behavior. The range of key press sequence predictabilities as measured by the dynamical entropy was used to quantify the dysregulation of response sequences and reflects the range of fixity and randomness of the responses. A factor analysis was performed and step-wise multiple regression analyses were used to relate the factor scores to demographic, clinical, symptomatic, Wisconsin Card Sorting Test (WCST), and Rorschach variables. The LEFT/RIGHT key press sequences were determined by three factors: 1) the degree of win-stay/lose-shift strategy; 2) the degree of contextual influence on the current choice; and 3) the degree of dysregulation on the choice task. Demographic and clinical variables did not predict any of the three response patterns on the choice task. In contrast, the WCST and Rorschach test predicted performance on various factors of choice task response patterns. Schizophrenic patients employ several rules, i.e., "win-stay/lose-shift" and "decide according to the previous choice," that fluctuate significantly when generating sequences on this task, confirming that a basic behavioral dysregulation occurs in a single schizophrenic subject across a single test session. The organization or the "temporal architecture" of the behavioral sequences is not related to symptoms per se, but is related to deficits in executive functioning, problem solving, and perceptual organizational abilities.

BAIAP2 is related to emotional modulation of human memory strength.

PubMed

Luksys, Gediminas; Ackermann, Sandra; Coynel, David; Fastenrath, Matthias; Gschwind, Leo; Heck, Angela; Rasch, Bjoern; Spalek, Klara; Vogler, Christian; Papassotiropoulos, Andreas; de Quervain, Dominique

2014-01-01

Memory performance is the result of many distinct mental processes, such as memory encoding, forgetting, and modulation of memory strength by emotional arousal. These processes, which are subserved by partly distinct molecular profiles, are not always amenable to direct observation. Therefore, computational models can be used to make inferences about specific mental processes and to study their genetic underpinnings. Here we combined a computational model-based analysis of memory-related processes with high density genetic information derived from a genome-wide study in healthy young adults. After identifying the best-fitting model for a verbal memory task and estimating the best-fitting individual cognitive parameters, we found a common variant in the gene encoding the brain-specific angiogenesis inhibitor 1-associated protein 2 (BAIAP2) that was related to the model parameter reflecting modulation of verbal memory strength by negative valence. We also observed an association between the same genetic variant and a similar emotional modulation phenotype in a different population performing a picture memory task. Furthermore, using functional neuroimaging we found robust genotype-dependent differences in activity of the parahippocampal cortex that were specifically related to successful memory encoding of negative versus neutral information. Finally, we analyzed cortical gene expression data of 193 deceased subjects and detected significant BAIAP2 genotype-dependent differences in BAIAP2 mRNA levels. Our findings suggest that model-based dissociation of specific cognitive parameters can improve the understanding of genetic underpinnings of human learning and memory.

Review and Process Effects of Spontaneous Note-Taking on Text Comprehension.

PubMed

Slotte; Lonka

1999-01-01

This study examines how quantitative and qualitative differences in spontaneously taken notes are related to text comprehension in combination with reviewing or not reviewing previously made notes. High school graduates (N = 226) were allowed to take notes in any way they desired while reading a philosophical text. Approximately half the participants were told that they could review their notes during writing tasks designed to measure the ability to define, compare, and evaluate text content. The other half of the participants answered the subsequent questions without their notes. The process of taking notes was rated on the basis of note quality and quantity. The results revealed significant review and process effects in spontaneous note-taking. Reviewing the notes during essay-writing generally resulted in good performance in an exam calling for deep-level text comprehension. However, this review effect was mainly limited to detailed learning instead of making one's own inferences. Results pertaining to note quality indicated that the participants who summarized the content of the text resulted in better performance in all tasks in comparison with those who produced notes following the text order or verbatim notes. The amount of note-taking was also positively related to text comprehension. The discussion focuses upon the situational appropriateness of note-taking effects that pose challenges to educators. Copyright 1999 Academic Press.

Associative processing and paranormal belief.

PubMed

Gianotti, L R; Mohr, C; Pizzagalli, D; Lehmann, D; Brugger, P

2001-12-01

In the present study we introduce a novel task for the quantitative assessment of both originality and speed of individual associations. This 'BAG' (Bridge-the-Associative-Gap) task was used to investigate the relationships between creativity and paranormal belief. Twelve strong 'believers' and 12 strong 'skeptics' in paranormal phenomena were selected from a large student population (n > 350). Subjects were asked to produce single-word associations to word pairs. In 40 trials the two stimulus words were semantically indirectly related and in 40 other trials the words were semantically unrelated. Separately for these two stimulus types, response commonalities and association latencies were calculated. The main finding was that for unrelated stimuli, believers produced associations that were more original (had a lower frequency of occurrence in the group as a whole) than those of the skeptics. For the interpretation of the result we propose a model of association behavior that captures both 'positive' psychological aspects (i.e., verbal creativity) and 'negative' aspects (susceptibility to unfounded inferences), and outline its relevance for psychiatry. This model suggests that believers adopt a looser response criterion than skeptics when confronted with 'semantic noise'. Such a signal detection view of the presence/absence of judgments for loose semantic relations may help to elucidate the commonalities between creative thinking, paranormal belief and delusional ideation.

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.

Measuring Memory and Attention to Preview in Motion.

PubMed

Jagacinski, Richard J; Hammond, Gordon M; Rizzi, Emanuele

2017-08-01

Objective Use perceptual-motor responses to perturbations to reveal the spatio-temporal detail of memory for the recent past and attention to preview when participants track a winding roadway. Background Memory of the recently passed roadway can be inferred from feedback control models of the participants' manual movement patterns. Similarly, attention to preview of the upcoming roadway can be inferred from feedforward control models of manual movement patterns. Method Perturbation techniques were used to measure these memory and attention functions. Results In a laboratory tracking task, the bandwidth of lateral roadway deviations was found to primarily influence memory for the past roadway rather than attention to preview. A secondary auditory/verbal/vocal memory task resulted in higher velocity error and acceleration error in the tracking task but did not affect attention to preview. Attention to preview was affected by the frequency pattern of sinusoidal perturbations of the roadway. Conclusion Perturbation techniques permit measurement of the spatio-temporal span of memory and attention to preview that affect tracking a winding roadway. They also provide new ways to explore goal-directed forgetting and spatially distributed attention in the context of movement. More generally, these techniques provide sensitive measures of individual differences in cognitive aspects of action. Application Models of driving behavior and assessment of driving skill may benefit from more detailed spatio-temporal measurement of attention to preview.

EEG and Eye Tracking Demonstrate Vigilance Enhancement with Challenge Integration

PubMed Central

Bodala, Indu P.; Li, Junhua; Thakor, Nitish V.; Al-Nashash, Hasan

2016-01-01

Maintaining vigilance is possibly the first requirement for surveillance tasks where personnel are faced with monotonous yet intensive monitoring tasks. Decrement in vigilance in such situations could result in dangerous consequences such as accidents, loss of life and system failure. In this paper, we investigate the possibility to enhance vigilance or sustained attention using “challenge integration,” a strategy that integrates a primary task with challenging stimuli. A primary surveillance task (identifying an intruder in a simulated factory environment) and a challenge stimulus (periods of rain obscuring the surveillance scene) were employed to test the changes in vigilance levels. The effect of integrating challenging events (resulting from artificially simulated rain) into the task were compared to the initial monotonous phase. EEG and eye tracking data is collected and analyzed for n = 12 subjects. Frontal midline theta power and frontal theta to parietal alpha power ratio which are used as measures of engagement and attention allocation show an increase due to challenge integration (p < 0.05 in each case). Relative delta band power of EEG also shows statistically significant suppression on the frontoparietal and occipital cortices due to challenge integration (p < 0.05). Saccade amplitude, saccade velocity and blink rate obtained from eye tracking data exhibit statistically significant changes during the challenge phase of the experiment (p < 0.05 in each case). From the correlation analysis between the statistically significant measures of eye tracking and EEG, we infer that saccade amplitude and saccade velocity decrease with vigilance decrement along with frontal midline theta and frontal theta to parietal alpha ratio. Conversely, blink rate and relative delta power increase with vigilance decrement. However, these measures exhibit a reverse trend when challenge stimulus appears in the task suggesting vigilance enhancement. Moreover, the mean reaction time is lower for the challenge integrated phase (RTmean = 3.65 ± 1.4s) compared to initial monotonous phase without challenge (RTmean = 4.6 ± 2.7s). Our work shows that vigilance level, as assessed by response of these vital signs, is enhanced by challenge integration. PMID:27375464

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

PubMed Central

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

2017-01-01

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 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. PMID:28615794

Causal Inference for Cross-Modal Action Selection: A Computational Study in a Decision Making Framework.

PubMed

Daemi, Mehdi; Harris, Laurence R; Crawford, J Douglas

2016-01-01

Animals try to make sense of sensory information from multiple modalities by categorizing them into perceptions of individual or multiple external objects or internal concepts. For example, the brain constructs sensory, spatial representations of the locations of visual and auditory stimuli in the visual and auditory cortices based on retinal and cochlear stimulations. Currently, it is not known how the brain compares the temporal and spatial features of these sensory representations to decide whether they originate from the same or separate sources in space. Here, we propose a computational model of how the brain might solve such a task. We reduce the visual and auditory information to time-varying, finite-dimensional signals. We introduce controlled, leaky integrators as working memory that retains the sensory information for the limited time-course of task implementation. We propose our model within an evidence-based, decision-making framework, where the alternative plan units are saliency maps of space. A spatiotemporal similarity measure, computed directly from the unimodal signals, is suggested as the criterion to infer common or separate causes. We provide simulations that (1) validate our model against behavioral, experimental results in tasks where the participants were asked to report common or separate causes for cross-modal stimuli presented with arbitrary spatial and temporal disparities. (2) Predict the behavior in novel experiments where stimuli have different combinations of spatial, temporal, and reliability features. (3) Illustrate the dynamics of the proposed internal system. These results confirm our spatiotemporal similarity measure as a viable criterion for causal inference, and our decision-making framework as a viable mechanism for target selection, which may be used by the brain in cross-modal situations. Further, we suggest that a similar approach can be extended to other cognitive problems where working memory is a limiting factor, such as target selection among higher numbers of stimuli and selections among other modality combinations.

Sequential sensory and decision processing in posterior parietal cortex

PubMed Central

Ibos, Guilhem; Freedman, David J

2017-01-01

Decisions about the behavioral significance of sensory stimuli often require comparing sensory inference of what we are looking at to internal models of what we are looking for. Here, we test how neuronal selectivity for visual features is transformed into decision-related signals in posterior parietal cortex (area LIP). Monkeys performed a visual matching task that required them to detect target stimuli composed of conjunctions of color and motion-direction. Neuronal recordings from area LIP revealed two main findings. First, the sequential processing of visual features and the selection of target-stimuli suggest that LIP is involved in transforming sensory information into decision-related signals. Second, the patterns of color and motion selectivity and their impact on decision-related encoding suggest that LIP plays a role in detecting target stimuli by comparing bottom-up sensory inputs (what the monkeys were looking at) and top-down cognitive encoding inputs (what the monkeys were looking for). DOI: http://dx.doi.org/10.7554/eLife.23743.001 PMID:28418332

Complex Tasks Force Hand Laterality and Technological Behaviour in Naturalistically Housed Chimpanzees: Inferences in Hominin Evolution

PubMed Central

Mosquera, M.; Geribàs, N.; Bargalló, A.; Llorente, M.; Riba, D.

2012-01-01

Clear hand laterality patterns in humans are widely accepted. However, humans only elicit a significant hand laterality pattern when performing complementary role differentiation (CRD) tasks. Meanwhile, hand laterality in chimpanzees is weaker and controversial. Here we have reevaluated our results on hand laterality in chimpanzees housed in naturalistic environments at Fundació Mona (Spain) and Chimfunshi Wild Orphanage (Zambia). Our results show that the difference between hand laterality in humans and chimpanzees is not as great as once thought. Furthermore, we found a link between hand laterality and task complexity and also an even more interesting connection: CRD tasks elicited not only the hand laterality but also the use of tools. This paper aims to turn attention to the importance of this threefold connection in human evolution: the link between CRD tasks, hand laterality, and tool use, which has important evolutionary implications that may explain the development of complex behaviour in early hominins. PMID:22550466

Complex tasks force hand laterality and technological behaviour in naturalistically housed chimpanzees: inferences in hominin evolution.

PubMed

Mosquera, M; Geribàs, N; Bargalló, A; Llorente, M; Riba, D

2012-01-01

Clear hand laterality patterns in humans are widely accepted. However, humans only elicit a significant hand laterality pattern when performing complementary role differentiation (CRD) tasks. Meanwhile, hand laterality in chimpanzees is weaker and controversial. Here we have reevaluated our results on hand laterality in chimpanzees housed in naturalistic environments at Fundació Mona (Spain) and Chimfunshi Wild Orphanage (Zambia). Our results show that the difference between hand laterality in humans and chimpanzees is not as great as once thought. Furthermore, we found a link between hand laterality and task complexity and also an even more interesting connection: CRD tasks elicited not only the hand laterality but also the use of tools. This paper aims to turn attention to the importance of this threefold connection in human evolution: the link between CRD tasks, hand laterality, and tool use, which has important evolutionary implications that may explain the development of complex behaviour in early hominins.

Oral Motor Abilities Are Task Dependent: A Factor Analytic Approach to Performance Rate.

PubMed

Staiger, Anja; Schölderle, Theresa; Brendel, Bettina; Bötzel, Kai; Ziegler, Wolfram

2017-01-01

Measures of performance rates in speech-like or volitional nonspeech oral motor tasks are frequently used to draw inferences about articulation rate abnormalities in patients with neurologic movement disorders. The study objective was to investigate the structural relationship between rate measures of speech and of oral motor behaviors different from speech. A total of 130 patients with neurologic movement disorders and 130 healthy subjects participated in the study. Rate data was collected for oral reading (speech), rapid syllable repetition (speech-like), and rapid single articulator movements (nonspeech). The authors used factor analysis to determine whether the different rate variables reflect the same or distinct constructs. The behavioral data were most appropriately captured by a measurement model in which the different task types loaded onto separate latent variables. The data on oral motor performance rates show that speech tasks and oral motor tasks such as rapid syllable repetition or repetitive single articulator movements measure separate traits.

Knowledge-based reasoning in the Paladin tactical decision generation system

NASA Technical Reports Server (NTRS)

Chappell, Alan R.

1993-01-01

A real-time tactical decision generation system for air combat engagements, Paladin, has been developed. A pilot's job in air combat includes tasks that are largely symbolic. These symbolic tasks are generally performed through the application of experience and training (i.e. knowledge) gathered over years of flying a fighter aircraft. Two such tasks, situation assessment and throttle control, are identified and broken out in Paladin to be handled by specialized knowledge based systems. Knowledge pertaining to these tasks is encoded into rule-bases to provide the foundation for decisions. Paladin uses a custom built inference engine and a partitioned rule-base structure to give these symbolic results in real-time. This paper provides an overview of knowledge-based reasoning systems as a subset of rule-based systems. The knowledge used by Paladin in generating results as well as the system design for real-time execution is discussed.

A preliminary investigation of Stroop-related intrinsic connectivity in cocaine dependence: Associations with treatment outcomes

PubMed Central

Mitchell, Marci R.; Balodis, Iris M.; DeVito, Elise E.; Lacadie, Cheryl M.; Yeston, Jon; Scheinost, Dustin; Constable, R. Todd; Carroll, Kathleen M.; Potenza, Marc N.

2013-01-01

Background Cocaine-dependent individuals demonstrate neural and behavioral differences compared to healthy comparison subjects when performing the Stroop color-word inference test. Stroop measures also relate to treatment outcome for cocaine dependence. Intrinsic connectivity analyses assess the extent to which task-related regional brain activations are related to each other in the absence of defining a priori regions-of-interest. Objective This study examined: 1) the extent to which cocaine-dependent and non-addicted individuals differed on measures of intrinsic connectivity during fMRI Stroop performance; and, 2) the relationships between fMRI Stroop intrinsic connectivity and treatment outcome in cocaine dependence. Methods Sixteen treatment-seeking cocaine-dependent patients and matched non-addicted comparison subjects completed an fMRI Stroop task. Between-group differences in intrinsic connectivity were assessed and related to self-reported and urine-toxicology-based cocaine-abstinence measures. Results Cocaine-dependent patients vs. comparison subjects showed less intrinsic connectivity in cortical and sub-cortical regions. When adjusting for individual degree of intrinsic connectivity, cocaine-dependent vs. comparison subjects showed relatively greater intrinsic connectivity in the ventral striatum, putamen, inferior frontal gyrus, anterior insula, thalamus, and substantia nigra. Non-mean-adjusted intrinsic-connectivity measures in the midbrain, thalamus, ventral striatum, substantia nigra, insula, and hippocampus negatively correlated with measures of cocaine abstinence. Conclusion The diminished intrinsic connectivity in cocaine-dependent vs. comparison subjects suggests poorer communication across brain regions during cognitive-control processes. In mean-adjusted analyses, the cocaine-dependent group displayed relatively greater Stroop-related connectivity in regions implicated in motivational processes in addictions. The relationships between treatment outcomes and connectivity in the midbrain and basal ganglia suggest that connectivity represents a potential treatment target. PMID:24200209

Making Inferences in Adulthood: Falling Leaves Mean It's Fall.

ERIC Educational Resources Information Center

Zandi, Taher; Gregory, Monica E.

1988-01-01

Assessed age differences in making inferences from prose. Older adults correctly answered mean of 10 questions related to implicit information and 8 related to explicit information. Young adults answered mean of 7 implicit and 12 explicit information questions. In spite of poorer recall of factual details, older subjects made inferences to greater…

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…

An algebra-based method for inferring gene regulatory networks.

PubMed

Vera-Licona, Paola; Jarrah, Abdul; Garcia-Puente, Luis David; McGee, John; Laubenbacher, Reinhard

2014-03-26

The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the dynamic patterns present in the network. Boolean polynomial dynamical systems provide a powerful modeling framework for the reverse engineering of gene regulatory networks, that enables a rich mathematical structure on the model search space. A C++ implementation of the method, distributed under LPGL license, is available, together with the source code, at http://www.paola-vera-licona.net/Software/EARevEng/REACT.html.

A cognitive psychometric model for the psychodiagnostic assessment of memory-related deficits.

PubMed

Alexander, Gregory E; Satalich, Timothy A; Shankle, W Rodman; Batchelder, William H

2016-03-01

Clinical tests used for psychodiagnostic purposes, such as the well-known Alzheimer's Disease Assessment Scale: Cognitive subscale (ADAS-Cog), include a free-recall task. The free-recall task taps into latent cognitive processes associated with learning and memory components of human cognition, any of which might be impaired with the progression of Alzheimer's disease (AD). A Hidden Markov model of free recall is developed to measure latent cognitive processes used during the free-recall task. In return, these cognitive measurements give us insight into the degree to which normal cognitive functions are differentially impaired by medical conditions, such as AD and related disorders. The model is used to analyze the free-recall data obtained from healthy elderly participants, participants diagnosed as having mild cognitive impairment, and participants diagnosed with early AD. The model is specified hierarchically to handle item differences because of the serial position curve in free recall, as well as within-group individual differences in participants' recall abilities. Bayesian hierarchical inference is used to estimate the model. The model analysis suggests that the impaired patients have the following: (1) long-term memory encoding deficits, (2) short-term memory (STM) retrieval deficits for all but very short time intervals, (3) poorer transfer into long-term memory for items successfully retrieved from STM, and (4) poorer retention of items encoded into long-term memory after longer delays. Yet, impaired patients appear to have no deficit in immediate recall of encoded words in long-term memory or for very short time intervals in STM. (c) 2016 APA, all rights reserved).

Robot-assisted surgery: an emerging platform for human neuroscience research

PubMed Central

Jarc, Anthony M.; Nisky, Ilana

2015-01-01

Classic studies in human sensorimotor control use simplified tasks to uncover fundamental control strategies employed by the nervous system. Such simple tasks are critical for isolating specific features of motor, sensory, or cognitive processes, and for inferring causality between these features and observed behavioral changes. However, it remains unclear how these theories translate to complex sensorimotor tasks or to natural behaviors. Part of the difficulty in performing such experiments has been the lack of appropriate tools for measuring complex motor skills in real-world contexts. Robot-assisted surgery (RAS) provides an opportunity to overcome these challenges by enabling unobtrusive measurements of user behavior. In addition, a continuum of tasks with varying complexity—from simple tasks such as those in classic studies to highly complex tasks such as a surgical procedure—can be studied using RAS platforms. Finally, RAS includes a diverse participant population of inexperienced users all the way to expert surgeons. In this perspective, we illustrate how the characteristics of RAS systems make them compelling platforms to extend many theories in human neuroscience, as well as, to develop new theories altogether. PMID:26089785