scholarly journals Autistic traits, but not schizotypy, predict overweighting of sensory information in Bayesian visual integration

2017 ◽  
Author(s):  
Povilas Karvelis ◽  
Aaron R. Seitz ◽  
Stephen M. Lawrie ◽  
Peggy Seriès
Keyword(s):  
Bayesian Inference ◽  
Statistical Learning ◽  
Bayesian Modeling ◽  
Autistic Spectrum Disorder ◽  
Sensory Information ◽  
Autistic Traits ◽  
Veridical Perception ◽  
Visual Integration ◽  
The Brain ◽  
Healthy Participants

Recent theories propose that schizophrenia/schizotypy and autistic spectrum disorder are related to impairments in Bayesian inference i.e. how the brain integrates sensory information (likelihoods) with prior knowledge. However existing accounts fail to clarify: i) how proposed theories differ in accounts of ASD vs. schizophrenia and ii) whether the impairments result from weaker priors or enhanced likelihoods. Here, we directly address these issues by characterizing how 91 healthy participants, scored for autistic and schizotypal traits, implicitly learned and combined priors with sensory information. This was accomplished through a visual statistical learning paradigm designed to quantitatively assess variations in individuals’ likelihoods and priors. The acquisition of the priors was found to be intact along both traits spectra. However, autistic traits were associated with more veridical perception and weaker influence of expectations. Bayesian modeling revealed that this was due not to weaker prior expectations but to more precise sensory representations.

scholarly journals Autistic traits, but not schizotypy, predict increased weighting of sensory information in Bayesian visual integration

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Povilas Karvelis ◽  
Aaron R Seitz ◽  
Stephen M Lawrie ◽  
Peggy Seriès
Keyword(s):  
Bayesian Inference ◽  
Statistical Learning ◽  
Bayesian Modeling ◽  
Autistic Spectrum Disorder ◽  
Sensory Information ◽  
Autistic Traits ◽  
Veridical Perception ◽  
Visual Integration ◽  
The Brain ◽  
Healthy Participants

Recent theories propose that schizophrenia/schizotypy and autistic spectrum disorder are related to impairments in Bayesian inference that is, how the brain integrates sensory information (likelihoods) with prior knowledge. However existing accounts fail to clarify: (i) how proposed theories differ in accounts of ASD vs. schizophrenia and (ii) whether the impairments result from weaker priors or enhanced likelihoods. Here, we directly address these issues by characterizing how 91 healthy participants, scored for autistic and schizotypal traits, implicitly learned and combined priors with sensory information. This was accomplished through a visual statistical learning paradigm designed to quantitatively assess variations in individuals’ likelihoods and priors. The acquisition of the priors was found to be intact along both traits spectra. However, autistic traits were associated with more veridical perception and weaker influence of expectations. Bayesian modeling revealed that this was due, not to weaker prior expectations, but to more precise sensory representations.

scholarly journals Statistical Learning, Inductive Bias, and Bayesian Inference in Language Acquisition

2016 ◽  
Author(s):  
Lisa Pearl ◽  
Sharon Goldwater
Keyword(s):  
Bayesian Inference ◽  
Language Acquisition ◽  
Statistical Learning ◽  
Bayesian Modeling ◽  
Bayesian Models ◽  
Statistical Information ◽  
Historical Overview ◽  
Modeling Framework ◽  
Inductive Bias ◽  
Behavioral Evidence

Bayesian models of language acquisition are powerful tools for exploring how linguistic generalizations can be made. Notably, Bayesian models assume children leverage statistical information in sophisticated ways, and so it is important to demonstrate that children’s behavior is consistent with both the assumptions of the Bayesian framework and the predictions of specific models. We first provide a historical overview of behavioral evidence suggesting children utilize available statistical information to make useful generalizations in a variety of tasks. We then discuss the Bayesian modeling framework, including benefits of particular interest to both developmental and theoretical linguists. We conclude with a review of several case studies that demonstrate how Bayesian models can be applied to problems of interest in language acquisition.

scholarly journals Neural Correlates of Multisensory Reliability and Perceptual Weights Emerge at Early Latencies during Audio-visual Integration

2017 ◽  
Author(s):  
Stephanie C Boyle ◽  
Stephanie J Kayser ◽  
Christoph Kayser
Keyword(s):  
Temporal Dynamics ◽  
Sensory Information ◽  
Neural Correlates ◽  
Stimulus Onset ◽  
Neuronal Responses ◽  
Visual Integration ◽  
Neural Processes ◽  
Process Effects ◽  
The Brain ◽  
Perceptual Weights

To make accurate perceptual estimates observers must take the reliability of sensory information into account. Despite many behavioural studies showing that subjects weight individual sensory cues in proportion to their reliabilities, it is still unclear when during a trial neuronal responses are modulated by the reliability of sensory information, or when they reflect the perceptual weights attributed to each sensory input during decision making. We investigated these questions using a combination of psychophysics, EEG based neuroimaging and single-trial decoding. Our results show that the weighted integration of sensory information in the brain is a dynamic process; effects of sensory reliability on task-relevant EEG components were evident around 84ms after stimulus onset, while neural correlates of perceptual weights emerged around 120ms after stimulus onset. These neural processes also had different underlying topographies, arising from areas consistent with sensory and parietal regions. Together these results reveal the temporal dynamics of perceptual and neural audio-visual integration and support the notion of temporally early and functionally specific multisensory processes in the brain.

Relation Between Level of Prefrontal Activity and Subject's Performance

1999 ◽  
Vol 13 (2) ◽  
pp. 117-125 ◽  
Author(s):  
Laurence Casini ◽  
Françoise Macar ◽  
Marie-Hélène Giard
Keyword(s):  
Brain Activity ◽  
Sensory Information ◽  
Practice Phase ◽  
Trained Subjects ◽  
Anagram Task ◽  
Economic Information ◽  
Long Duration ◽  
Level Of Activity ◽  
The Brain ◽  
Processing Mechanism

Abstract The experiment reported here was aimed at determining whether the level of brain activity can be related to performance in trained subjects. Two tasks were compared: a temporal and a linguistic task. An array of four letters appeared on a screen. In the temporal task, subjects had to decide whether the letters remained on the screen for a short or a long duration as learned in a practice phase. In the linguistic task, they had to determine whether the four letters could form a word or not (anagram task). These tasks allowed us to compare the level of brain activity obtained in correct and incorrect responses. The current density measures recorded over prefrontal areas showed a relationship between the performance and the level of activity in the temporal task only. The level of activity obtained with correct responses was lower than that obtained with incorrect responses. This suggests that a good temporal performance could be the result of an efficacious, but economic, information-processing mechanism in the brain. In addition, the absence of this relation in the anagram task results in the question of whether this relation is specific to the processing of sensory information only.

Measuring the World

2018 ◽  
Author(s):  
Ann-Sophie Barwich
Keyword(s):  
Sensory Information ◽  
Network Models ◽  
Model System ◽  
Stimulus Input ◽  
Expectancy Effects ◽  
Neural Network Models ◽  
Perceptual Object ◽  
External Objects ◽  
The Common ◽  
The Brain

How much does stimulus input shape perception? The common-sense view is that our perceptions are representations of objects and their features and that the stimulus structures the perceptual object. The problem for this view concerns perceptual biases as responsible for distortions and the subjectivity of perceptual experience. These biases are increasingly studied as constitutive factors of brain processes in recent neuroscience. In neural network models the brain is said to cope with the plethora of sensory information by predicting stimulus regularities on the basis of previous experiences. Drawing on this development, this chapter analyses perceptions as processes. Looking at olfaction as a model system, it argues for the need to abandon a stimulus-centred perspective, where smells are thought of as stable percepts, computationally linked to external objects such as odorous molecules. Perception here is presented as a measure of changing signal ratios in an environment informed by expectancy effects from top-down processes.

scholarly journals Dysfunctions in Infants’ Statistical Learning are Related to Parental Autistic Traits

2021 ◽  
Author(s):  
Bettoni Roberta ◽  
Valentina Riva ◽  
Chiara Cantiani ◽  
Elena Maria Riboldi ◽  
Massimo Molteni ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Skills ◽  
Statistical Learning ◽  
Crucial Role ◽  
Autistic Traits ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Statistical Structure ◽  
The Relationship ◽  
Visual Habituation

AbstractStatistical learning refers to the ability to extract the statistical relations embedded in a sequence, and it plays a crucial role in the development of communicative and social skills that are impacted in the Autism Spectrum Disorder (ASD). Here, we investigated the relationship between infants’ SL ability and autistic traits in their parents. Using a visual habituation task, we tested infant offspring of adults (non-diagnosed) who show high (HAT infants) versus low (LAT infants) autistic traits. Results demonstrated that LAT infants learned the statistical structure embedded in a visual sequence, while HAT infants failed. Moreover, infants’ SL ability was related to autistic traits in their parents, further suggesting that early dysfunctions in SL might contribute to variabilities in ASD symptoms.

scholarly journals Introducing the StataStan Interface for Fast, Complex Bayesian Modeling Using Stan

2017 ◽  
Vol 17 (2) ◽  
pp. 330-342 ◽  
Author(s):  
Robert L. Grant ◽  
Bob Carpenter ◽  
Daniel C. Furr ◽  
Andrew Gelman
Keyword(s):  
Bayesian Inference ◽  
Item Response Theory ◽  
Open Source ◽  
Item Response ◽  
Bayesian Modeling ◽  
Inference Engine ◽  
Software Components ◽  
Statistical Software ◽  
Simulation Based ◽  
Item Response Theory Models

In this article, we present StataStan, an interface that allows simulation-based Bayesian inference in Stata via calls to Stan, the flexible, open-source Bayesian inference engine. Stan is written in C++, and Stata users can use the commands stan and windowsmonitor to run Stan programs from within Stata. We provide a brief overview of Bayesian algorithms, details of the commands available from Statistical Software Components, considerations for users who are new to Stan, and a simple example. Stan uses a different algorithm than bayesmh, BUGS, JAGS, SAS, and MLwiN. This algorithm provides considerable improvements in efficiency and speed. In a companion article, we give an extended comparison of StataStan and bayesmh in the context of item response theory models.

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