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Testing predictive coding theories of autism spectrum disorder using models of active inference

Several competing neuro-computational theories of autism have emerged from predictive coding models of the brain. To disentangle their subtly different predictions about the nature of atypicalities in autistic perception, we performed computational modelling of two sensorimotor tasks: the predictive...

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Detalles Bibliográficos
Autores principales: Arthur, Tom, Vine, Sam, Buckingham, Gavin, Brosnan, Mark, Wilson, Mark, Harris, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10529610/
https://www.ncbi.nlm.nih.gov/pubmed/37695796
http://dx.doi.org/10.1371/journal.pcbi.1011473
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author Arthur, Tom
Vine, Sam
Buckingham, Gavin
Brosnan, Mark
Wilson, Mark
Harris, David
author_facet Arthur, Tom
Vine, Sam
Buckingham, Gavin
Brosnan, Mark
Wilson, Mark
Harris, David
author_sort Arthur, Tom
collection PubMed
description Several competing neuro-computational theories of autism have emerged from predictive coding models of the brain. To disentangle their subtly different predictions about the nature of atypicalities in autistic perception, we performed computational modelling of two sensorimotor tasks: the predictive use of manual gripping forces during object lifting and anticipatory eye movements during a naturalistic interception task. In contrast to some accounts, we found no evidence of chronic atypicalities in the use of priors or weighting of sensory information during object lifting. Differences in prior beliefs, rates of belief updating, and the precision weighting of prediction errors were, however, observed for anticipatory eye movements. Most notably, we observed autism-related difficulties in flexibly adapting learning rates in response to environmental change (i.e., volatility). These findings suggest that atypical encoding of precision and context-sensitive adjustments provide a better explanation of autistic perception than generic attenuation of priors or persistently high precision prediction errors. Our results did not, however, support previous suggestions that autistic people perceive their environment to be persistently volatile.
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spelling pubmed-105296102023-09-28 Testing predictive coding theories of autism spectrum disorder using models of active inference Arthur, Tom Vine, Sam Buckingham, Gavin Brosnan, Mark Wilson, Mark Harris, David PLoS Comput Biol Research Article Several competing neuro-computational theories of autism have emerged from predictive coding models of the brain. To disentangle their subtly different predictions about the nature of atypicalities in autistic perception, we performed computational modelling of two sensorimotor tasks: the predictive use of manual gripping forces during object lifting and anticipatory eye movements during a naturalistic interception task. In contrast to some accounts, we found no evidence of chronic atypicalities in the use of priors or weighting of sensory information during object lifting. Differences in prior beliefs, rates of belief updating, and the precision weighting of prediction errors were, however, observed for anticipatory eye movements. Most notably, we observed autism-related difficulties in flexibly adapting learning rates in response to environmental change (i.e., volatility). These findings suggest that atypical encoding of precision and context-sensitive adjustments provide a better explanation of autistic perception than generic attenuation of priors or persistently high precision prediction errors. Our results did not, however, support previous suggestions that autistic people perceive their environment to be persistently volatile. Public Library of Science 2023-09-11 /pmc/articles/PMC10529610/ /pubmed/37695796 http://dx.doi.org/10.1371/journal.pcbi.1011473 Text en © 2023 Arthur et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Arthur, Tom
Vine, Sam
Buckingham, Gavin
Brosnan, Mark
Wilson, Mark
Harris, David
Testing predictive coding theories of autism spectrum disorder using models of active inference
title Testing predictive coding theories of autism spectrum disorder using models of active inference
title_full Testing predictive coding theories of autism spectrum disorder using models of active inference
title_fullStr Testing predictive coding theories of autism spectrum disorder using models of active inference
title_full_unstemmed Testing predictive coding theories of autism spectrum disorder using models of active inference
title_short Testing predictive coding theories of autism spectrum disorder using models of active inference
title_sort testing predictive coding theories of autism spectrum disorder using models of active inference
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10529610/
https://www.ncbi.nlm.nih.gov/pubmed/37695796
http://dx.doi.org/10.1371/journal.pcbi.1011473
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