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

ELife
Povilas KarvelisPeggy Seriès

Abstract

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.

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Citations

Jul 2, 2019·Brain : a Journal of Neurology·Vincent ValtonPeggy Seriès
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Sep 25, 2021·PLoS Computational Biology·Nikitas Angeletos ChrysaitisPeggy Seriès

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Software Mentioned

Matlab Psychophysics Toolbox
EstimationBias
NostimDetected
BayesEstimatedParams
Matlab
MATLAB script analyze _ params . m
MATLAB script analyze _ data . m
fminsearchbnd
MATLAB script

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