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Social Bayes: Using Bayesian Modeling to Study Autistic Trait–Related Differences in Social Cognition


Sevgi, Meltem; Diaconescu, Andreea O; Henco, Lara; Tittgemeyer, Marc; Schilbach, Leonhard (2020). Social Bayes: Using Bayesian Modeling to Study Autistic Trait–Related Differences in Social Cognition. Biological Psychiatry, 87(2):185-193.

Abstract

Background: The autistic spectrum is characterized by profound impairments of social interaction. The exact subpersonal processes, however, that underlie the observable lack of social reciprocity are still a matter of substantial controversy. Recently, it has been suggested that the autistic spectrum might be characterized by alterations of the brain's inference about the causes of socially relevant sensory signals.

Methods: We used a novel reward-based learning task that required integration of nonsocial and social cues in conjunction with computational modeling. Thirty-six healthy subjects were selected based on their score on the Autism-Spectrum Quotient (AQ), and AQ scores were assessed for correlations with cue-related model parameters and task scores.

Results: Individual differences in AQ scores were significantly correlated with participants' total task scores, with high AQ scorers performing more poorly in the task (r = -.39, 95% confidence interval = -0.68 to -0.13). Computational modeling of the behavioral data unmasked a learning deficit in high AQ scorers, namely, the failure to integrate social context to adapt one's belief precision-the precision afforded to prior beliefs about changing states in the world-particularly in relation to the nonsocial cue.

Conclusions: More pronounced autistic traits in a group of healthy control subjects were related to lower scores associated with misintegration of the social cue. Computational modeling further demonstrated that these trait-related performance differences are not explained by an inability to process the social stimuli and their causes, but rather by the extent to which participants consider social information to infer the nonsocial cue.

Keywords: Autistic traits; Bayesian modeling; Computational psychiatry; Reward-based learning; Social cognition; Social gaze.

Abstract

Background: The autistic spectrum is characterized by profound impairments of social interaction. The exact subpersonal processes, however, that underlie the observable lack of social reciprocity are still a matter of substantial controversy. Recently, it has been suggested that the autistic spectrum might be characterized by alterations of the brain's inference about the causes of socially relevant sensory signals.

Methods: We used a novel reward-based learning task that required integration of nonsocial and social cues in conjunction with computational modeling. Thirty-six healthy subjects were selected based on their score on the Autism-Spectrum Quotient (AQ), and AQ scores were assessed for correlations with cue-related model parameters and task scores.

Results: Individual differences in AQ scores were significantly correlated with participants' total task scores, with high AQ scorers performing more poorly in the task (r = -.39, 95% confidence interval = -0.68 to -0.13). Computational modeling of the behavioral data unmasked a learning deficit in high AQ scorers, namely, the failure to integrate social context to adapt one's belief precision-the precision afforded to prior beliefs about changing states in the world-particularly in relation to the nonsocial cue.

Conclusions: More pronounced autistic traits in a group of healthy control subjects were related to lower scores associated with misintegration of the social cue. Computational modeling further demonstrated that these trait-related performance differences are not explained by an inability to process the social stimuli and their causes, but rather by the extent to which participants consider social information to infer the nonsocial cue.

Keywords: Autistic traits; Bayesian modeling; Computational psychiatry; Reward-based learning; Social cognition; Social gaze.

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Additional indexing

Item Type:Journal Article, refereed, further contribution
Communities & Collections:04 Faculty of Medicine > Institute of Biomedical Engineering
Dewey Decimal Classification:170 Ethics
610 Medicine & health
Scopus Subject Areas:Life Sciences > Biological Psychiatry
Uncontrolled Keywords:Biological Psychiatry
Language:English
Date:1 January 2020
Deposited On:07 Jan 2021 16:11
Last Modified:08 Jan 2021 21:00
Publisher:Elsevier
ISSN:0006-3223
OA Status:Hybrid
Publisher DOI:https://doi.org/10.1016/j.biopsych.2019.09.032
Related URLs:https://www.zora.uzh.ch/id/eprint/123875/
PubMed ID:31856957
Project Information:
  • : FunderSNSF
  • : Grant IDPZ00P3_167952
  • : Project TitleNeurocomputational Modelling of Delusions and its Clinical Utility for Psychosis

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