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Social Bayes: using Bayesian modeling to study autistic trait–related differences in social cognition


Sevgi, Meltem; Diaconescu, Andreea O; Tittgemeyer, Marc; Schilbach, Leonhard (2016). Social Bayes: using Bayesian modeling to study autistic trait–related differences in social cognition. Biological Psychiatry, 80(2):112-119.

Abstract

Background: Autism is characterized by impairments of social interaction, but the underlying subpersonal processes are still a matter of controversy. It has been suggested that the autistic spectrum might be characterized by alterations of the brain’s inference on the causes of socially relevant signals. However, it is unclear at what level of processing such trait-related alterations may occur.
Methods: We used a reward-based learning task that requires the integration of nonsocial and social cues in conjunction with computational modeling. Healthy subjects (N = 36) were selected based on their Autism Quotient Spectrum (AQ) score, and AQ scores were assessed for correlations with model parameters and task scores.
Results: Individual differences in AQ were inversely correlated with participants’ task scores (r = −.39, 95% confidence interval [CI] [−.68, −.13]). Moreover, AQ scores were significantly correlated with a social weighting parameter that indicated how strongly the decision was influenced by the social cue (r = −.42, 95% CI [−.66, −.19]), but not with other model parameters. Also, more pronounced social weighting was related to higher scores (r = .50, 95% CI [.20, .86]).
Conclusions: Our results demonstrate that higher autistic traits in healthy subjects are related to lower scores in a learning task that requires social cue integration. Computational modeling further demonstrates that these trait-related performance differences are not explained by an inability to process the social stimuli and its causes, but rather by the extent to which participants take into account social information during decision making.

Abstract

Background: Autism is characterized by impairments of social interaction, but the underlying subpersonal processes are still a matter of controversy. It has been suggested that the autistic spectrum might be characterized by alterations of the brain’s inference on the causes of socially relevant signals. However, it is unclear at what level of processing such trait-related alterations may occur.
Methods: We used a reward-based learning task that requires the integration of nonsocial and social cues in conjunction with computational modeling. Healthy subjects (N = 36) were selected based on their Autism Quotient Spectrum (AQ) score, and AQ scores were assessed for correlations with model parameters and task scores.
Results: Individual differences in AQ were inversely correlated with participants’ task scores (r = −.39, 95% confidence interval [CI] [−.68, −.13]). Moreover, AQ scores were significantly correlated with a social weighting parameter that indicated how strongly the decision was influenced by the social cue (r = −.42, 95% CI [−.66, −.19]), but not with other model parameters. Also, more pronounced social weighting was related to higher scores (r = .50, 95% CI [.20, .86]).
Conclusions: Our results demonstrate that higher autistic traits in healthy subjects are related to lower scores in a learning task that requires social cue integration. Computational modeling further demonstrates that these trait-related performance differences are not explained by an inability to process the social stimuli and its causes, but rather by the extent to which participants take into account social information during decision making.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Institute of Biomedical Engineering
Dewey Decimal Classification:170 Ethics
610 Medicine & health
Uncontrolled Keywords:Autistic traits; Bayesian modeling; Computational psychiatry; Reward-based learning; Social cognition; Social gaze
Language:English
Date:2016
Deposited On:29 Apr 2016 16:59
Last Modified:25 Jun 2016 01:02
Publisher:Elsevier
ISSN:0006-3223
Publisher DOI:https://doi.org/10.1016/j.biopsych.2015.11.025
PubMed ID:26831352

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