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

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.