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Autistic traits, Bayesian modeling, computational psychiatry, reward-based learning, social cognition, social gaze
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.
