Neural signatures of social inferences predict the number of real-life social contacts and autism severity

Anita Tusche (), Robert P. Spunt, Lynn K. Paul, Julian M. Tyszka and Ralph Adolphs
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Anita Tusche: California Institute of Technology
Robert P. Spunt: California Institute of Technology
Lynn K. Paul: California Institute of Technology
Julian M. Tyszka: California Institute of Technology
Ralph Adolphs: California Institute of Technology

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: Abstract We regularly infer other people’s thoughts and feelings from observing their actions, but how this ability contributes to successful social behavior and interactions remains unknown. We show that neural activation patterns during social inferences obtained in the laboratory predict the number of social contacts in the real world, as measured by the social network index, in three neurotypical samples (total n = 126) and one sample of autistic adults (n = 23). We also show that brain patterns during social inference generalize across individuals in these groups. Cross-validated associations between brain activations and social inference localize selectively to the right posterior superior temporal sulcus and were specific for social, but not nonsocial, inference. Activation within this same brain region also predicts autism-like trait scores from questionnaires and autism symptom severity. Thus, neural activations produced while thinking about other people’s mental states predict variance in multiple indices of social functioning in the real world.

Date: 2023
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DOI: 10.1038/s41467-023-40078-3

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