Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
Archival ReportNeurocognitive Mechanisms of Social Inferences in Typical and Autistic Adolescents
Section snippets
Methods and Materials
The study consisted of 2 assessments, an online preference survey in a large sample of adolescents (N = 99; 55 female; age, mean ± SD = 15.7 ± 1.4 years; age range = 11–18 years) to establish preferences of the adolescent population, and a functional magnetic resonance imaging (fMRI) experiment in a separate group of TD adolescents (n = 26; 11 female; age, mean ± SD = 13.7 ± 2.5 years; age range = 9–18 years) and adolescents with ASD (n = 20; 12 female; age, mean ± SD = 14.8 ± 2.8 years; age
Differences in Social Inferences Between Typical and Autistic Adolescents
We tested group differences in task performance with 2 different metrics: unsigned model-free PEs (the numerical differences between estimates and feedback) and open-answer descriptions of preference profiles. Average accuracy, i.e., average absolute model-free Pes, did not differentiate between TD adolescents and adolescents with ASD. Median model-free PEs were on average equally high (median for TD = 2.49; median for ASD = 2.49; H-test on median PEs: χ245 = 0.07, p = .790) and reduced over
Discussion
This study investigated how adolescents with and without autism build knowledge representations of their peers. We tested whether adolescents rely on preexisting social knowledge to make preference inferences and how their knowledge is updated during learning about a specific peer. Based on preference profiles from an independent sample of adolescents, we devised computational models that combine knowledge about peers at varying levels of complexity with PE updating. TD adolescents relied on a
Acknowledgments and Disclosures
This research was supported by the Hilibrand Fellowship for Autism Research at the Yale Child Study Center.
We thank Koen Frolichs for assisting us with data analysis.
This article was published as a preprint on bioRxiv: https://doi.org/10.1101/850552.
DL is a cofounder of Neurogazer. The other authors report no biomedical financial interests or potential conflicts of interest.
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