Gabriela Rosenblau1, Christoph W Korn2, Abigail Dutton3, Daeyeol Lee4, Kevin A Pelphrey5. 1. Center for Translational Developmental Neuroscience, Yale Child Study Center, Yale University, New Haven, Connecticut; Autism and Neurodevelopmental Disorders Institute, George Washington University, Washington, DC; Department of Psychological and Brain Sciences, George Washington University, Washington DC. Electronic address: grosenblau@gwu.edu. 2. Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. 3. Center for Translational Developmental Neuroscience, Yale Child Study Center, Yale University, New Haven, Connecticut. 4. Zanvyl Krieger Mind/Brain Institute, Department of Neuroscience, Department of Psychological and Brain Sciences, Johns Hopkins University, Maryland. 5. Center for Translational Developmental Neuroscience, Yale Child Study Center, Yale University, New Haven, Connecticut; Department of Neurology, University of Virginia, Charlottesville, Virginia.
Abstract
BACKGROUND: Many of our efforts in social interactions are dedicated to learning about others. Adolescents with autism have core deficits in social learning, but a mechanistic understanding of these deficits and how they relate to neural development is lacking. The present study aimed to specify how adolescents with and without autism represent and acquire social knowledge and how these processes are implemented in neural activity. METHODS: Typically developing adolescents (n = 26) and adolescents with autism spectrum disorder (ASD) (n = 20) rated in the magnetic resonance scanner how much 3 peers liked a variety of items and received trial-by-trial feedback about the peers' actual preference ratings. In a separate study, we established the preferences of a new sample of adolescents (N = 99), used to examine population preference structures. Using computational models, we tested whether participants in the magnetic resonance study relied on preference structures during learning and how model predictions were implemented in brain activity. RESULTS: Typically developing adolescents relied on average population preferences and prediction error updating. Importantly, prediction error updating was scaled by the similarity between items. In contrast, preferences of adolescents with ASD were best described by a No-Learning model that relied only on the participant's own preferences for each item. Model predictions were encoded in neural activity. Typically developing adolescents encoded prediction errors in the putamen, and adolescents with ASD showed greater encoding of own preferences in the angular gyrus. CONCLUSIONS: We specified how adolescents represent and update social knowledge during learning. Our findings indicate that adolescents with ASD rely only on their own preferences when making social inferences.
BACKGROUND: Many of our efforts in social interactions are dedicated to learning about others. Adolescents with autism have core deficits in social learning, but a mechanistic understanding of these deficits and how they relate to neural development is lacking. The present study aimed to specify how adolescents with and without autism represent and acquire social knowledge and how these processes are implemented in neural activity. METHODS: Typically developing adolescents (n = 26) and adolescents with autism spectrum disorder (ASD) (n = 20) rated in the magnetic resonance scanner how much 3 peers liked a variety of items and received trial-by-trial feedback about the peers' actual preference ratings. In a separate study, we established the preferences of a new sample of adolescents (N = 99), used to examine population preference structures. Using computational models, we tested whether participants in the magnetic resonance study relied on preference structures during learning and how model predictions were implemented in brain activity. RESULTS: Typically developing adolescents relied on average population preferences and prediction error updating. Importantly, prediction error updating was scaled by the similarity between items. In contrast, preferences of adolescents with ASD were best described by a No-Learning model that relied only on the participant's own preferences for each item. Model predictions were encoded in neural activity. Typically developing adolescents encoded prediction errors in the putamen, and adolescents with ASD showed greater encoding of own preferences in the angular gyrus. CONCLUSIONS: We specified how adolescents represent and update social knowledge during learning. Our findings indicate that adolescents with ASD rely only on their own preferences when making social inferences.