Neuro-computational mechanisms of social learning and variation along psychiatric symptom dimensions and in autism - PROJECT SUMMARY Social learning is key for acquiring knowledge about the world and for deciding how to act in social situations. By allowing individuals to learn the consequences of actions in the environment without having to directly experience them, social learning abilities are evolutionary advantageous. Yet they vary widely across individuals, and deficits are common in a number of disorders associated with impaired social function, such autism spectrum disorders. Autism affect nearly 2% of births in the US, and exhibits high comorbidity with other disorders, such as social anxiety. Symptoms in common include social withdrawal and difficulties navigating social interactions, and can cause challenges during clinical assessments and treatment decisions. To address these challenges, we need to provide a better characterization of the mechanisms underlying deficits in social function. This project aims to do so by developing a novel task battery assessing multiple aspects of social learning and an integrative neuro-computational framework dissecting the underlying mechanisms, both in the general population, in relation to symptom dimensions relevant to psychopathology (such as social anxiety), and in individuals with autism. During the K99 phase of the award, under the mentorship of Dr. John O’Doherty at Caltech, the candidate will receive training in advanced computational methods (with Dr. Yisong Yue), and in biological and clinical psychiatry (with Dr. Ralph Adolphs and Dr. Jamie Feusner). In Aim 1, the candidate will establish a hierarchical computational modelling framework to characterize social learning across a battery of three tasks - observational learning, social integration and dynamic trust learning. In Aim 2, a trans-diagnostic approach will be used in a large-scale online study to link individual differences in social learning computations with symptoms dimensions relevant to psychopathology, particularly autistic traits and social anxiety. During the R00 phase of the award, the candidate will combine her computational and psychiatry training to investigate social learning in individuals with autism and matched controls. Participants will complete the social learning task battery both behaviorally (Aim 3) and while undergoing fMRI (Aim 4). This will determine the task battery’s test-retest reliability, and identify social learning computations that are altered in autism and those that vary along relevant symptom dimensions identified in Aim 2. The fMRI findings will illuminate the neural computations and functional connectivity patterns associated with social learning and their alterations in autism. Using cutting-edge computational modelling and neuroimaging methods, this project will refine our understanding of social dysfunction and will contribute methodological and conceptual innovations to advance the burgeoning field of computational psychiatry. In the long term, such computational characterization of psychiatric deficits has the potential to inform policies and clinical interventions to improve social function. This Pathway to Independence award will allow the candidate to reach her training and research goals, form new collaborations, and overall paves the way for a successful transition to independence.
