CRCNS US-German Research Proposal: Efficient representations of social knowledge structures for learning from a computational, neural and psychiatric perspective (RepSocKnow)

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PROJECT DESCRIPTION US-German Research Proposal for Collaboration in Computational Neuroscience: Efficient representations of social knowledge structures for learning from a computational, neural and psychiatric perspective (RepSocKnow) US-side PI: Prof. Gabriela Rosenblau, Ph.D., Assistant Professor, Department of Psychology, The George Washington University, 2115 G Street NW, Washington, DC 20052 German-side PI: Prof. Christoph W. Korn, Ph.D., Assistant Professor & PI Emmy-Noether Group, Section Social Neuroscience, Department of General Psychiatry, University of Heidelberg, Vossstraße 4, 69115 Heidelberg, Germany Consultant 1: Prof. Daniela Schiller, Ph.D., Associate Professor, Department of Psychiatry, Department of Neuroscience, and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York 10029, NY Consultant 2: Jan Gläscher, Ph.D., PI Bernstein Research Group, Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany 1. Aims and hypotheses This is a resubmission of our last year's CRCNS proposal, which received good and very good scores from reviewers. Reviewers were excited about the general neuro-computational approach that builds on the complementary skills of the PIs. In this revision, we address the reviewers' requests for clearer descriptions of the planed experiments and analyses. Importantly, our new proposal has direct relevance for clinical practice. By leveraging ideas from computational psychiatry [1–4] and from the Research Domain Criteria (RDoC; e.g., [5, 6]), we aim to apply our neuro-computational approach to improve the understanding of core social deficits shared by many pervasive neuro-psychiatric disorders. The general goal of this proposal is to establish comprehensive—and clinically relevant—neuro-computational models of aberrant learning in social contexts via behavioral and functional magnetic resonance imaging (fMRI) experiments. Learning about others is crucial for successful social interactions [7]. Social interactions strongly predict wellbeing [8]. Different types of impairments in social functioning accompany a variety of clinical conditions and constitute core symptoms of Autism Spectrum Disorders (ASD) [9–11] and Personality Disorders with a Borderline pattern qualifier (BPD) [12–16]. We harness our neuro-computational approach to investigate how social knowledge structures shape—and in turn are shaped by—learning about others. The mechanisms underlying knowledge representations and learning that we propose in our computational modeling approach are not “social” per se and we deem it a strength that they can be applied to learning across various (non-)social domains. Here, we focus on social learning to specify commonalities and differences between healthy individuals and individuals with marked social deficits associated with ASD or BPD. While these two clinical groups probably both employ overly rigid social knowledge structures, they exhibit different types of malfunctioning: ASD are characterized by under-mentalizing, i.e., insufficient inferences about mental states of others—possibly due to poor or unspecific social knowledge[17–19]. In contrast, BPD show over- mentalizing and overly negative interpretations of others' personality or intentions[12, 13]. Our novel neuro-computational framework can improve the understanding social malfunctioning along dimensional and categorical psychiatric criteria of ASD and BPD. We aim to test and refine computational models that formalize adequate strategies for acquiring and employing social knowledge structures during learning. Our models offer normative perspectives on social learning by specifying how ideal agents could learn in our controlled but ecologically valid tasks. Thereby, we can quantify how much humans—particularly clinical populations characterized by pervasive social deficits—deviate from computationally specified “optimal” benchmark strategies. In conjunction, this project aims to reveal the neural computations underlying the representation of social knowledge structures and their flexible deployment during learning. The project addresses three specific aims: