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

> **NIH NIH R01** · GEORGE WASHINGTON UNIVERSITY · 2023 · $162,335

## Abstract

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 so...

## Key facts

- **NIH application ID:** 10688109
- **Project number:** 5R01MH132236-02
- **Recipient organization:** GEORGE WASHINGTON UNIVERSITY
- **Principal Investigator:** Gabriela Rosenblau
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $162,335
- **Award type:** 5
- **Project period:** 2022-08-22 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10688109

## Citation

> US National Institutes of Health, RePORTER application 10688109, CRCNS US-German Research Proposal: Efficient representations of social knowledge structures for learning from a computational, neural and psychiatric perspective (RepSocKnow) (5R01MH132236-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10688109. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*