# Delineating proactive social behaviors in dynamic and multidimensional social space

> **NIH NIH R21** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2020 · $211,875

## Abstract

Project Summary
The human brain tracks multiplexed signals during social interactions. The breakdown of any of these
computations could lead to social deficits observed in many psychiatric disorders. While social neuroscience
has been growing rapidly in recent years, the complexity of human social interactions has not been well
quantified with computational models. Importantly, previous social neuroscience research generally assumes
that the structure of social environments are stochastic and social agents act in a reactive way, leaving at least
two knowledge gaps in the literature: 1) the proactive nature of social agents and 2) the dynamic and
multidimensional feature of social space. The overarching aim of this project is to develop novel computational
models and paradigms to capture social controllability and social navigation in ‘unselected’ human participants
(laboratory study n=100, mobile app n=10,000), which can ultimately be used to capture social failures across
disorders. In Aim 1, we will develop a novel generative model and paradigm for social controllability, based on
a rich literature on model-based decision-making and our previous work on social learning. Key subject-level
parameters include: simulated controllability (delta), future thinking weight (i.e. weight put on planning future
interactions), and learning rate (epsilon). In Aim 2, we will delineate navigational computations of dynamic
social relationships using a novel social interaction game in which participants interact and develop
relationships with virtual characters, we will devise novel measures that track the trajectories of social
relationships and geometrically quantify the overall structure of individuals’ two-dimensional social space
framed by power and affiliation. In Aim 3, we will use machine learning to 1) deep phenotype participants along
the dimensions of social controllability and navigation and 2) predict clinical and subclinical symptoms among a
large sample of ‘unfiltered’ volunteers. Upon successful completion of these aims, this proof-of-concept project
will provide important validation for new computational frameworks for social controllability and social
navigation, potentially breaking new grounds for computational psychiatry research of social dysfunction. The
resulting paradigms, models, and findings will be critical for a wide range of clinical disorders including
psychotic, mood, and personality disorders. Furthermore, the proposed paradigms can be back-translatable to
animal models, in relation to the social defeat model of depression and other animal models of social
behaviors. Thus, the proposed computational framework could have far-reaching influences that would exceed
the specific focus on social control and social space navigation, advancing the possibility to advance
mechanistic understanding of and develop individualized diagnosis and treatments across multiple psychiatric
disorders.

## Key facts

- **NIH application ID:** 9995592
- **Project number:** 5R21MH120789-02
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Xiaosi Gu
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $211,875
- **Award type:** 5
- **Project period:** 2019-08-15 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9995592

## Citation

> US National Institutes of Health, RePORTER application 9995592, Delineating proactive social behaviors in dynamic and multidimensional social space (5R21MH120789-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9995592. Licensed CC0.

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