# The neural computations underlying human social interaction recognition

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2024 · $624,461

## Abstract

Project Summary
The ability to perceive and understand social interactions is crucial to daily life, and characteristically altered in
autism. From a brief glance, we can effortlessly recognize whether people are interacting, whether the interaction
is cooperative or competitive, and its communicative intent. However, little is known about the neural basis of
these abilities. We recently identified a region in the posterior superior temporal sulcus (pSTS) that is selectively
engaged when viewing social interactions. This discovery, coupled with novel methodological and modeling
advances, creates an opportunity to investigate the neurocomputational underpinnings of social interactions. We
will measure fMRI, EEG, and computational modeling responses to both controlled and naturalistic stimuli to
investigate the neural basis of social interaction perception and understanding. Our central hypotheses are that
the pSTS is a key computational junction between the visual and conceptual representations of a social
interaction, and that it extracts these representations via two different computational mechanisms: bottom-up
pattern recognition (from visual information in body and motion-selective brain regions) vs. top-down cognitive
processes (based on input from the theory of mind network), respectively. Aim 1 will test for a neural hierarchy
of social interaction representations from visual primitives to abstract concepts. Using a condition-rich,
multimodal fMRI experiment, we will test the working hypothesis that social interactions are processed
hierarchically along a ‘third visual pathway’: with social primitives represented in body and motion-selective visual
regions, multimodal representations of social interactions in the pSTS, and higher-level social features along the
STS and theory of mind network. Aim 2 will identify the direction of information flow across the social interaction
network. By combining EEG recordings with our fMRI data from Aim 1, we can investigate the relative timing of
information flow across brain regions to determine whether different aspects of a social interaction (from visual
to conceptual) are extracted in a bottom-up versus top-down manner. We hypothesize that social interaction
detection and goal-compatibility (i.e., cooperation vs. competition) will be coded early in the pSTS via bottom-up
information flow from visual regions. In contrast, we hypothesize that other social evaluations will be represented
significantly later based on additional input from the theory of mind network. Aim 3 will identify the neural
computations underlying social interaction representations. We will compare our neural recordings with bottom-
up (discriminative) and top-down (generative) computational models, which directly operationalize the neural
computational theories outlined above, to understand the computations carried out across the social interaction
brain network. The proposed studies will provide novel insights into the neural c...

## Key facts

- **NIH application ID:** 10806164
- **Project number:** 5R01MH132826-02
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Leyla Isik
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $624,461
- **Award type:** 5
- **Project period:** 2023-03-09 → 2028-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10806164

## Citation

> US National Institutes of Health, RePORTER application 10806164, The neural computations underlying human social interaction recognition (5R01MH132826-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10806164. Licensed CC0.

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