# Dissecting the features and neural mechanisms supporting naturalistic social inference

> **NIH NIH F32** · CALIFORNIA INSTITUTE OF TECHNOLOGY · 2022 · $67,582

## Abstract

Project Summary
 This first-time fellowship application proposes an integrated training and research plan focused on human
social interactions. The applicant has a strong and broad background in computational modeling of behavioral
human data, and will now leverage this in her postdoc at Caltech to learn how to model and analyze human
interactions from text-based chats and video interactions in behavior and in the brain (using fMRI). Aim 1 will
utilize state-of-the-art computer vision and natural language processing methods to annotate the visual, auditory
and text features of social interactions. Specific features, such as facial expressions and the semantic content
of text, will be extracted from 300 recorded social interactions between healthy adults. These features, in turn,
will be used to fit models that predict the social judgments that participants make about one another: how
trustworthy, friendly or arrogant do they judge their partner to be? A main goal of this first Aim is to extend human
social judgments beyond the typically narrow context used in past studies (e.g., static photos of faces) into the
naturalistic, interactive context we actually encounter in the real world. This emphasis will also be important for
future applications to psychiatric populations with critical deficits in social cognition (such as autism, outside the
scope of the present fellowship). Aim 2 translates the interactions of Aim 1 into neuroimaging, and BOLD-fMRI
will be acquired while participants watch previously recorded social interactions. This will reveal the brain regions
and networks that track the dynamic features of the stimuli. Neural representations of social inferences will be
extracted from multivariate activation patterns using representational similarity analysis, and similarity in neural
processing will be compared to similarity in behavioral social inference using inter-subject correlation analysis.
All analyses will both apply a whole-brain approach and will query specific social cognition networks. A main
goal of Aim 2 is to characterize the neural systems that subserve the dynamic construction of social attributions,
information that in future studies can be linked to individual differences and psychiatric illness.

## Key facts

- **NIH application ID:** 10465951
- **Project number:** 1F32MH130086-01
- **Recipient organization:** CALIFORNIA INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Nina Rouhani
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $67,582
- **Award type:** 1
- **Project period:** 2022-06-01 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10465951

## Citation

> US National Institutes of Health, RePORTER application 10465951, Dissecting the features and neural mechanisms supporting naturalistic social inference (1F32MH130086-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10465951. Licensed CC0.

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