# Active Social Vision: How the Brain Processes Visual Information During Natural Social Perception

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $689,799

## Abstract

Summary/Abstract
 The dominant paradigm in social visual neuroscience has been to present simplified stimuli, often
consisting of static images in isolation presented without context, passively, to subjects required to maintain gaze
fixation. While much has been learned using this paradigm, it cannot capture all aspects of how social vision (SV)
works. Specifically, active observers combine motoric information gathering behaviors (eye and head movement)
to optimally sample perceptual information in a complex, natural multisensory social environment. Indeed, there
are few places where active vision is more critical than in the study of the neurobiology of SV. Real world SV is a
prototypic active sensing process where we use our experience, along with all of our sensing and interpretive
capacities to gather social and affective information from other people in a complex and dynamic setting. Simply
put, SV, as typically studied, does not adequately approximate actively interacting with friends, family, or your
doctor. Recent advances in computer vision, machine learning, and computational analysis now provide means
to attack a critical goal of social neuroscience: how the social and affective system guides active information
gathering in natural social contexts. We propose a novel natural approach to studying SV.
 This project will leverage a powerful technique to measure activity in the human brain: direct recording
from electrodes implanted in the brains of surgical epilepsy patients. Surgical epilepsy patients who spend 1-2
weeks in the hospital while having their brain activity monitored afford the unique opportunity to record
multiscale neural activity during natural interactions with friends, family, doctors, nurses, experimenters, etc. In
addition, patients will play a social game to allow for the study of real world SV using a semi-controlled and
repeatable task. Furthermore, participants will engage in more standard laboratory SV paradigms to assess how
results from real world conditions compare to the results from traditional experiments. The neural recordings
will be acquired simultaneously with video and audio monitoring, and eye tracking. State-of-the-art computer
vision analysis will provide a continuous assessment of social cues (e.g., eye gaze and facial expression) from
people with whom the patients are interacting. Computational analyses and mechanistic neural measurements
will determine the correspondence between what is perceived by the patient, the neural coding, and the
neurobiological implementation of that code. This approach will allow us to bridge across three critical levels of
analysis required for understanding the SV information processing systems. We hypothesize that real world SV
is an extended, iterative process that combines active, dynamic motor/attentional sampling strategies with prior
and contextual information to actively plan information gathering, which both enables and constrains the flow
of information thro...

## Key facts

- **NIH application ID:** 10784764
- **Project number:** 5R01MH132225-02
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Fernando De la Torre
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $689,799
- **Award type:** 5
- **Project period:** 2023-02-17 → 2027-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10784764

## Citation

> US National Institutes of Health, RePORTER application 10784764, Active Social Vision: How the Brain Processes Visual Information During Natural Social Perception (5R01MH132225-02). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10784764. Licensed CC0.

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