# Examining naturalistic social engagement: Using mobile eye-tracking to investigate individual differences and within-person variation in adolescent behavior, attention, and neural processing

> **NIH NIH F31** · PENNSYLVANIA STATE UNIVERSITY, THE · 2021 · $41,275

## Abstract

Project Summary/Abstract
 Peer relationships are highly influential in adolescent social development1,2 and may be protective against
anxiety3 and depression.4,5 Investigating markers of social engagement may elucidate targets for
understanding processes underlying individual socio-emotional trajectories. Two major gaps exist in the
literature on adolescent social engagement: 1) The behavioral and attentional mechanisms underlying
successful social engagement are not well understood. 2) Studies examining potential mechanisms a) rely on
computer-based tasks that emulate social interactions (e.g., cyberball,6 chatroom task,7,8 virtual school9) or b)
forgo measures of neural processing or eye-tracking in favor of more naturalistic social interactions. In the
current study, 13-year-olds will be paired with a novel peer and provided the opportunity to engage in a
naturalistic social interaction. During the social interaction, behavioral and mobile eye-tracking (MET) data will
be collected. Importantly, the mobile eye-tracking system will also capture each participant’s perspective of the
social interaction. Gaze measures will be used to examine social attention during a naturalistic interaction. The
perspective videos will be used to examine mentalizing system and threat circuit connectivity in response to
the naturalistic interaction. Specifically, participants will “re-live” the interaction in the scanner, watching both
positive and negative moments from both their, and their partner’s, perspectives. The design will enable the
identification of multiple markers of naturalistic social engagement and the research team will address three
main aims: 1) Identify individual differences in adolescent social attention and behavior during a naturalistic
peer interaction. 2) Examine individual differences in mentalizing system and threat circuit connectivity during a
re-experienced peer interaction. 3) Examine the potential moderating effect of social attention on individual
differences in mentalizing system and threat circuit connectivity patterns. Findings will provide valuable
information about markers of social engagement, paving the way for future work examining how these markers
may develop and interact to support the emergence of psychopathology, particularly anxiety.
 The current study is designed with an integrated training plan that will prepare the fellowship applicant for a
future career as a developmental neuroscientist. The three overarching training goals are: 1) Obtain training in
how to measure individual differences in adolescent peer interactions. 2) Obtain training in neuroscience
methodology and multilevel modeling. 3) Advance training and professional development in preparation for an
independent research career. These training goals will effectively prepare the applicant for the next steps in the
desired career path of becoming a developmental neuroscientist as well as lay the foundation for a future
research program examining how inter...

## Key facts

- **NIH application ID:** 10115522
- **Project number:** 5F31MH121035-02
- **Recipient organization:** PENNSYLVANIA STATE UNIVERSITY, THE
- **Principal Investigator:** Alicia Vallorani
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $41,275
- **Award type:** 5
- **Project period:** 2020-01-01 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10115522

## Citation

> US National Institutes of Health, RePORTER application 10115522, Examining naturalistic social engagement: Using mobile eye-tracking to investigate individual differences and within-person variation in adolescent behavior, attention, and neural processing (5F31MH121035-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10115522. Licensed CC0.

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