# Eye tracking and computational approaches to understand the roles of maturation and experience in infant looking

> **NIH NIH F32** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2024 · $77,284

## Abstract

Project Summary/Abstract
Infants use their eyes to gather information from their environment and learn about the unique scenes and
objects that surround them. As infants accumulate these experiences, their cortex is concurrently maturing to
support more controlled attention and visual processes. However, it is unclear how much of the development of
gaze control reflects cortical maturation versus individual experiences. Therefore, the proposed project seeks
to evaluate three competing hypotheses regarding respective roles of maturation and experience. We will use
both traditional eye tracking analyses as well as advanced computational modeling. Specifically, in Aim 1, we
will record infant looking behaviors while viewing scenes and objects that range in familiarity and assess how
stimulus properties (e.g., salience, meaningfulness) relate to looking patterns for familiar and unfamiliar images
across the first year of life. In Aim 2, we will relate these looking behaviors to a convolutional neural network
inspired by the mature brain’s visual system. This innovative approach will evaluate the relationship between
infants’ looking behaviors and use of higher-level visual processing across contexts that vary in familiarity.
Ultimately, results from this proposal will inform developmental theories of visual attention by characterizing the
contributions of maturational versus experiential factors. The information gained from the results of this project
may facilitate future assessments and interventions of clinical populations with atypical visual scanning and
attention control. To successfully accomplish these aims, it is necessary that the applicant gains theoretical and
methodological expertise in both infant and adult attention research. This integrative perspective will allow her
to characterize infant attention and visual behaviors using sophisticated methodological and analytical
approaches, which will ultimately generate a wide range of subsequent research questions using similar
techniques. These aims will be most successfully completed under the supervision and mentorship of Drs. Lisa
Oakes and Steven Luck at the University of California, Davis, who are experts on attention in infant and adult
populations, respectively. The proposed training goals focus on expanding content knowledge of attention and
gaining methodological expertise in convolutional neural networks and representational similarity analysis.
Accomplishing these training goals will better prepare the applicant for a successful independent research
career that pushes the field of attention development forward.

## Key facts

- **NIH application ID:** 10898543
- **Project number:** 5F32EY034017-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Brianna Keenan Hunter
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $77,284
- **Award type:** 5
- **Project period:** 2023-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10898543

## Citation

> US National Institutes of Health, RePORTER application 10898543, Eye tracking and computational approaches to understand the roles of maturation and experience in infant looking (5F32EY034017-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10898543. Licensed CC0.

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