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

眼动追踪和计算方法可用于了解成熟和经验在婴儿注视中的作用

基本信息

批准号：
10602809
负责人：
Brianna Keenan Hunter
金额：
$ 7.16万
依托单位：
UNIVERSITY OF CALIFORNIA AT DAVIS
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-01 至 2026-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10602809
关键词：
Adult Age Attention Behavior Behavioral Brain Brain region California Categories Cerebrum Child Cities Clinical Clutterings Complex Computer Models Development Environment Event Exposure to Eye Eye Movements Face Familiarity Future Goals Health Benefit Home Human Image Individual Individual Differences Infant Infant Development Instruction Intervention Knowledge Learning Life Link Mentorship Methodology Methods Modeling Neural Network Simulation Parents Pattern Persons Population Process Property Proxy Public Health Research Role Scanning Shapes Stimulus Stream Supervision Techniques Testing Training Universities Variant Vision Visual Visual System Visual attention Visual impairment Work attentional control career convolutional neural network cortical visual impairment experience experimental study gaze infancy information gathering innovation insight movement analysis prenatal sample fixation skills theories vision development visual process visual processing visual tracking

项目摘要

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.

项目总结/文摘