Infant Social Attention Associated with Elevated Likelihood for Autism Spectrum Disorder: A Multi-Method Comparison
Xiaoxue Fu1, Emma Platt2, Frederick Shic3,4
1Department of Psychology, University of South Carolina, Columbia, SC, USA. xiaoxuef@mailbox.sc.edu.
Journal of Autism and Developmental Disorders
|April 28, 2024
Summary
Eye tracking (ET) is a promising tool for identifying early autism spectrum disorder (ASD) markers in infants. This study found ET and manual coding (MC) measures comparable in detecting attention differences in infants at high familial risk for ASD.
Area of Science:
- Developmental psychology
- Neurodevelopmental disorders
- Biomedical engineering
Background:
- Early identification of autism spectrum disorder (ASD) is crucial for intervention.
- Eye tracking (ET) offers a sensitive method for measuring infant attention patterns.
- Data loss in ET can be a challenge, particularly in high-risk infant populations.
Purpose of the Study:
- To compare eye tracking (ET) and manual coding (MC) for assessing attention in infants with elevated (EL) and low (LL) likelihood of ASD.
- To evaluate the viability of ET for capturing atypical social attention in EL infants.
- To determine if ET can serve as a biomarker for ASD.
Main Methods:
- Prospective, longitudinal study design.
- Collected ET and MC data on social and nonsocial attention in 25 EL and 47 LL infants from 3 to 24 months.
- Analyzed data for group differences in attention patterns.
Main Results:
- ET achieved a high success rate (95.83%), with data loss comparable to MC.
- Significant positive association found between ET and MC attention measures.
- Comparable findings between ET and MC analyses revealed group differences in attention development between EL and LL infants.
Conclusions:
- Infant eye tracking (ET) is a viable and promising tool for early ASD detection.
- ET can identify distinct social attention patterns indicative of ASD risk.
- Findings support ET as a potential biomarker for identifying early markers associated with ASD likelihood.
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