Predicting Eye Movement and Fixation Patterns on Scenic Images Using Machine Learning for Children with Autism Spectrum Disorder
type: CreativeWork
description: This study uses eye-tracking experiment data to predict the fixation points for children with Autism Spectrum Disorder (ASD) and Typically Developing (TD) for 14 ASD and 14 TD subjects for 300 scenic images. Based on explanatory Logistic Regression models, it is evident that fixation patterns for both ASD and TD subjects focus near the center of each scenic image. Using gradient boosting the researchers successfully identify 31.7% and 39.5% of all fixation points in the top decile of predicted fixation points for ASD and TD subjects respectively. Results conclude that TD subjects have less variability in their eye movement and fixation points leading to increased accuracy in predicting where they will look.
abstract: This study uses eye-tracking experiment data to predict the fixation points for children with Autism Spectrum Disorder (ASD) and Typically Developing (TD) for 14 ASD and 14 TD subjects for 300 scenic images. Based on explanatory Logistic Regression models, it is evident that fixation patterns for both ASD and TD subjects focus near the center of each scenic image. Using gradient boosting the researchers successfully identify 31.7% and 39.5% of all fixation points in the top decile of predicted fixation points for ASD and TD subjects respectively. Results conclude that TD subjects have less variability in their eye movement and fixation points leading to increased accuracy in predicting where they will look.
author: Anden, Raymond
date published: 2021-08-01T07:00:00Z
date modified: 2026-07-24T01:38:16Z
provider: Chapman University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Chapman University