Predicting Eye Movement and Fixation Patterns on Scenic Images Using Machine Learning for Children with Autism Spectrum Disorder

<p>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.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computational and Data Sciences

Year

2021

Author dc:creator

• Anden, Raymond

Contributors dc:contributor

• Erik Linstead
• Elizabeth Stevens
• Hesham El-Askary

Repository record dc:identifier

https://digitalcommons.chapman.edu/cads_dissertations/23

OAI identifier oai:identifier

oai:digitalcommons.chapman.edu:cads_dissertations-1023