Tracking eye behavior in decision-making maze problems

Abstract

Neurodegenerative diseases, such as Alzheimer's and Huntington's, can significantly impact a patient's ability to solve everyday problems. The ability to detect early signs of mental decline is crucial for determining whether someone might be at risk for these diseases. Eye behavior is often correlated to cognitive load, so examining the behavior of the eyes during decision-making tasks could provide us with insights on how individuals think when solving challenging problems. But eye tracking data is notoriously noisy, so developing methods for cleaning, parsing, and visualizing the data is important for understanding what it means. By conducting eye tracking studies on healthy individuals using mazes of various difficulty levels and characteristics, I was able to gather, process, and examine gaze data to investigate patterns in eye behavior during decision-making problems.