Tracking eye behavior in decision-making maze problems

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Wang, Jing Xian,M. Eng.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Randall Davis.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/123117

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123117