2026-09-20T09:44:29Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1231172026-06-06T01:03:06Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Randall Davis.
Wang, Jing Xian,M. Eng.Massachusetts Institute of Technology.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-12-05T18:04:26Z
2019-12-05T18:04:26Z
2019
2019
https://hdl.handle.net/1721.1/123117
1128185885
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 67-68).
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.
by Jing Xian Wang.
M.Eng. in Computer Science and Engineering
M.Eng.inComputerScienceandEngineering Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
68 pages
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Tracking eye behavior in decision-making maze problems
Thesis
application/pdf
2019-12-05T18:04:25Z
Publication
Master
EECS
unknown
unknown
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<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.</Abstract>
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