Publication Date

Spring 2024

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Nada Attar

Second Advisor

Robert Chun

Third Advisor

Sayma Akther

Keywords

Gender bias, Eye-tracking technology, Scene perception, Pupil dilation, Cognitive psychology, Tobii Pro Fusion, MATLAB, Python

Abstract

Gender bias deeply affects how we perceive and interact with the world around us. This study examined how gender bias affects scene perception by using eye-tracking technology. The relation between gender bias and pupil dilation is examined utilizing art as a stimuli and also studied the underlying cognitive processes. This experiment combines image presentations along with input from participants, drawing on previous research in gender bias, cognitive psychology, and eye-tracking methodologies. The experiment design showcases a series of images to participants, subtly replacing one image during the trial, and then asks participants whether the

replacement took place. This study uses MATLAB scripts alongside a Tobii Pro Fusion eye- tracker to monitor changes in pupil size, track eye movements, and record participant responses.

The data collected is then analyzed with Python to uncover underlying biases in perception and decision-making. This research not only deepens our understanding of gender bias but also lays the groundwork for future studies in this field.

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Available for download on Sunday, May 25, 2025

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