Publication Date

Spring 2023

Degree Type

Master's Project

Degree Name

Master of Science (MS)


Computer Science

First Advisor

Nada Attar

Second Advisor

Reem Albaghli

Third Advisor

Thomas Austin


gender bias, untraining bias, eye-tracking, male and female, cognitive load, human- computer interaction


In recent years, social cognitive theory has emphasized the role of cognitive processes in shaping perceptions and behavior related to gender bias. By examining the impact of targeted training interventions, this study seeks to better understand the influence of such processes on decision-making in the context of character selection. This human-computer interaction study explores the potential of intervention-based training to untraining gender bias in character selection. With an increasing need to address gender bias in various domains, understanding the impact of gender-based training becomes crucial. According to our hypothesis, exposure to masculine characters would boost people’s preference for female- intellectualized characters. Utilizing a two-part experiment, subjects were presented with a series of images across three blocks, with the second block providing gender-specific training. Experiment 1 focused on training with female characters, while Experiment 2 used male characters. The results demonstrated a significant increase in female character choices in Block 3 compared to Block 1, particularly for female-intellectualized characters in Experiment 2. Eye-tracking data further revealed slower response times and greater pupil size for female characters in Block 3 compared to Block 1 in both experiments, indicating higher cognitive load. These findings suggest that intervention-based training can effectively counter gender bias in character selection.