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Gender-based eye movement differences in passive indoor picture viewing: An eye-tracking study
Bahman Abdi Sargezeh1, Niloofar Tavakoli2, Mohammad Reza Daliri2
1Neuroscience and Neuroengineering Research Lab., Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science & Technology (IUST), Narmak, 16846-13114 Tehran, Iran; Electronics Engineering Department, School of Electrical Engineering, Iran University of Science & Technology (IUST), Narmak, 16846-13114 Tehran, Iran.
Physiology & Behavior
|March 30, 2019
Summary
Gender differences exist in how males and females visually explore indoor images. Females exhibit more exploratory gaze behavior, suggesting they process visual information differently than males.
Area of Science:
- Cognitive Neuroscience
- Visual Perception
- Human-Computer Interaction
Background:
- Brain structure differences between males and females can influence visual processing.
- Understanding gender-specific eye movement patterns is crucial for gaze-based research.
Purpose of the Study:
- To investigate gender differences in eye movement patterns during passive indoor picture viewing.
- To analyze various eye movement parameters like fixation duration and scan path length.
- To determine if eye movement data can classify gender with high accuracy.
Main Methods:
- An eye-tracking experiment was conducted with 25 males and 20 females.
- Participants viewed indoor pictures passively.
- Eye movement parameters analyzed included fixation duration, scan path length, saccade amplitude, and fixation-to-saccade duration ratio.
Main Results:
- Significant differences in eye movement patterns were observed between genders.
- Females displayed more explorative gaze behavior, characterized by larger saccade amplitudes and longer scan paths.
- Females had a shorter ratio of fixation duration to saccade duration, suggesting faster image inspection.
Conclusions:
- Males and females, even within the same culture, perceive their environment differently.
- Eye movement patterns can be used to classify gender with 70% accuracy using a support vector machine.
- Findings have significant implications for gaze-based research and models.
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