Egoistic versus Prosocial Decision Making: an Eye Movement Data Report

Area of Science:

• Behavioral Science
• Cognitive Neuroscience
• Human-Computer Interaction

Background:

• Eye tracking is a powerful tool for understanding human behavior.
• Decision-making processes are complex and influenced by visual attention.
• Existing datasets may not fully capture the nuances of oculomotor activity during decision tasks.

Purpose of the Study:

• To present a novel dataset of eye tracking metrics.
• To facilitate research in human behavior and decision-making.
• To support the development of computational models of visual attention.

Main Methods:

• Collected eye tracking data using SMI eye tracking glasses.
• Conducted fourteen experiments under individual and social decision-making conditions.
• Recorded visual behavior metrics and eye movement data in decision-relevant areas.

Main Results:

• The dataset includes 4,180 visual behavior metrics summaries.
• The dataset contains 3,744 eye movement records.
• Data covers both individual and social decision-making scenarios.

Conclusions:

• The presented eye tracking dataset is a valuable resource.
• This data can enhance computational models of decision processes.
• Findings contribute to understanding oculomotor activity in decision-making.