What Eye Tracking Can Reveal about Dynamic Decision-Making

Abstract

While eye tracking can provide invaluable information on visual cognition, it is uncertain whether the pattern of one’s ocular behavior could reflect mental processes beyond the mere visual encoding of task-relevant information. The present study is concerned with the use of eye-movement measures as indicators of the cognitive processing involved in situation monitoring and dynamic decision-making tasks. In the context of a computer-controlled simulation of radar-based risk assessment, we monitored eye movements and extracted metrics relative to 1) scanpath, 2) eye fixations, and 3) pupillary response in order to predict the quality of decisions and time taken to classify aircraft displayed on a radar screen according to their threat level. Based on multiple regressions performed on almost 10,000 classifications, eye-tracking data can explain 77.9% of the variance in decision time but failed to predict classification accuracy. However, when regressions were applied to individual differences, eye movements can predict both classification time (69.2%) and accuracy (45.9%). While the analysis of scanpath and fixation duration is a good indicator of information seeking and can predict the time taken to make a decision, pupil dilation appears to be informative on the quality of that decision. These findings show how dynamic, event-based measures of eye movements could serve as an assessment method that goes beyond traditional usability testing and provide insights in the design of user interface and decision support systems.

Keywords: Dynamic decision-making, Eye movements, Mind-eye correspondence, Eye gaze, Pupil dilation, Microworld

DOI: 10.54941/ahfe100226