Time Pressure Detection in a Visual Search Task Using Eye Tracking Metrics: A Virtual Reality Study

Abstract

Time pressure under a time constraint is a commonly found factor in complex domains that can impair visual search. Detecting when a user is subject to a time constraint is crucial for implementing timely interventions to counteract its detrimental effect on performance. Eye tracking, a non-intrusive method for recording eye movements, offers promising potential for time pressure detection. The present study investigates whether a classifier trained exclusively on eye tracking metrics can reliably classify if a user was under time pressure. For this study, eye tracking data from 40 participants was collected as they searched for objects in a virtual living room under different timing conditions and varying reward incentives, and 13 eye tracking metrics were calculated. The results showed that the support vector machine (SVM) classifier was the best-performing model with 0.82 AUROC, 74% accuracy, and 75% f1 score. This demonstrates the potential of eye tracking in detecting time pressure. These results, while promising, underline the importance of combining eye tracking with different physiological and behavioral measures to improve time pressure detection.