Exploring Eye Tracking as a Measure for Cognitive Load Detection in VR Locomotion

Eye tracking data has long been recognized as a reliable indicator of user cognitive load levels during human-computer interaction (HCI) tasks. However, its potential in the context of virtual reality (VR) remains relatively unexplored. Here, we present an ongoing study aimed at investigating the feasibility of detecting cognitive load in VR, particularly during VR locomotion, using an eye-tracking-based machine-learning approach. Data were collected using a within-subjects design, with participants performing VR locomotion tasks using five locomotion techniques. Our preliminary analyses validate the effectiveness of leveraging eye-tracking data as informative features in uncovering cognitive load in VR locomotion contexts, which motivates our further explorations.