Gaze-based Prediction of Cognitive Load in Augmented Reality ETRA026

Cognitive load affects learning and task performance; specifically, increased cognitive load hinders an individual’s ability to process information. In augmented reality (AR) interfaces, distracting notifications can also heighten cognitive load. Integrated gaze tracking offers a non-intrusive way to monitor cognitive states and provides the opportunity to predict and adapt to changes in cognitive load. In this paper, we demonstrate how cognitive load prediction models can leverage built-in gaze-tracking data in AR to accurately predict cognitive load during search tasks. We collected gaze data from participants under both cognitively overloaded and non-overloaded states and analyzed gaze feature signatures to identify load-dependent patterns. We compared individual and group-trained models for predictive performance and generalizability. We initially used logistic regression, then tested tree-based ensemble models to improve performance. The best-performing XGBoost group model achieves a test AUC-ROC of 0.85. This work demonstrates robust cognitive load monitoring for AR tasks using built-in eye-tracking measurements.