Automatic Gaze-Based Detection Of Mind Wandering With Metacognitive Awareness

Mind wandering (MW) is a ubiquitous phenomenon where attention involuntarily shifts from task-related processing to task-unrelated thoughts. There is a need for adaptive systems that can reorient attention when MW is detected due to its detrimental effects on performance and productivity. This paper proposes an automated gaze-based detector of self-caught MW (i.e., when users become consciously aware that they are MW). Eye gaze data and self-reports of MW were collected as 178 users read four instructional texts from a computer interface. Supervised machine learning models trained on features extracted from users' gaze fixations were used to detect pages where users caught themselves MW. The best performing model achieved a user-independent kappa of .45 (accuracy of 74% compared to a chance accuracy of 52%); the first ever demonstration of a self-caught MW detector. An analysis of the features revealed that during MW, users made more regression fixations, had longer saccades that crossed lines more often, and had more uniform fixation durations, indicating a violation from normal reading patterns. Applications of the MW detector are discussed.