Automatic Gaze-Based Detection of Mind Wandering during Film Viewing

Mind wandering (MW) reflects a shift in attention from taskrelated to task-unrelated thoughts. It is negatively related to performance across a range of tasks, suggesting the importance of detecting and responding to MW in real-time. Currently, there is a paucity of research on MW detection in contexts other than reading. We addressed this gap by using eye gaze to automatically detect MW during narrative film comprehension, an activity that is used across a range of learning environments. In the current study, students self-reported MW as they watched a 32.5-minute commercial film. Students’ eye gaze was recorded with an eye tracker. Supervised machine learning models were used to detect MW using global (content-independent), local (contentdependent), and combined global+local features. We achieved a student-independent score (MW F1) of .45, which reflected a 29% improvement over a chance baseline. Models built using local features were more accurate than the global and combined models. An analysis of diagnostic features revealed that MW primarily manifested as a breakdown in attentional synchrony between eye gaze and visually salient areas of the screen. We consider limitations, applications, and refinements of the MW detector.