Toward Fully Automated Person-Independent Detection of Mind Wandering

Mind wandering is a ubiquitous phenomenon where attention involuntary shifts from task-related processing to task-unrelated thoughts. Mind wandering has negative effects on performance, hence, intelligent interfaces that detect mind wandering can intervene to restore attention to the current task. We investigated the use of eye gaze and contextual cues to automatically detect mind wandering during reading with a computer interface. Participants were pseudo-randomly probed to report mind wandering instances while an eye tracker recorded their gaze during a computerized reading task. Supervised machine learning techniques detected positive responses to mind wandering probes from gaze and context features in a user-independent fashion. Mind wandering was predicted with an accuracy of 72% (expected accuracy by chance was 62%) when probed at the end of a page and an accuracy of 59% (chance was 50%) when probed in the midst of reading a page. Possible improvements to the detectors and applications are discussed.