Automatic Detection of Mind Wandering During Reading Using Gaze and Physiology

Mind wandering (MW) entails an involuntary shift in attention from task-related thoughts to task-unrelated thoughts, and has been shown to have detrimental effects on performance in a number of contexts. This paper proposes an automated multimodal detector of MW using eye gaze and physiology (skin conductance and skin temperature) and aspects of the context (e.g., time on task, task difficulty). Data in the form of eye gaze and physiological signals were collected as 178 participants read four instructional texts from a computer interface. Participants periodically provided self-reports of MW in response to pseudorandom auditory probes during reading. Supervised machine learning models trained on features extracted from participants' gaze fixations, physiological signals, and contextual cues were used to detect pages where participants provided positive responses of MW to the auditory probes. Two methods of combining gaze and physiology features were explored. Feature level fusion entailed building a single model by combining feature vectors from individual modalities. Decision level fusion entailed building individual models for each modality and adjudicating amongst individual decisions. Feature level fusion resulted in an 11% improvement in classification accuracy over the best unimodal model, but there was no comparable improvement for decision level fusion. This was reflected by a small improvement in both precision and recall. An analysis of the features indicated that MW was associated with fewer and longer fixations and saccades, and a higher more deterministic skin temperature. Possible applications of the detector are discussed.