Mind wandering is a ubiquitous phenomenon in which attention shifts from task-related to task-unrelated thoughts. The last decade has witnessed an explosion of interest in mind wandering, but research has been stymied by a lack of objective measures, leading to a near-exclusive reliance on self-reports. We addressed this issue by developing an eye-gaze-based, machine-learned model of mind wandering during computerized reading. Data were collected in a study in which 132 participants reported self-caught mind wandering while reading excerpts from a book on a computer screen. A remote Tobii TX300 or T60 eyetracker recorded their gaze during reading. The data were used to train supervised classification models to discriminate between mind wandering and normal reading in a manner that would generalize to new participants. We found that at the point of maximal agreement between the model-based and self-reported mind-wandering means (smallest difference between the group-level means: M model = .310, M self = .319), the participant-level mind-wandering proportional distributions were similar and were significantly correlated (r = .400). The model-based estimates were internally consistent (r = .751) and predicted text comprehension more strongly than did self-reported mind wandering (rmodel = −.374, r self = −.208). Our results also indicate that a robust strategy of probabilistically predicting mind wandering in cases with poor or missing gaze data led to improved performance on all metrics, as compared to simply discarding these data. Our findings demonstrate that an automated objective measure might be available for laboratory studies of mind wandering during reading, providing an appealing alternative or complement to self-reports.Keywords Mind wandering . Reading . Eye gaze . Machine learning It is common for one's attention to shift toward spontaneously generated, task-unrelated thoughts. This phenomenon is called mind wandering. Numerous studies have investigated mind wandering across a range of tasks and have found that it occurs anywhere between 20%-50% of the time (Kane et al.,

Mind wandering is a ubiquitous phenomenon where attention involuntarily shifts from task-related thoughts to internal task-unrelated thoughts. Mind wandering can have negative effects on performance; hence, intelligent interfaces that detect mind wandering can improve performance by intervening and restoring 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 pseudorandomly probed to report mind wandering while an eye tracker recorded their gaze during the reading task. Supervised machine learning techniques detected positive responses to mind wandering probes from eye gaze and context features in a userindependent fashion. Mind wandering was detected with an accuracy of 72 % (expected accuracy by chance was 60 %) when probed at the end of a page and an accuracy of 67 % (chance was 59 %) when probed in the midst of reading a page. Global gaze features (gaze patterns independent of content, such as fixation durations) were more effective than content-specific local gaze features. An analysis of the features revealed diagnostic patterns of eye gaze behavior during mind wandering: (1) certain types of fixations were longer; (2) reading times were longer than expected; (3) more words were skipped; and (4) there was a larger variability in pupil diameter. Finally, the automatically detected mind wandering rate correlated negatively with measures of learning and transfer even after controlling for prior knowledge, thereby providing evidence of predictive validity. Possible improvements to the detector and applications that utilize the detector are discussed.

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During mind wandering, visual processing of external information is attenuated. Accordingly, mind wandering is associated with changes in gaze behaviors, albeit findings are inconsistent in the literature. This heterogeneity obfuscates a complete view of the moment-to-moment processing priorities of the visual system during mind wandering. We hypothesize that this observed heterogeneity is an effect of idiosyncrasy across tasks with varying spatial allocation demands, visual processing demands, and discourse processing demands and reflects a strategic, compensatory shift in how the visual system operates during mind wandering. We recorded eye movements and mind wandering (via thought-probes) as 132 college-aged adults completed a battery of 7 short (6 min) tasks with different visual demands. We found that for tasks requiring extensive sampling of the visual field, there were fewer fixations, and, depending on the specific task, fixations were longer and/or more dispersed. This suggests that visual sampling is sparser and potentially slower and more dispersed to compensate for the decreased sampling rate during mind wandering. For tasks that demand centrally focused gaze, mind wandering was accompanied by more exploratory eye movements, such as shorter and more dispersed fixations as well as larger saccades. Gaze behaviors were not reliably associated with mind wandering during a film comprehension task. These findings provide insight into how the visual system prioritizes external information when attention is focused inward and indicates the importance of task demands when assessing the relationship between eye movements, visual processing, and mind wandering.