We successfully classified attention states both within (person-dependent) and across (person-independent) individuals using event-related potential (ERP) measures. We recorded scalp EEG from participants as they performed an auditory target detection task and self-reported whether they were on task or mind wandering.
Here we examined whether electrophysiological measures can be used in machine learning models to accurately predict mind wandering states. Despite its prevalence and impact, studies to date have yet to identify robust behavioral signatures, making unobtrusive, yet reliable detection of mind wandering a difficult but important task for future applications. This universal phenomenon has been linked to numerous disruptive functional outcomes, including performance errors and negative affect. Mind wandering is often characterized by attention oriented away from an external task towards our internal, self-generated thoughts.
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