Mind Wandering in a Multimodal Reading Setting: Behavior Analysis & Automatic Detection Using Eye-Tracking and an EDA Sensor

Mind wandering is a drift of attention away from the physical world and towards our thoughts and concerns. Mind wandering affects our cognitive state in ways that can foster creativity but hinder productivity. In the context of learning, mind wandering is primarily associated with lower performance. This study has two goals. First, we investigate the effects of text semantics and music on the frequency and type of mind wandering. Second, using eye-tracking and electrodermal features, we propose a novel technique for automatic, user-independent detection of mind wandering. We find that mind wandering was most frequent in texts for which readers had high expertise and that were combined with sad music. Furthermore, a significant increase in task-related thoughts was observed for texts for which readers had little prior knowledge. A Random Forest classification model yielded an [Formula: see text]-Score of 0.78 when using only electrodermal features to detect mind wandering, of 0.80 when using only eye-movement features, and of 0.83 when using both. Our findings pave the way for building applications which automatically detect events of mind wandering during reading.