Crossed Eyes: Domain Adaptation for Gaze-Based Mind Wandering Models

The effectiveness of user interfaces are limited by the tendency for the human mind to wander. Intelligent user interfaces can combat this by detecting when mind wandering occurs and attempting to regain user attention through a variety of intervention strategies. However, collecting data to build mind wandering detection models can be expensive, especially considering the variety of media available and potential differences in mind wandering across them. We explored the possibility of using eye gaze to build cross-domain models of mind wandering where models trained on data from users in one domain are used for different users in another domain. We built supervised classification models using a dataset of 132 users whose mind wandering reports were collected in response to thought-probes while they completed tasks from seven different domains for six minutes each (five domains are investigated here: Illustrated Text, Narrative Film, Video Lecture, Naturalistic Scene, and Reading Text). We used global eye gaze features to build within- and cross- domain models using 5-fold user-independent cross validation. The best performing within-domain models yielded AUROCs ranging from .57 to .72, which were comparable for the cross-domain models (AUROCs of .56 to .68). Models built from coarse-grained locality features capturing the spatial distribution of gaze resulted in slightly better transfer on average (transfer ratios of .61 vs .54 for global models) due to improved performance in certain domains. Instance-based and feature-level domain adaptation did not result in any improvements in transfer. We found that seven gaze features likely contributed to transfer as they were among the top ten features for at least four domains. Our results indicate that gaze features are suitable for domain adaptation from similar domains, but more research is needed to improve domain adaptation between more dissimilar domains.