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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. 

Abstract We know that reading involves coordination between textual characteristics and visual attention, but research linking eye movements during reading and comprehension assessed after reading is surprisingly limited, especially for reading long connected texts. We tested two competing possibilities: (a) the weak association hypothesis: Links between eye movements and comprehension are weak and short‐lived, versus (b) the strong association hypothesis: The two are robustly linked, even after a delay. Using a predictive modeling approach, we trained regression models to predict comprehension scores from global eye movement features, using participant‐level cross‐validation to ensure that the models generalize across participants. We used data from three studies in which readers (Ns = 104, 130, 147) answered multiple‐choice comprehension questions ~30 min after reading a 6,500‐word text, or after reading up to eight 1,000‐word texts. The models generated accurate predictions of participants' text comprehension scores (correlations between observed and predicted comprehension: 0.384, 0.362, 0.372,ps < .001), in line with the strong association hypothesis. We found that making more, but shorter fixations, consistently predicted comprehension across all studies. Furthermore, models trained on one study's data could successfully predict comprehension on the others, suggesting generalizability across studies. Collectively, these findings suggest that there is a robust link between eye movements and subsequent comprehension of a long connected text, thereby connecting theories of low‐level eye movements with those of higher order text processing during reading.