The prevalence of the acronym tl;dr (too long; didn't read) suggests that people intentionally disengage their attention from long sections of text. We studied this real-world phenomenon in an educational context by measuring rates of intentional and unintentional mind-wandering while undergraduate student participants (n = 80) read academic passages that were presented in either short sections of text (one sentence per screen)or relatively long sections (2-6 sentences per screen). We found that participants were significantly more likely tounintentionallydisengage their attention while reading the longer sections of text, whereas intentional mind-wandering rates were equivalent across short and long sections of text. The difference in unintentional mind-wandering rates suggests that section length may serve as a cue that people use to assess the cost-benefit tradeoffs involved in attending to (or disengaging from) text. We conclude that instructors should avoid presenting electronic reading material in long sections of text.

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 similar to 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.

We propose that machine-learned computational models (MLCMs), in which the model parameters and perhaps even structure are learned from data, can complement extant approaches to the study of text and discourse. Such models are particularly useful when theoretical understanding is insufficient, when the data are rife with nonlinearities and interactivity, and when researchers aspire to take advantage of big data. Being fully instantiated computer programs, MLCMs can also be used for autonomous assessment and real-time intervention. We illustrate these ideas in the context of an eye movement-based MLCM of textbase comprehension during reading along connected text. Using a dataset where 104 participants read a 6,500-word text, we trained Random Forests models to predict comprehension scores from six eye movement features. The models were highly accurate (area under the receiver operating characteristic curve = .902; r = .661), robust, and generalized across participants, suggesting possible use in future studies. We conclude by arguing for an increased role of MLCMs in the future of discourse research.

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.