An automated behavioral measure of mind wandering during computerized reading

Abstract

Mind wandering is a ubiquitous phenomenon in which attention shifts from task-related to task-unrelated thoughts. The last decade has witnessed an explosion of interest in mind wandering, but research has been stymied by a lack of objective measures, leading to a near-exclusive reliance on self-reports. We addressed this issue by developing an eye-gaze-based, machine-learned model of mind wandering during computerized reading. Data were collected in a study in which 132 participants reported self-caught mind wandering while reading excerpts from a book on a computer screen. A remote Tobii TX300 or T60 eyetracker recorded their gaze during reading. The data were used to train supervised classification models to discriminate between mind wandering and normal reading in a manner that would generalize to new participants. We found that at the point of maximal agreement between the model-based and self-reported mind-wandering means (smallest difference between the group-level means: M model = .310, M self = .319), the participant-level mind-wandering proportional distributions were similar and were significantly correlated (r = .400). The model-based estimates were internally consistent (r = .751) and predicted text comprehension more strongly than did self-reported mind wandering (rmodel = −.374, r self = −.208). Our results also indicate that a robust strategy of probabilistically predicting mind wandering in cases with poor or missing gaze data led to improved performance on all metrics, as compared to simply discarding these data. Our findings demonstrate that an automated objective measure might be available for laboratory studies of mind wandering during reading, providing an appealing alternative or complement to self-reports.Keywords Mind wandering . Reading . Eye gaze . Machine learning It is common for one's attention to shift toward spontaneously generated, task-unrelated thoughts. This phenomenon is called mind wandering. Numerous studies have investigated mind wandering across a range of tasks and have found that it occurs anywhere between 20%-50% of the time (Kane et al.,

Evaluation of Appearance-Based Methods and Implications for Gaze-Based Applications

“…Most intuitively, eye tracking techniques have been used to capture and infer user behaviours, such as eye contact [70] and daily activities [6,55]. Eye tracking data has been also used to recognise users' latent states, including interest and engagement [32,34], affective states [41], cognitive states [20,38], and attentive states [14,62]. It has been pointed out that eye tracking data can even be associated with mental disorders, such as Alzheimer's disease [23], Parkinson's disease [30], and schizophrenia [17].…”

Wandering eyes: Eye movements during mind wandering in video lectures

“…For example, the usual association between looking times and word properties (e.g., word frequency) was reduced during MW (Foulsham, Farley, & Kingstone, 2013;Reichle, Reineberg, & Schooler, 2010;Steindorf & Rummel, 2019); readers also tended to skip more words and perform fewer horizontal eye movements during MW (Bixler & D'Mello, 2016;Faber, Bixler, & D'Mello, 2018). These findings not only inspired theoretical accounts of how reading is disrupted during MW (e.g., Smallwood, 2011) but also facilitated the development of algorithms to detect MW during natural reading (e.g., Bixler & D'Mello, 2016;Faber et al, 2018). These successes point to the importance to expand this line of research to various lecture settings to better understand how the learner's attention is disrupted and how we can help learners recover from MW.…”

Predicting intentional and unintentional task unrelated thought with EEG

“…Ubiquitous both in daily life (Kane et al, 2007(Kane et al, , 2017Killingsworth & Gilbert, 2010;Poerio, Totterdell, & Miles, 2013;Risko, Anderson, Sarwal, Engelhardt, & Kingstone, 2012;Seli, Beaty, et al, 2018) and in the lab (McVay, Kane, & Kwapil, 2009;Smallwood, Davies, et al, 2004;Smallwood, Obonsawin, & Heim, 2003;Smallwood, O'Connor, Sudberry, Haskell, & Ballantyne, 2004;Varao-Sousa, Smilek, & Kingstone, 2018), these off-task experiences can vary along different dimensions such as content (Ruby, Smallwood, Engen, & Singer, 2013;Smallwood, Nind, & O'Connor, 2009;Smallwood & O'Connor, 2011), intrinsic or extrinsic constraints imposed on cognition (Christoff, Irving, Fox, Nathan Spreng, & Andrews-Hanna, 2016;Mills, Raffaelli, Irving, Stan, & Christoff, 2018), metacognitive awareness (Drescher, Van den Bussche, & Desender, 2018;Schooler, 2002;Schooler et al, 2011;Zedelius, Broadway, & Schooler, 2015) and degrees of intentionality (Martel, Arvaneh, Robertson, Smallwood, & Dockree, 2019;Robison & Unsworth, 2018;Seli, Ralph, Konishi, Smilek, & Schacter, 2017;Seli, Ralph, Risko, et al, 2017;Seli, Risko, Smilek, & Schacter, 2016;Seli, Wammes, Risko, & Smilek, 2015). As mind-wandering research gains more traction in cognitive research and neuroscience, efforts have recently been put forth to resolve the two key issues stymying scientific progress, the ontological uncertainty due to the apparent heterogeneity of the phenomenon Seli, Kane, Metzinger, et al, 2018;Seli, Kane, Smallwood, et al, 2018;Wang et al, 2018) and the paucity of objective measures (Faber, Bixler, & D'Mello, 2018;…”

“…leveraging the heightened metacognitive ability of expert meditators (Ellamil et al, 2016;Fox et al, 2012;Girn et al, 2017), or via a triangulation process of subjective, behavioral and neural correlates of off-task thought Smallwood & Schooler, 2015). Previous work identified several measures modulated by off-task states, including behavioral measures such as speech patterns (Drummond & Litman, 2010), response time (Bastian & Sackur, 2013;Cheyne, Carriere, & Smilek, 2006;Stawarczyk, Majerus, Maquet, & D'Argembeau, 2011)), body movement Farley, Risko, & Kingstone, 2013;Seli, Carriere, Thomson, et al, 2014), reading speed , and peripheral physiological measures such as eye movement (Frank, Nara, Zavagnin, Touron, & Kane, 2015;Reichle, Reineberg, & Schooler, 2010;Uzzaman & Joordens, 2011;Zhang, Miller, Sun, & Cortina, 2018), gaze (Faber et al, 2018;Huang, Li, Ngai, Leong, & Bulling, 2019;Mills, Bixler, Wang, & D'Mello, 2016), blinks (Grandchamp, Braboszcz, & Delorme, 2014;McIntire, McKinley, Goodyear, & McIntire, 2014;Smilek, Carriere, & Cheyne, 2010), pupil dilation (Franklin, Broadway, Mrazek, Smallwood, & Schooler, 2013;Konishi, Brown, Battaglini, & Smallwood, 2017;, and neurophysiological measures (Baird et al, 2014;Broadway et al, 2015/4;Jin et al, 2019;Kam et al, 2011;A. Martel, Arvaneh, Taylor, Dockree, & Robertson, 2017;Martel et al, 2019;O'Connell et al, 2009;Smallwood et al, 2008).…”

“…Research in that domain is inching ever closer to a general index of attention with studies identifying predictors of off-task states from oculometric data during breath-counting (Grandchamp et al, 2014) and reading (Faber et al, 2018), as well as from EEG data during a SART (Jin et al, 2019;Kawashima & Kumano, 2017;A. Martel et al, 2017) and simulated driving (Baldwin et al, 2017;Guo, Pan, Zhao, Cao, & Zhang, 2018).…”