Automated Physiological-Based Detection of Mind Wandering during Learning

Blanchard, Nathaniel; Bixler, Robert; Joyce, Tera; D’Mello, Sidney K.

doi:10.1007/978-3-319-07221-0_7

Cited by 71 publications

(63 citation statements)

References 9 publications

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“…Previous work has identified some behavioral and physiological measures that are modulated by mind wandering. These include behavioral measures such as response times (McVay & Kane, 2009), physical posture (Seli et al, 2014), prosody (Drummond & Litman, 2010), reading speed , and physiological measures such as brain activity (Christoff, Gordon, Smallwood, Smith, & Schooler, 2009;Mittner et al, 2014;O'Connell et al, 2009;Smallwood, Beach, Schooler, & Handy, 2008;Weissman, Roberts, Visscher, & Woldorff, 2006), peripheral physiological responses (Blanchard, Bixler, Joyce, & D'Mello, 2014;Pham & Wang, 2015;Smallwood et al, 2004), eye movements (Foulsham, Farley, & Kingstone, 2013;Frank, Nara, Zavagnin, Touron, & Kane, 2015;Reichle, Reineberg, & Schooler, 2010;Uzzaman & Joordens, 2011), eye blinks (Frank et al, 2015;Grandchamp, Braboszcz, & Delorme, 2014;Smilek et al, 2010;Uzzaman & Joordens, 2011), and pupil diameter (Franklin, Broadway, Mrazek, Smallwood, & Schooler, 2013;Smallwood et al, 2011).…”

Section: Abstract Mind Wandering Reading Eye Gaze Machine Learningmentioning

confidence: 99%

An automated behavioral measure of mind wandering during computerized reading

2017

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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.,

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Section: Abstract Mind Wandering Reading Eye Gaze Machine Learningmentioning

confidence: 99%

An automated behavioral measure of mind wandering during computerized reading

2017

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“…Indeed, as noted earlier, across numerous studies, unguided thought has frequently been implied in researchers' conceptualizations of mind wandering (e.g., Blanchard et al, 2014;Carciofo et al, 2014;Christoff et al, 2016;Qu et al, 2015;Irving, 2016;Rummel & Boywitt, 2014; for more examples, see Seli, Risko, & Smilek, 2016a, Supplemental Materials), and such a conceptualization appears to be a sensible one.…”

Section: Thoughts That Are Unguidedmentioning

confidence: 83%

“…Indeed, intentionally occurring thoughts might seem to best reflect states wherein people actively guide their attention toward a task, which appear to be antithetical to our general conceptualization of mind wandering. It is presumably the case that, for this reason, some researchers have explicitly defined mind wandering in terms of unintentional thought (e.g., Blanchard et al, 2014;Carciofo et al, 2014;Qu et al, 2015;Rummel & Boywitt, 2014; for more examples, see Seli, Risko, & Smilek, 2016a, Supplemental Materials). One problem, however, with such a strict definition of mind wandering is that it excludes as cases of mind wandering situations in which people are at ease, sitting dreamily, allowing their thoughts to wander, or cases in which an individual deliberately neglects the task at hand in the service of entertaining TUT.…”

Section: The Intentionality Of Mind Wanderingmentioning

confidence: 99%

“…as 'mind wandering', in conflict with the authors' conception. Similarly, Blanchard et al (2014), although conceptualizing mind wandering as "unintentional lapses of attention" (p. 55), did not ensure that participants' reports of "mind wandering" were indeed accompanied by a lack of intention. Thus, the authors' operational definition allowed reports of "mind wandering" to include thoughts that were engaged intentionally, even though such thoughts were, according to the researchers, not reflective of mind wandering.…”

Section: Minimizing Confusions Arising From Different Definitions Of mentioning

confidence: 99%

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What does (and should) "mind wandering" mean?

Seli¹,

Maillet²,

Schacter³

et al. 2017

Preprint

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In recent years, there has been a tremendous increase in the number of studies examining mind wandering, and research on the topic has spread widely across various domains of psychological research. As research on the topic of mind wandering has accelerated, the defining features of this conscious state have expanded, and researchers have begun to define mind wandering in conceptually and operationally different ways between -and sometimes even within -studies.Yet, despite clear differences in the definitions adopted, 'mind wandering' is often discussed in broad terms, and inferences drawn by researchers are rarely constrained to their specific operational definitions. This practice produces a lack of clarity in our understanding of mind wandering, and it can lead to illusory inconsistencies in the literature. To minimize these problems, we propose that researchers adopt a family-resemblances approach to the investigation of mind wandering, which entails (a) treating mind wandering as a heterogeneous construct and (b) more clearly measuring and describing the specific aspects of the variety of mind wandering that researchers are attempting to investigate. To help move the field forward, we delineate a prototypical case of mind wandering in the broader context of related forms of thought, which should guide the use of the term in future research.

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“…We used head distance as a measure of body posture, because it indicates whether the person is leaning forward or backward with respect to his standard resting position. Inspired by the relevant literature on interest, boredom and emotions (D'Mello et al, 2012; Soleymani et al, 2012;Blanchard et al, 2014), 60 features were extracted (see Table 3). We calculated the correlation of interest, curiosity, coping, novelty, and complexity with the features extracted from the eye gaze behavior (see Table 4).…”

Section: Eye Gaze and Posturementioning

confidence: 99%

Behavioral and Physiological Responses to Visual Interest and Appraisals: Multimodal Analysis and Automatic Recognition

Soleymani

Mortillaro

2018

Front. ICT

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Interest drives our focus of attention and plays an important role in social communication. Given its relevance for many activities (e.g., learning, entertainment) a system able to automatically detect someone's interest has several potential applications. In this paper, we analyze the physiological and behavioral patterns associated with visual interest and present a method for the automatic recognition of interest, curiosity and their most relevant appraisals, namely, coping potential, novelty and complexity. We conducted an experiment in which participants watched images and micro-videos while multimodal signals were recorded-facial expressions, galvanic skin response (GSR), and eye gaze. After watching each stimulus, participants self-reported their level of interest, curiosity, coping potential, perceived novelty, and complexity. Results showed that interest was associated with other facial Action Units than smiling when dynamics was taken into consideration, especially inner brow raiser and eye lid tightener. Longer saccades were also present when participants watched interesting stimuli. However, correlations of appraisals with specific facial Action Units and eye gaze were in general stronger than those we found for interest. We trained Random Forests regression models to detect the level of interest, curiosity, and appraisals from multimodal features. The recognition models-unimodal and multimodal-for appraisals generally outperformed those for interest, in particular for static images. In summary, our study suggests that automatic appraisal detection may be a suitable way to detect subtle emotions like interest for which prototypical expressions do not exist.

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Automated Physiological-Based Detection of Mind Wandering during Learning

Cited by 71 publications

References 9 publications

An automated behavioral measure of mind wandering during computerized reading

An automated behavioral measure of mind wandering during computerized reading

What does (and should) "mind wandering" mean?

Behavioral and Physiological Responses to Visual Interest and Appraisals: Multimodal Analysis and Automatic Recognition

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