Oculometric variations during mind wandering

Abstract

A significant body of literature supports the contention that pupil size varies depending on cognitive load, affective state, and level of drowsiness. Here we assessed whether oculometric measures such as gaze position, blink frequency and pupil size were correlated with the occurrence and time course of self-reported mind-wandering episodes. We recorded the pupil size of two subjects engaged in a monotonous breath counting task while keeping their eyes on a fixation cross. This task is conducive to producing mind-wandering episodes. Each subject performed ten 20-min sessions, for total duration of about 4 h. Subjects were instructed to report spontaneous mind-wandering episodes by pressing a button when they lost count of their breath. After each button press, subjects filled in a short questionnaire describing the characteristics of their mind-wandering episode. We observed larger pupil size during the breath-focusing period compared to the mind-wandering period (p < 0.01 for both subjects). Our findings contradict previous research showing a higher baseline pupil size during mind wandering episodes in visual tasks. We discuss possible explanations for this discrepancy. We also analyzed nine other oculometric measures including blink rate, blink duration and gaze position. We built a support vector machine (SVM) classifier and showed that mean pupil size was the most reliable predictor of mind wandering in both subjects. The classification accuracy of mind wandering data segments vs. breath-focusing data segments was 81% for the first subject and 77% for the second subject. Additionally, we analyzed oculometric measures in light of the phenomenological data collected in the questionnaires. We showed that how well subjects remembered their thoughts while mind wandering was positively correlated with pupil size (subject 1, p < 0.001; subject 2, p < 0.05). Feelings of well being were also positively correlated with pupil size (subject 1, p < 0.001; subject 2, p < 0.001). Our results suggest that oculometric data could be used as a neurocognitive marker of mind-wandering episodes.

Pupillary correlates of lapses of sustained attention

“…To our knowledge, only three prior studies have examined a potential link between changes in baseline pupil diameter and subjective attentional state by examining differences between on-and off-task states while simultaneously measuring baseline pupil diameter (Franklin, Broadway, Mrazek, Smallwood, & Schooler, 2013;Grandchamp, Braboszcz, & Delorme, 2014;Mittner et al, 2014). In one of these studies (Franklin et al, 2013) the authors found that off-task reports (thought to reflect mind-wandering) were associated with larger baseline pupil diameters than were on-task reports.…”

“…In one of these studies (Franklin et al, 2013) the authors found that off-task reports (thought to reflect mind-wandering) were associated with larger baseline pupil diameters than were on-task reports. However, both Grandchamp et al (2014) and Mittner et al (2014) found that off-task reports were associated with smaller baseline pupil diameters than were on-task reports. Thus, there are clear conflicting results in the literature.…”

“…Based on prior pupillometry work, which suggested a medium to large effect size (Franklin et al, 2013;Grandchamp et al, 2014;Mittner et al, 2014) we determined that a minimum sample size of 35 participants would be sufficient to find a medium effect size, with power of .80 and alpha set at .05 (two tailed). Participants were between the ages of 18 and 35 and received course credit for their participation.…”

An automated behavioral measure of mind wandering during computerized reading

“…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).…”

A Preliminary Study for Exploring High-speed Train Driver Fatigue Using Eye-gaze Cue

“…When data loss occurs, -1 along with other physically impossible values is recorded by the eye tracker. Data segments containing more than 40% of missing data points or blinks with duration greater than 4s within the analysis intervals were discarded [13].…”