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| Mean duration of mind wandering for error trials in different conditions. (A) Mean duration of mind wandering from all participants, grouped by four conditions of trials. (B) Mean duration when participants made wrong responses was longer than that when they missed responses. (C) Mean duration for error trials without self-report was significantly longer than that with self-report. * p < 0.05. Error bars indicate ±SD.

| Mean duration of mind wandering for error trials in different conditions. (A) Mean duration of mind wandering from all participants, grouped by four conditions of trials. (B) Mean duration when participants made wrong responses was longer than that when they missed responses. (C) Mean duration for error trials without self-report was significantly longer than that with self-report. * p < 0.05. Error bars indicate ±SD.

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Article
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Mind wandering happens when one train of thought, related to a current undertaking, is interrupted by unrelated thoughts. The detection and evaluation of mind wandering can greatly help in understanding the attention control mechanism during certain focal tasks. Subjective assessments such as random thought-probe and spontaneous self-report are the...

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Context 1
... duration of mind wandering for error trials in the task obtained from all participants varied across 3-56.5 s (mean 22.3 ± 11.3 s). Furthermore, Figure 7A shows the mean duration of mind wandering for error trials in different conditions (wrong response or missed response, with self-report or without self-report). ...
Context 2
... between the missed response trials and the wrong response trials showed that there was no significant difference between the mind-wandering duration when the participants made wrong responses (26.9 s, SD = 13.6) and that when they missed responses [19.9 s, SD = 10.9; one-way ANOVA, F(1,7) = 1.513, p > 0.05], as shown in Figure 7B. In addition, the mean mind-wandering duration for error trials without self- report (30.6 s, SD = 12.8) was significantly longer than that with self-report [18.1 s, SD = 9.5; one-way ANOVA, F(1,11) = 4.882, p < 0.05], as shown in Figure 7C. ...
Context 3
... ANOVA, F(1,7) = 1.513, p > 0.05], as shown in Figure 7B. In addition, the mean mind-wandering duration for error trials without self- report (30.6 s, SD = 12.8) was significantly longer than that with self-report [18.1 s, SD = 9.5; one-way ANOVA, F(1,11) = 4.882, p < 0.05], as shown in Figure 7C. ...

Citations

... Previous successful attempts of mind wandering detection primarily used behavioral measures such as eye tracking and pupillometry [19][20][21][22] or task-related measures, such as driving performance [23,24] and reading time [25]. Studies have also used physiological measures such as heart rate and skin conductance [26] as well as synchronization between respiration and sensory pressure [27]. These findings serve to highlight the value of using behavioral and physiological measures to detect mind wandering at above chance levels. ...
Article
Full-text available
Mind wandering is often characterized by attention oriented away from an external task towards our internal, self-generated thoughts. This universal phenomenon has been linked to numerous disruptive functional outcomes, including performance errors and negative affect. Despite its prevalence and impact, studies to date have yet to identify robust behavioral signatures, making unobtrusive, yet reliable detection of mind wandering a difficult but important task for future applications. Here we examined whether electrophysiological measures can be used in machine learning models to accurately predict mind wandering states. We recorded scalp EEG from participants as they performed an auditory target detection task and self-reported whether they were on task or mind wandering. We successfully classified attention states both within (person-dependent) and across (person-independent) individuals using event-related potential (ERP) measures. Non-linear and linear machine learning models detected mind wandering above-chance within subjects: support vector machine (AUC = 0.715) and logistic regression (AUC = 0.635). Importantly, these models also generalized across subjects: support vector machine (AUC = 0.613) and logistic regression (AUC = 0.609), suggesting we can reliably predict a given individual’s attention state based on ERP patterns observed in the group. This study is the first to demonstrate that machine learning models can generalize to “never-seen-before” individuals using electrophysiological measures, highlighting their potential for real-time prediction of covert attention states.
... Because deep breathing practice was proved to be beneficial for a variety of cognitive functions and widely used in meditation, participants were instructed to keep a constant, slow, and deep diaphragmatic breathing during the task (Brown and Gerbarg, 2009). More specific requirements for breathing were described in our previous study (Zheng et al., 2019). ...
... Our previous study has validated that the synchronization between the force and respiration signals can be used as an objective marker of attentional state (Zheng et al., 2019). Thus, another advantage of the HAM paradigm might lie in the possibility of real-time monitoring of the attentional state. ...
Article
Full-text available
Sustained attention is a fundamental ability ensuring effective cognitive processing and can be enhanced by meditation practice. However, keeping a focused meditative state is challenging for novices because involuntary mind-wandering frequently occurs during their practice. Inspired by the potential of force-control tasks in invoking internal somatic attention, we proposed a haptics-assisted meditation (HAM) to help reduce mind-wandering and enhance attention. During HAM, participants were instructed to maintain awareness on the respiration and meanwhile adjust bimanual fingertip pressures to keep synchronized with the respiration. This paradigm required somatosensory attention as a physiological foundation, aiming to help novices meditate starting with the body and gradually gain essential meditation skills. A cross-sectional study on 12 novices indicated that the participants reported less mind-wandering during HAM compared with the classic breath-counting meditation (BCM). In a further longitudinal study, the experimental group with 10 novices showed significantly improved performance in several attentional tests after 5 days’ practice of HAM. They tended to show more significant improvements in a few tests than did the control group performing the 5-day BCM practice. To investigate the brain activities related to HAM, we applied functional near-infrared spectroscopy (fNIRS) to record cerebral hemodynamic responses from the prefrontal and sensorimotor cortices when performing HAM, and we assessed the changes in cerebral activation and functional connectivity (FC) after the 5-day HAM practice. The prefrontal and sensorimotor regions demonstrated a uniform activation when performing HAM, and there was a significant increase in the right prefrontal activation after the practice. We also observed significant changes in the FC between the brain regions related to the attention networks. These behavioral and neural findings together provided preliminary evidence for the effectiveness of HAM on attention enhancement in the early stage of meditation learning.
Conference Paper
Detection and intervention of various impaired driver states have been intensively studied with corresponding technologies widely implemented in modern vehicles. Different algorithms are proposed to detect certain states or conditions, with intervention means like driver alerts or vehicle active safety features being developed and optimized accordingly. However, there lacks a unified view of all of these different driver states. To support the development of vehicle systems, this study tries to compare the commonly-seen impaired driver states in terms of their detection features as well as the effects on degraded driving performance. A meta-analysis is conducted to identify the overlapping and disjoint spaces among them from the angle of the vehicle design. The research finds some answers about the driver behavior and environment features that the vehicle system shall pay attention to and the degraded driving performance that the vehicle shall prepare for when impaired driving happens in different ways in reality.