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| Number of trials in different conditions (Error or Correct trials, with or without self-report, Above or Below).

| Number of trials in different conditions (Error or Correct trials, with or without self-report, Above or Below).

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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
... was why we chose the median split analytical approach, defining TS * as the median TS , instead of the mean value of TS for all correct trials in each session. Table 1 lists the number of the detected error trials from all participants and the error trials without self-report (named as "missed-report"). The results showed that there were 26 error trials, and 8 of these error trials were missed-reports (caused by four participants). ...
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... order to assess the effectiveness of TS * in mind wandering detection, we counted and compared the number of self- reports during trials above the TS * ( TS (i) > TS * ) and that below the TS * ( TS (i) ≤ TS * ). Based on the data of the four columns of "Error trials-with self-report-Above, " "Error trials-with self-report-Below, " "Correct trials-with self-report- Above, " and "Correct trials-with self-report-Below" in Table 1, we analyzed the correlation between the number of self-reports and the TS values. One-way ANOVA on the number of self-reports showed that the participants reported significantly more mind- wandering episodes during trials above the TS * than those below the TS * [F(1,22) = 7.175, p < 0.05, Figure 6A]. ...
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... results of Table 1 verify that the relatively large value of TS effectively reflects the occurrence of mind wandering in the respiration-force coordinating task. Whenever the participants made error trials or reported mind-wandering episodes, TS values exceeded the TS * for most cases. ...
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... addition, the proposed index TS cannot ensure uninterrupted monitoring of mind wandering (i.e., there is a blind window in the temporal domain). It can be evidenced by the contradiction between the small values of TS and self- reports as listed in Table 1. In these trials, participants produced a good temporal synchronization on the task but still reported a mind-wandering episode. ...
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... critical point worth noting is that the interpretation of results in this paper is based on a basic assumption -errors produced in the respiration-force coordinating task reflect mind wandering (more accurately, task-unrelated thoughts). This assumption is at least a fact for our relatively simple task, which can be validated by the data in Table 1 (18 mind-wandering episodes were reported following errors trials, while only 8 errors were produced in the absence of self-reports). It demonstrated that most participants are able to report their mind-wandering episodes when errors occurred during the task. ...


... 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. ...
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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. ...
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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.