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Detecting Mind Wandering: An Objective Method via Simultaneous Control of Respiration and Fingertip Pressure


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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 ways previous research has assessed mind wandering. Here we propose a task in which participants are asked to simultaneously control respiration and fingertip pressure. They are instructed to click a force sensor at the exact moment of inhalation and exhalation of their respiration. The temporal synchronization between the respiratory signals and the fingertip force pulses offers an objective index to detect mind wandering. Twelve participants engaged in the proposed task in which self-reports of mind wandering are compared with the proposed objective index. The results show that the participants reported significantly more mind-wandering episodes during the trials with a larger temporal synchronization than they did during those trials with a smaller temporal synchronization. The findings suggest that the temporal synchronization might be used as an objective marker of mind wandering in attention training and exploration of the attention control mechanism.
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fpsyg-10-00216 February 2, 2019 Time: 18:20 # 1
published: 05 February 2019
doi: 10.3389/fpsyg.2019.00216
Edited by:
Marieke Karlijn Van Vugt,
University of Groningen, Netherlands
Reviewed by:
Tracy Brandmeyer,
University of California,
San Francisco, United States
Miriam Gade,
Medical School Berlin, Germany
Dangxiao Wang
Specialty section:
This article was submitted to
a section of the journal
Frontiers in Psychology
Received: 15 September 2018
Accepted: 22 January 2019
Published: 05 February 2019
Zheng Y, Wang D, Zhang Y and
Xu W (2019) Detecting Mind
Wandering: An Objective Method via
Simultaneous Control of Respiration
and Fingertip Pressure.
Front. Psychol. 10:216.
doi: 10.3389/fpsyg.2019.00216
Detecting Mind Wandering: An
Objective Method via Simultaneous
Control of Respiration and Fingertip
Yilei Zheng1, Dangxiao Wang1,2*, Yuru Zhang1,2 and Weiliang Xu2,3
1State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China, 2Beijing Advanced
Innovation Center for Biomedical Engineering, Beihang University, Beijing, China, 3Department of Mechanical Engineering,
The University of Auckland, Auckland, New Zealand
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 ways previous research has assessed mind wandering. Here we
propose a task in which participants are asked to simultaneously control respiration and
fingertip pressure. They are instructed to click a force sensor at the exact moment of
inhalation and exhalation of their respiration. The temporal synchronization between the
respiratory signals and the fingertip force pulses offers an objective index to detect mind
wandering. Twelve participants engaged in the proposed task in which self-reports of
mind wandering are compared with the proposed objective index. The results show that
the participants reported significantly more mind-wandering episodes during the trials
with a larger temporal synchronization than they did during those trials with a smaller
temporal synchronization. The findings suggest that the temporal synchronization might
be used as an objective marker of mind wandering in attention training and exploration
of the attention control mechanism.
Keywords: sustained attention, mind wandering detection, simultaneous control, respiration, fingertip pressure,
temporal synchronization
Sustained attention, focusing on a target or a task and resisting the occurrence of unrelated
thoughts, is a fundamental human ability that ensures effective cognitive processing (Raz and Buhle,
2006;Chun et al., 2011). However, our attentions are not always tied to ongoing events or to tasks we
are performing. When we produce thoughts unrelated to an ongoing task, this is commonly referred
to as mind wandering (Antrobus et al., 1970;Smallwood and Schooler, 2006). Several studies have
associated mind wandering with a range of beneficial functions such as planning and creativity
(Baird et al., 2011, 2012). Nevertheless, mind wandering’s correlation with costly outcomes such
as driving accidents (Yanko and Spalek, 2013), working inefficiency (Farley et al., 2013;Seli et al.,
2016), affective dysfunction (Smallwood et al., 2003;Killingsworth and Gilbert, 2010), and impaired
performance in daily life (McVay et al., 2009) has received far more attention.
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Zheng et al. Detecting Mind Wandering
One example of involuntary mind wandering often occurs
during meditation. During focused attention meditation,
participants are required to maintain attention on specific
content such as breathing or a candle flame. In the sustained
breath awareness task, for example, participants need to focus
continuously on their breath; if they realize the occurrence of
mind wandering, they are instructed to reorient focus toward the
breath (Braboszcz and Delorme, 2010;Hasenkamp et al., 2012).
However, participants, especially novices, are generally unaware
of mind wandering at the moment it occurs. It has previously
been observed that meditation is a promising tool to improve
attention span and reveal the attention control mechanism (Lutz
et al., 2004, 2009;Tang et al., 2007;Brewer et al., 2011). Studies
exploring the effects of intense meditation training on mind
wandering have also suggested that meditation training reduces
the susceptibility of minds to wander, subsequently leading to
longer periods of meditative absorption and better attentional
performance (Brandmeyer and Delorme, 2016;Zanesco et al.,
2016). Accordingly, detecting mind wandering during meditation
is a crucial and necessary step toward enhancing the effectiveness
of attention training and may contribute to exploration of
the neural mechanisms underlying the regulation of sustained
attention (Schooler et al., 2011;Schmalzl et al., 2015).
Mind wandering has been mainly detected through two
thought-report methods: discrete thought-probes (Allan Cheyne
et al., 2009;Stawarczyk et al., 2011;Seli et al., 2013;Levinson et al.,
2014) and spontaneous self-reports (Smallwood and Schooler,
2006;Braboszcz and Delorme, 2010). In the former, participants
are randomly probed about their subjective attentional states; one
of these ways is being asked to press buttons during a specific
task. This method is easy to implement but valid only at the
moment of the probe. It misses some vital information such as the
time of alternating states, the starting moment, and the duration
of a mind-wandering episode. In addition, the mental state of
participants after a thought-probe cannot be assessed: whether
they continue wandering, restart a new wandering, or refocus
attention back to the task.
In spontaneous self-reports, participants are requested to
note the moment they become aware of mind wandering. This
method allows continuous tracking of mind wandering from the
participant’s perspective. However, this tracking is subjective and
limits the ability of researchers to maintain consistent evaluation
among different participants. Furthermore, monitoring one’s own
mind wandering is actually a task which may induce mind
wandering (Bastian and Sackur, 2013;Vugt et al., 2015). Both
methods have a fundamental flaw in that a participant’s mind
wandering is evaluated solely by themselves, and that participants
may not be aware when their attention drifts away.
To improve the aforementioned methods for tracking mind
wandering, there have been extensive efforts made in pursuing
objective measures or sensor-based metrics. Some of these
measures are behavioral, including response time (RT) variability
(Bastian and Sackur, 2013;Esterman et al., 2013;Rosenberg
et al., 2013, 2015;Seli et al., 2013), increased error rate (McVay
and Kane, 2012), and decreased comprehension (Smallwood
et al., 2008;Schad et al., 2012). Other measures are electro-
physiological and neurological, including increased galvanic skin
response (Smallwood et al., 2007), pupil dilation (Smallwood
et al., 2011;Wainstein et al., 2017), increased activity in the
default mode and executive networks (Christoff et al., 2009),
increased energy in theta and delta bands and decreased energy
in the alpha and beta bands (Braboszcz and Delorme, 2010), and
decreased amplitude of sensory-triggered ERP (Kam et al., 2011).
Nevertheless, since mind wandering is an inherently subjective
experience, subjective feedback remains an important method
for monitoring attentional state. In the studies using random
thought-probes (Christoff et al., 2009;Seli et al., 2013), the
measures within the few seconds preceding off-task reports
and within the few seconds preceding on-task reports were
contrasted. In the studies using spontaneous self-reports
(Braboszcz and Delorme, 2010;Hasenkamp et al., 2012;
Bastian and Sackur, 2013), the measures within the seconds
preceding the report and within the seconds following the
report were contrasted. Note that for the measures using
self-reported mind-wandering episodes, participants must be
alerted to the instruction that they should refocus attention
to the task immediately after reporting mind-wandering
Different from previous studies on exogenous attention, this
paper makes an attempt to explore the objective method for
mind-wandering detection in an endogenous attention task. It
should be clarified that mind wandering encompasses a broad
range of phenomena and there is still not a uniform definition
of mind wandering in previous studies (Seli et al., 2018). Mind
wandering in our study is defined as task-unrelated thoughts.
This paper proposed a novel respiration–force coordinating
task wherein participants were instructed to click a force sensor
when they started to inhale and exhale. Respiratory signals,
fingertip pressure, and self-reports of mind-wandering episodes
were recorded during the task. The temporal synchronization
between the respiratory signals and the fingertip force pulses
was defined as an objective index to detect mind wandering.
Experimental results based on 12 participants indicated that
the participants reported significantly more mind-wandering
episodes during the trials with a larger temporal synchronization
than those trials with a smaller temporal synchronization. In
addition, the durations of mind-wandering episodes in the task
were estimated according to different task responses.
Twelve healthy students (four females; mean
age = 24 ±1.62 years, range = 22–27 years) from Beihang
University participated in the study. All participants were right-
handed and had no cognitive deficit disorders or somatosensory
disorders. Notably, none of them had experience in meditation
or related exercises. This study was approved by State Key
Laboratory of Virtual Reality Technology and Systems of China.
All the methods were performed in accordance with the relevant
guidelines and regulations. All participants provided written
informed consent prior to participation and each of them was
paid U150 (about $23) upon completion of the experiment.
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A respiration sensor (used in combination with NeXus-10 Mark
II, Mindmedia Inc., Netherlands) was tied to the participant’s
abdomen to measure the respiratory signals. Three force
sensors (A, B and C in Figure 1, FSG15N1A, Honeywell Inc.,
United States) were mounted on a fixed plate to measure the
forces exerted by the left index, right index, and right middle
fingertips, respectively.
Experimental Task and Procedure
All participants conducted the respiration–force coordinating
task in the experiment. The coordinating task involves
synchronous controlling of fingertip force pulses and deep
breathing. Deep breathing practices, which generally involve
inhalation and exhalation of air at a slow rate that is different
from regular breath cycles, form an integral component of
many meditation programs (Brown and Gerbarg, 2009).
A number of studies show that the deep breathing practice
may result in marked beneficial effects across a variety of
cognitive functions, including inhibitory control, working
memory, and attention and emotion regulation (Busch et al.,
2012;Brown et al., 2013;Vlemincx et al., 2016;Yadav and
Mutha, 2016). Depending on the breathing pattern, deep
breathing practices can take various forms (Brown et al.,
2013); during the respiration-force coordinating task, the
participants were instructed to keep a constant, slow, and deep
diaphragmatic breathing rhythm with a brief pause following
the inspiratory/expiratory period of each breathing cycle.
Instead of requiring a specific respiration rate and depth, the
participants were instructed only to breathe as slowly and
deeply as possible to maintain comfort throughout the breathing
Figure 1 shows the procedure of the respiration–force
coordinating task, during which participants sat in a chair in
front of the force-sensor plate at a convenient height. They were
required to click button B using the right index fingertip at the
exact time when they started breathing in. Similarly, they clicked
button C using the right middle fingertip when beginning to
breathe out. The participants were asked to try their best to keep
the temporal synchronization between the respiratory signals and
fingertip pressure. Additionally, the participants were instructed
to immediately report whenever they became aware of mind
wandering. They did this by clicking button A with the left index
fingertip and then refocusing attention back to the task.
Mind wandering during the task is defined as “failing to
click any buttons when breathing in or out” or “clicking the
wrong button, such as button C when breathing in or button
B when breathing out.” These guidelines were instructed to all
participants prior to the experiment. To prevent confusion, the
participants were required to rest their fingers on the top of
corresponding buttons before each session of the experiment so
that they would not click a wrong button.
Before the experiment, participants were provided with
written instructions and sufficient practice to make sure they were
fully familiar with the experimental requirements. The whole
experiment lasted 1 h and 15 min, split into four sessions of
15 min with 5-min breaks between two adjacent sessions. The
participants were required to wear an eyeshade for eliminating
external visual disturbance and a pair of head-mounted earmuffs
for eliminating surrounding noise during the task.
We defined each inhalation or exhalation within a respiratory
cycle as a trial. Trials in which participants clicked the
corresponding button correctly when they breathed in/out were
considered correct trials. Two types of trials were considered
error trials: (a) missed response, i.e., participants did not click
any buttons during inhalation or exhalation phases within a
respiratory cycle; (b) wrong response, i.e., participants clicked
the wrong button (clicked button C when breathing in or clicked
button B when breathing out), as shown in Figure 2A. Accuracy
was defined as the percentage of correct trials over all trials in
each session.
Temporal Synchronization
In this work, the time difference between starting to breathe
in/out and clicking the corresponding button was named the
temporal synchronization (1TS). In the ith trial, the in-breath
start moment was denoted by tB(i), as shown in Figure 2B. The
moment when the force was greater than 0.2 N was defined
as the button-click moment and denoted by tF(i) in Figure 2B
considering the zero drift of the force sensors. 1TS of the ith trial
was then computed as follows:
1TS(i)= |tB(i)tF(i)|(1)
Given that the respiratory signals reached plateaus near to the
peak and valley, calculating the end data points of the peak and
valley plateaus [i.e., B(i) and B(i+1) in Figure 2B] is the key of
the algorithm.
The algorithm steps were stated as follows. First, band-pass
filter (0.5–100 Hz) and moving average filtering were used to
remove some obvious noise. Second, we segmented the filtered
data into segments of each respiratory cycle by setting the
minimum interval among two adjacent peaks and minimum
amplitude of peaks. So that the moments of the maximum points
and minimum points in each data segment can be obtained. Last,
for each data segment, least square method was used to obtain a
straight line which passed the maximum or minimum point to fit
the plateaus of peak or valley. The intersections of this straight
line with the filtered signals were then calculated. Generally,
more than one intersection would be obtained. However, the
intersection with the maximum abscissa (i.e., the corresponding
moment) was considered the end of the peak or valley plateau.
The accuracy of the algorithm for computing the temporal
synchronization is about 50 ms, which meets our requirements
for the subsequent analysis. In addition, it should be noted that
1TS of a missed response trial was treated as half of the duration
of the associated respiratory cycle. Its value was much larger than
that of correct trials. 1TS of a wrong response trial was, however,
defined as the duration from the beginning of breathing-in/-out
to the moment of clicking the wrong button. Its value was not
necessarily larger than that of correct trials.
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FIGURE 1 | Experimental task. Participants sat in a chair in front of the fixed force-sensor plate, tied a respiration sensor belt to the abdomen, and wore an
eyeshade and a pair of earmuffs. They clicked button B using the right index fingertip when beginning to breathe in and clicked button C using the right middle
fingertip when beginning to breathe out. Button A was clicked by the left index fingertip to report mind wandering whenever the participants were aware of it. The
data of fingertip force pulses and respiration were recorded in real-time.
FIGURE 2 | (A) Definition of error trials, including missed response and wrong response. (B) 1TS in two consecutive trials. (C) Model of detecting mind wandering.
Error trials and correct trials with 1TS values larger than 1TSwere considered “mind wandering.” Correct trials with 1TS values smaller than 1TSwere considered
“on-task.” 1TSwas the median of 1TS for all correct trials in each session.
Model of Detecting Mind Wandering
We hypothesized that the internal attentional states can be
objectively reflected by external measurable signals including
respiratory signals and fingertip pressure. All participants were
provided sufficient practice prior to the formal experiment
to ensure they were fully familiar with the respiration-force
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coordinating task. Considering that the task is simple enough for
all participants and that they reported a good mental state during
the whole experiment, it is reasonable to state that error trials and
poor temporal synchronization (1TS(i) larger than 1TS ) reflect
mind wandering.
We proposed an objective model to detect mind wandering
during the task (Figure 2C). The inputs included the force
of the right index fingertip FRI, the force of the right middle
fingertip FRM, and the respiratory signal R. The output was
the attentional state at each trial: “mind wandering” state or
“on-task” state. Here we defined 1TSas the median of 1TS
for all correct trials in each session. To compute 1TS, the
1TS values of all correct trials were calculated and normalized
by min–max normalization method (Shalabi et al., 2006) for
each session of each participant. The 1TSof each session
was then used to distinguish “mind wandering” state and
“on-task” state. Those correct trials (1TS(i)> 1TS ) in each
session were considered “mind wandering,” which represents a
poor temporal synchronization between respiration and fingertip
pressures signals. Other correct trials (1TS(i)1TS ) with a
good temporal synchronization were considered “on-task.”
Estimating Duration of Mind Wandering
The duration of mind-wandering episode for error trials was
estimated as illustrated in Figure 3. For error trials with self-
report, including missed response (Figure 3A) and wrong
response (Figure 3B), the duration was determined from the end
moment of the latest correct trial to the start moment of the
self-report. As for missed response and wrong response without
self-report (shown in Figure 3C,D), the duration was determined
from the end moment of the latest correct trial to the start
moment of the next correct trial.
Overall Performance
The mean accuracy of four sessions for each participant is
presented in Figure 4. All participants made more than 97.5% of
correct trials on average. Half of the participants made no errors
in all sessions.
To further assess the mean 1TS and the variability of 1TS,
we first calculated the proportion of different 1TS ranges for all
correct trials (shown in Figure 5A). The results illustrated that
the 1TS of 99.1% of correct trials was shorter than 3000 ms.
Considering that such trials with 1TS exceeding 3000 ms may
not be caused by participants, we excluded these trials when
calculating the mean 1TS in the subsequent analysis. Moreover,
the 1TS shorter than 1000 ms took up 87.5% of correct
trials and the proportion of trials with smaller 1TS was even
higher (Figure 5A). Together these findings demonstrated that
participants performed accurate temporal synchronization in
most trials.
FIGURE 4 | Mean accuracy of four sessions for all participants during the
task. Error bars indicate ±SD.
FIGURE 3 | Estimating duration of mind wandering. For (A) missed response with self-report and (B) wrong response with self-report, the duration was considered
from the end of the latest correct trial to the self-report. For (C) missed response without self-report and (D) wrong response without self-report, the duration was
considered from the end of the latest correct trial to the beginning of the next correct trial.
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FIGURE 5 | Overall performance of 1TS.(A) Proportion of different 1TS ranges for all correct trials of four sessions. (B) Mean and median of 1TS for correct trials of
four sessions for each participant. Error bars indicate ±SD.
The mean and median of 1TS for correct trials are displayed
in Figure 5B. The results of paired t-test showed that the
mean 1TS was significantly greater than the median 1TS
(t= 5.247, p<0.001). Therefore, the mean 1TS was not
reasonable to evaluate participants’ performance because the
overall performance would be underestimated. That was why we
chose the median split analytical approach, defining 1TSas the
median 1TS, instead of the mean value of 1TS for all correct trials
in each session.
Detection of Mind Wandering
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).
In order to assess the effectiveness of 1TSin mind wandering
detection, we counted and compared the number of self-
reports during trials above the 1TS(1TS (i)> 1TS) and that
below the 1TS(1TS (i)1TS). 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 1TS values. One-way ANOVA on the number of self-reports
showed that the participants reported significantly more mind-
wandering episodes during trials above the 1TSthan those
below the 1TS[F(1,22) = 7.175, p<0.05, Figure 6A].
In addition, we analyzed the correlation between the number
of error trials and the 1TS values based on the data of the
four columns belonging to “Error trials.” One-way ANOVA on
the number of error trials showed that the participants made
significantly more error trials during trials above the 1TSwhen
compared with those below the 1TS[F(1,22) = 4.477, p<0.05,
Figure 6B]. These findings demonstrated that the participants
were in a state of inattention during trials above the 1TS, when
they tended to report more mind-wandering episodes and make
more error trials.
Duration of Mind Wandering
We estimated the duration of mind-wandering episodes in
the proposed task according to different task responses (see
the section “Materials and Methods”). The 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
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TABLE 1 | Number of trials in different conditions (Error or Correct trials, with or without self-report, Above or Below).
Participants number Error trials Correct trials
With self-report Without self-report With self-report Without self-report
Above Below Above Below Above Below Above Below
1 0 0 0 0 1 3 184 193
2 0 0 0 0 0 1 73 72
3 2 1 0 0 2 0 134 139
4 0 0 0 0 1 0 77 80
5 5 0 0 0 2 0 162 164
6 0 0 0 0 1 0 141 142
7 3 0 1 0 1 0 74 81
8 0 0 1 1 0 0 78 79
9 4 1 3 0 1 0 157 160
10 0 0 0 0 5 1 333 337
11 1 1 2 0 1 0 157 158
12 0 0 0 0 1 0 170 174
Total 15 3 7 1 16 5 1740 1779
Above: TS(i) > ∆TS ; below: TS(i) TS .
FIGURE 6 | Number of self-reports and error trials during trials above and
below the 1TS. Participants (A) reported significantly more mind-wandering
episodes and (B) made significantly more error trials during trials above the
1TSthan those below the 1TS .p<0.05. Error bars indicate ±SD.
wandering for error trials in different conditions (wrong response
or missed response, with self-report or without self-report).
Contrasts 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.
Given that monitoring mind wandering during internally
oriented states such as meditation is inherently subjective and
thus notoriously difficult to measure, in this study we proposed
a method for assessing mind wandering during an internally
focused breath awareness task. This task requires participants to
monitor the exact moment of inhalation and exhalation of their
breath while simultaneously pressing a button at the moment
of each individual inhalation and exhalation. As breathing is a
naturally occurring phenomenon that continues without effort,
the correspondence between the participants self-reports and the
inspiratory/expiratory moment is a good indication of whether
the participants are focused on the task or whether they have
begun mind wandering. The experiment results support that
the larger temporal synchronization between the respiratory
signals and the fingertip pressures indicated the mind-wandering
state. Together these results add to the growing list of objective
measures used to detect mind wandering.
Several previous studies (Bastian and Sackur, 2013;Esterman
et al., 2013;Rosenberg et al., 2013, 2015;Seli et al., 2013;
Laflamme et al., 2018) highlight RT variability instead of raw RT
(corresponding to 1TS in this paper) as an important indicator
of attentional state in some focal tasks such as the Sustained
Attention to Response Task (SART) (Helton et al., 2009). Using
a go/no-go paradigm, SART requires participants to repetitively
respond to the stimuli as quickly and as accurately as possible.
During this task, deviant RTs, whether fast or slow, represent
reduced attention to the task. Abnormally slow RTs may indicate
reduced attention to the task or inefficient processing of the
ongoing stream of visual stimuli thus requiring more time to
accurately discriminate targets, while abnormally fast RTs may
indicate premature or routinized responding and inattention to
the potential need for response inhibition. Performance in SART
is subjected to a speed–accuracy tradeoff resulting from strategy
choices and from the failures of controlling motor reflexes (Dang
et al., 2018). In our study, we used raw 1TS instead of 1TS
variability as the index of attentional state. We considered the
following aspects. First, the stimulus signal in our task is the onset
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FIGURE 7 | 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.
of inhalation or exhalation in each respiratory cycle. Participants
pay attention to their respiration and produce the fingertip force
pulses, which are more related to endogenous attention rather
than response inhibition. Therefore, we confidently hypothesize
that participants can monitor their respiratory signals and
produce correct fingertip force pulses with small 1TS easily. The
speed–accuracy tradeoff does not need to be considered since
the respiratory signals can be controlled by participants. Second,
adopting 1TS variability (standard deviation or coefficient of
variation of several trials) as the index of attentional state to
our work is unreasonable since the duration of each trial is not
Statistical results of Table 1 verify that the relatively large
value of 1TS effectively reflects the occurrence of mind
wandering in the respiration–force coordinating task. Whenever
the participants made error trials or reported mind-wandering
episodes, 1TS values exceeded the 1TSfor most cases.
Nevertheless, the 1TSis a relative value which varies between
subjects and between sessions within-subjects. The 1TSdepends
on the 1TS values of all correct trials in each session. Thus, it
may be hard to realize the real-time online detection of mind
wandering via the method of 1TS. One possible solution is
determining a threshold for 1TS based on the absolute values
of 1TS. Of course, the 1TS threshold should be modified
according to different participants and adjusted adaptively as the
performance of task changes.
In addition, the proposed index 1TS 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 1TS 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. The reason is that 1TS is only sampled
and computed at the beginning of inhalation or exhalation. The
detection of a brief mind-wandering episode in the duration
between two adjacent sampling points may be missed through
this method. In other words, the temporal resolution of the
mind wandering detection is half of the respiratory cycle. One
effective solution for improving the temporal resolution is to
require participants to increase/decrease fingertip pressure
along with the inhalation/exhalation process synchronously. By
observing the synchronization between the temporal gradient of
respiratory signals and that of fingertip pressure signals, a higher
temporal resolution index could be devised to realize continuous
monitoring of mind wandering.
It should be noted that the mind-wandering levels of error
trials and of correct trials with poor temporal synchronization
are considered different although they are both caused by mind
wandering. This can be explained by the characteristics of the
task. Given that the task is simple and all participants carried
out adequate practice before the formal experiment, it is mind
wandering that impairs a participant’s performance on the task.
These impairments can be found in two results: error trials and
increased 1TS. In the error trials, the level of mind wandering
was so deep that the participants completely forget to produce
force pulses or produce a wrong force pulse. However, in the
correct trials with large 1TS values, participants produced a
correct response with a poor temporal synchronization. This
level of mind wandering was relatively weak, or the duration of
mind wandering was so short that the attention refocused on
the task before the participants realized the occurrence of mind
wandering. One possibility is that the participants distributed
some attentional resources to other task-unrelated thoughts,
which inevitably reduced the speed of force production, and thus
led to the relatively large values of 1TS.
Moreover, the duration of mind wandering in the respiration–
force coordinating task fluctuated with in a range of 3–56.5 s
according to different types of error trials (wrong response,
missed response, with and without self-report). It must be
noted that the estimation of mind-wandering duration in this
paper is conservative. The estimation method is based on
the assumption that mind wandering occurs immediately after
the end of the latest correct trial and continues until the
beginning of next correct trial or the self-report. The obtained
duration of mind wandering also includes the interval from
realizing mind wandering to clicking the button, and therefore
is longer than the actual duration. Nevertheless, one advantage
of the estimation method is that the duration can be computed
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Zheng et al. Detecting Mind Wandering
separately according to different types of error trials, which
may help to explore different types of mind-wandering episodes.
The mind-wandering duration for the correct trials with poor
temporal synchronization can also be estimated by this method;
the duration is considered as the time span of consecutive trials
with a 1TS value above the 1TS.
A 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. However, those
eight missed-reports may be attributed to two possibilities.
One is that the mind wandering indeed happened and caused
the error, but the participants did not notice the occurrence
of mind wandering or they might have forgotten to report.
The other possibility is that the participants produced wrong
fingertip response pulses when they were not thinking about task-
unrelated thoughts. Maybe there was a misalignment between
perceptual and finger-controlling processes in the absence of task
inattention and therefore the assumption we made in this paper
may have potential limitation. Nevertheless, the interpretations of
results in this paper are still valid since the absence of self-reports
following error trials in the results was minor. Meanwhile, an
experiment with a larger sample size is worthwhile and essential
to further verify the effectiveness and robustness of 1TS on
detecting mind wandering.
The proposed respiration–force coordinating task provides an
objective method to detect mind wandering in the endogenous
attention task. The major difference from previous objective
detection methods of mind wandering is that stimulus signals
originate from internal human bodies (i.e., the onset of inhalation
or exhalation in each respiratory cycle) rather than external visual
or auditory presentations. The respiration–force coordinating
task preserves the feature of internal focus guidance in meditation
practices. It brings a promising solution for objective monitoring
of mind wandering during meditation and attention training.
Based on the present work, one possible place for future
research is applying the proposed method to online mind-
wandering detection and reducing the frequency of mind-
wandering episodes in attention training by alerting practitioners
once their minds start wandering. Longitudinal studies are
also expected to be performed in future research to explore
whether long-term training of the respiration–force coordinating
task could improve attention. Furthermore, the mechanism
underlying the effect of the task on attention modulation could
be revealed via neuroimaging methods.
The raw data supporting the conclusions of this manuscript will
be made available by the authors, without undue reservation, to
any qualified researcher.
YiZ and DW designed, performed, analyzed, and wrote up
the research. YuZ and WX critically reviewed and edited the
This work was supported by the National Key Research and
Development Program under Grant No. 2017YFB1002803 and by
the National Natural Science Foundation of China under Grant
No. 61572055.
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2019 Zheng, Wang, Zhang and Xu. This is an open-access article
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Frontiers in Psychology | 10 February 2019 | Volume 10 | Article 216
... 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.
... 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.
Mind wandering, also known as task-unrelated thought, refers to the drift of attention from a focal task or train of thought. Because self-caught measures of mind wandering require participants to spontaneously indicate when they notice their attention drift, self-caught methodologies provide a way to measure mind wandering with meta-awareness. Given the proposed role of meta-awareness in mental health and psychological interventions, an overview of existing self-caught methodologies would help clinicians and researchers make informed decisions when choosing or adapting a mind wandering or meta-awareness measure. This systematic review included 39 studies after 790 studies were assessed for eligibility. All studies operationalised mind wandering as instances of attention drift from a primary task. Three types of primary task were identified: (1) tasks adapted from computerised continuous performance tests (CPT) of sustained attention, (2) tasks involving focusing on the breath or a stream of aural beats, akin to in-vivo mindfulness meditation, (3) tasks involving an everyday life activity such as reading. Although data on mind wandering without meta-awareness (e.g., measured with probe-caught measures) was also obtained in many studies, such data was not always used in conjunction with self-caught mind wandering data to determine level of mind wandering meta-awareness. Few studies reported both reliability and validity of the measures used. This review shows that considerable methodological heterogeneity exists in the literature. Methodological variants of self-caught mind wandering methodologies are documented and examined, and suggestions for future research and clinical work are suggested.
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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.
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The Sustained Attention to Response Task (SART) has been widely used in psychological literature as a measure of vigilance (the ability to sustain attention over a prolonged period of time). This task uses a Go/No-Go paradigm and requires the participants to repetitively respond to the stimuli as quickly and as accurately as possible. Previous literature indicates that performance in SART is subjected to a “speed–accuracy trade-off” (SATO) resulting from strategy choices and from the failures of controlling motor reflexes. In this study, 36 participants (n = 36) performed a series of four SARTs. The results support the perspective of strategy choice in SART and suggest that within-subjects SATO in SART should also be acknowledged in attempting to explain SART performance. The implications of the speed–accuracy trade-off should be fully understood when the SART is being used as a measure or tool.
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Attention-deficit/hyperactivity disorder (ADHD) diagnosis is based on reported symptoms, which carries the potential risk of over- or under-diagnosis. A biological marker that helps to objectively define the disorder, providing information about its pathophysiology, is needed. A promising marker of cognitive states in humans is pupil size, which reflects the activity of an ‘arousal’ network, related to the norepinephrine system. We monitored pupil size from ADHD and control subjects, during a visuo-spatial working memory task. A sub group of ADHD children performed the task twice, with and without methylphenidate, a norepinephrine–dopamine reuptake inhibitor. Off-medication patients showed a decreased pupil diameter during the task. This difference was no longer present when patients were on-medication. Pupil size correlated with the subjects’ performance and reaction time variability, two vastly studied indicators of attention. Furthermore, this effect was modulated by medication. Through pupil size, we provide evidence of an involvement of the noradrenergic system during an attentional task. Our results suggest that pupil size could serve as a biomarker in ADHD.
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Paced deep breathing practices, a core component of a number of meditation programs, have been shown to enhance a variety of cognitive functions. However, their effects on complex processes such as memory, and in particular, formation and retention of motor memories, remain unknown. Here we show that a 30-minute session of deep, alternate-nostril breathing remarkably enhances retention of a newly learned motor skill. Healthy humans learned to accurately trace a given path within a fixed time duration. Following learning, one group of subjects (n = 16) underwent the 30-minute breathing practice while another control group (n = 14) rested for the same duration. The breathing-practice group retained the motor skill strikingly better than controls, both immediately after the breathing session and also at 24 hours. These effects were confirmed in another group (n = 10) that rested for 30 minutes post-learning, but practiced breathing after their first retention test; these subjects showed significantly better retention at 24 hours but not 30 minutes. Our results thus uncover for the first time the remarkable facilitatory effects of simple breathing practices on complex functions such as motor memory, and have important implications for sports training and neuromotor rehabilitation in which better retention of learned motor skills is highly desirable.
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One outstanding question in the contemplative science literature relates to the direct impact of meditation experience on the monitoring of internal states and its respective correspondence with neural activity. In particular, to what extent does meditation influence the awareness, duration and frequency of the tendency of the mind to wander. To assess the relation between mind wandering and meditation, we tested 2 groups of meditators, one with a moderate level of experience (non-expert) and those who are well advanced in their practice (expert). We designed a novel paradigm using self-reports of internal mental states based on an experiential sampling probe paradigm presented during ~1 h of seated concentration meditation to gain insight into the dynamic measures of electroencephalography (EEG) during absorption in meditation as compared to reported mind wandering episodes. Our results show that expert meditation practitioners report a greater depth and frequency of sustained meditation, whereas non-expert practitioners report a greater depth and frequency of mind wandering episodes. This is one of the first direct behavioral indices of meditation expertise and its associated impact on the reduced frequency of mind wandering, with corresponding EEG activations showing increased frontal midline theta and somatosensory alpha rhythms during meditation as compared to mind wandering in expert practitioners. Frontal midline theta and somatosensory alpha rhythms are often observed during executive functioning, cognitive control and the active monitoring of sensory information. Our study thus provides additional new evidence to support the hypothesis that the maintenance of both internal and external orientations of attention may be maintained by similar neural mechanisms and that these mechanisms may be modulated by meditation training.
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It is challenging for individuals to maintain their attention on ongoing cognitive tasks without being distracted by task-unrelated thought. The wandering mind is thus a considerable obstacle when attention must be maintained over time. Mental training through meditation has been proposed as an effective method of attenuating the ebb and flow of attention to thoughts and feelings that distract from one’s foremost present goals. We provide evidence from 2 longitudinal studies that intensive meditation training in focused attention and monitoring meditation is associated with attenuated lapses of attention while reading. Across 2 studies, participants completed a reading task requiring ongoing error monitoring to detect episodes of semantic inconsistency. In a preliminary study, training participants were assessed at the beginning and end of a 3-month shamatha meditation retreat and again 7 years later. In a second study, training and experience-matched control participants were assessed at the beginning and end of a 1-month insight meditation retreat. Across both studies, training participants engaged in less mind wandering and less mindless reading following meditation training. Intensive meditation training may promote reductions in mind wandering among practitioners when required to maintain attention during a complex cognitive task.
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Highly motivated students often exhibit better academic performance than less motivated students. However, to date, the specific cognitive mechanisms through which motivation increases academic achievement are not well understood. Here we explored the possibility that mind wandering mediates the relation between motivation and academic performance, and additionally, we examined possible mediation by both intentional and unintentional forms of mind wandering. We found that participants reporting higher motivation to learn in a lecture-based setting tended to engage in less mind wandering, and that this decrease in mind wandering was in turn associated with greater retention of the lecture material. Critically, we also found that the influence of motivation on retention was mediated by both intentional and unintentional types of mind wandering. Not only do the present results advance our theoretical understanding of the mechanisms underlying the relation between motivation and academic achievement, they also provide insights into possible methods of intervention that may be useful in improving student retention in educational settings.
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During recent decades numerous yoga-based practices (YBP) have emerged in the West, with their aims ranging from fitness gains to therapeutic benefits and spiritual development. Yoga is also beginning to spark growing interest within the scientific community, and yoga-based interventions have been associated with measureable changes in physiological parameters, perceived emotional states, and cognitive functioning. YBP typically involve a combination of postures or movement sequences, conscious regulation of the breath, and various techniques to improve attentional focus. However, so far little if any research has attempted to deconstruct the role of these different component parts in order to better understand their respective contribution to the effects of YBP. A clear operational definition of yoga-based therapeutic interventions for scientific purposes, as well as a comprehensive theoretical framework from which testable hypotheses can be formulated, is therefore needed. Here we propose such a framework, and outline the bottom-up neurophysiological and top-down neurocognitive mechanisms hypothesized to be at play in YBP.
As empirical research on mind-wandering accelerates, we draw attention to an emerging trend in how mind-wandering is conceptualized. Previously articulated definitions of mind-wandering differ from each other in important ways, yet they also maintain overlapping characteristics. This conceptual structure suggests that mind-wandering is best considered from a family-resemblances perspective, which entails treating it as a graded, heterogeneous construct and clearly measuring and describing the specific aspect(s) of mind-wandering that researchers are investigating. We believe that adopting this family-resemblances approach will increase conceptual and methodological connections among related phenomena in the mind-wandering family and encourage a more nuanced and precise understanding of the many varieties of mind-wandering.
The metronome response task (MRT)—a sustained-attention task that requires participants to produce a response in synchrony with an audible metronome—was recently developed to index response variability in the context of studies on mind wandering. In the present studies, we report on the development and validation of a visual version of the MRT (the visual metronome response task; vMRT), which uses the rhythmic presentation of visual, rather than auditory, stimuli. Participants completed the vMRT (Studies 1 and 2) and the original (auditory-based) MRT (Study 2) while also responding to intermittent thought probes asking them to report the depth of their mind wandering. The results showed that (1) individual differences in response variability during the vMRT are highly reliable; (2) prior to thought probes, response variability increases with increasing depth of mind wandering; (3) response variability is highly consistent between the vMRT and the original MRT; and (4) both response variability and depth of mind wandering increase with increasing time on task. Our results indicate that the original MRT findings are consistent across the visual and auditory modalities, and that the response variability measured in both tasks indexes a non-modality-specific tendency toward behavioral variability. The vMRT will be useful in the place of the MRT in experimental contexts in which researchers’ designs require a visual-based primary task.
Both animal and human research have revealed important associations between sighs and relief. Previously we argued to conceive of sighs as resetters which temporarily induce relief. The present study aimed to investigate the psychological and physiological relief effect of sighs by instructed deep breaths and spontaneous sighs compared to a control breathing maneuver. Participants completed three blocks of 40 trials during which uncertainty cues were followed by either safety cues followed by a positive picture, or danger cues followed by a negative picture. One block was presented without breathing instructions, two subsequent blocks with breathing instructions. During the presentation of the safety and danger cues, an instruction was given to either 'take a deep breath' or 'postpone the next inhalation for 2 seconds' (breath hold). Continuously, participants rated relief and Frontalis electromyography was recorded. Trait anxiety sensitivity was assessed by the Anxiety Sensitivity Index. Self-reported relief and physiological tension were compared 5s before and after instructed deep breaths and breath holds, and before and after spontaneous deep breaths and breath holds in the respective blocks. Results show that self-reported relief following an instructed deep breath was higher than before. Physiological tension decreased following a spontaneous sigh in high anxiety sensitive persons and following a spontaneous breath hold in low anxiety sensitive persons. These results are the first to show that a deep breath relieves and, in anxiety sensitive persons, reduces physiological tension. These findings support the hypothesis that sighs are psychological and physiological resetters.