Distinguishing vigilance decrement and low task demands from mind‐wandering: A machine learning analysis of EEG

Abstract

Mind‐wandering is a ubiquitous mental phenomenon that is defined as self‐generated thought irrelevant to the ongoing task. Mind‐wandering tends to occur when people are in a low‐vigilance state or when they are performing a very easy task. In the current study, we investigated whether mind‐wandering is completely dependent on vigilance and current task demands, or whether it is an independent phenomenon. To this end, we trained support vector machine (SVM) classifiers on EEG data in conditions of low and high vigilance, as well as under conditions of low and high task demands, and subsequently tested those classifiers on participants' self‐reported mind‐wandering. Participants' momentary mental state was measured by means of intermittent thought probes in which they reported on their current mental state. The results showed that neither the vigilance classifier nor the task demands classifier could predict mind‐wandering above‐chance level, while a classifier trained on self‐reports of mind‐wandering was able to do so. This suggests that mind‐wandering is a mental state different from low vigilance or performing tasks with low demands—both which could be discriminated from the EEG above chance. Furthermore, we used dipole fitting to source‐localize the neural correlates of the most import features in each of the three classifiers, indeed finding a few distinct neural structures between the three phenomena. Our study demonstrates the value of machine‐learning classifiers in unveiling patterns in neural data and uncovering the associated neural structures by combining it with an EEG source analysis technique.

Full text

Eur J Neurosci. 2020;52:4147–4164.
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4147
wileyonlinelibrary.com/journal/ejn
Received: 25 February 2020
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Revised: 1 June 2020
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Accepted: 3 June 2020
DOI: 10.1111/ejn.14863
RESEARCH REPORT
Distinguishing vigilance decrement and low task demands from
mind-wandering: A machine learning analysis of EEG
Christina YiJin
|
Jelmer P.Borst
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Marieke K.van Vugt
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original
work is properly cited.
© 2020 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd
Edited by Christoph M. Michel.
The peer review history for this article is available at https://publo ns.com/publo n/10.1111/ejn.14863
Bernoulli Institute for Mathematics,
Computer Science and Artificial
Intelligence, University of Groningen,
Groningen, The Netherlands
Correspondence
Christina Yi Jin, Bernoulli Institute
for Mathematics, Computer Science
and Artificial Intelligence, University
of Groningen, Nijenborgh 9, 9747 AG
Groningen, The Netherlands.
Email: yi.jin@rug.nl
Abstract
Mind-wandering is a ubiquitous mental phenomenon that is defined as self-generated
thought irrelevant to the ongoing task. Mind-wandering tends to occur when people
are in a low-vigilance state or when they are performing a very easy task. In the
current study, we investigated whether mind-wandering is completely dependent on
vigilance and current task demands, or whether it is an independent phenomenon.
To this end, we trained support vector machine (SVM) classifiers on EEG data in
conditions of low and high vigilance, as well as under conditions of low and high
task demands, and subsequently tested those classifiers on participants' self-reported
mind-wandering. Participants' momentary mental state was measured by means of
intermittent thought probes in which they reported on their current mental state. The
results showed that neither the vigilance classifier nor the task demands classifier
could predict mind-wandering above-chance level, while a classifier trained on self-
reports of mind-wandering was able to do so. This suggests that mind-wandering is
a mental state different from low vigilance or performing tasks with low demands—
both which could be discriminated from the EEG above chance. Furthermore, we
used dipole fitting to source-localize the neural correlates of the most import features
in each of the three classifiers, indeed finding a few distinct neural structures be-
tween the three phenomena. Our study demonstrates the value of machine-learning
classifiers in unveiling patterns in neural data and uncovering the associated neural
structures by combining it with an EEG source analysis technique.
KEYWORDS
alpha oscillation, independent component analysis, mind-wandering, support vector machine, task
demands, vigilance

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1
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INTRODUCTION
Mind-wandering is a ubiquitous mental phenomenon that
occurs throughout our daily life in a relatively uncontrolled
way. For instance, when you are working on your paper,
you suddenly realize that you have been thinking about your
plans for the upcoming weekend for a while even without
any clues in the environment to trigger such a daydreaming.
In psychology, mind-wandering is commonly defined as
self-generated thought that is irrelevant to the current task
and oriented toward other things such as unfinished long-
term goals (Smallwood & Schooler,2015). Mind-wandering
can be both positive and negative depending on the situation
in which it occurs (Smallwood & Schooler,2015), how dif-
ficult it is to disengage from (van Vugt & Broers,2016) and
how frequently the agent has to reorient their attention back
to the current task (Allen etal.,2013).
There are various internal and external factors that de-
termine the likelihood of mind-wandering. Two of the most
important are the level of vigilance as an internal factor and
task demands as an external factor. Mind-wandering is more
likely to occur when both vigilance and task demands are low.
In this paper, we will investigate whether mind-wandering is
fully dependent on those factors, or whether it is a separate
phenomenon. To this end, we will examine the relation-
ship between mind-wandering, vigilance and task demands
through training EEG classifiers to do across-categorization
predictions. If classifiers trained on task demands and/or vig-
ilance levels cannot predict the occurrence of mind-wander-
ing, we can conclude that mind-wandering is indeed a distinct
phenomenon. We decided to use machine-learning classifiers
instead of standard statistical analyses that rely on averages,
as those approaches can be fooled by an apparent average
overlap that is not accompanied by precise relationships on
individual trials. Classifiers are in that respect much more
precise, because for a classifier to generalize from one task
or context to the next, the relationships need to be present on
individual trials.
In the remainder of the introduction, we will review the
connections between mind-wandering, vigilance decrement
and the effects of task demand.
1.1
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Mind-wandering and
vigilance decrement
Mind-wandering is known to depend on vigilance: As a per-
son's vigilance decreases, their mind-wandering tends to in-
crease (Krimsky, Forster, Llabre, & Jha,2017). A vigilance
decrement is defined as reduced alertness to critical events
(Haubert etal.,2018). The difference between mind-wander-
ing and vigilance decrement is hard to tell by behavioral stud-
ies, since both are associated with worse task performance
due to a withdrawal of attentional resources from the main
task. For example, Pattyn, Neyt, Henderickx, and Soetens
(2008) showed that vigilance decrement was accompanied by
an increasing error rate. Also during mind-wandering people
tend to make more mistakes (McVay & Kane,2013).
In neuroimaging research, reduced vigilance is found to be
associated with increased activity in the default mode network
(DMN), which might play a role in the reduction in the abil-
ity to control responding that has been observed with reduced
vigilance (Bogler, Vowinkel, Zhutovsky, & Haynes, 2017).
Interestingly, the DMN is also regarded as a key network under-
lying mind-wandering (Bonnelle etal.,2011; Christoff, Irving,
Fox, Spreng, & Andrews-Hanna,2016; Durantin, Dehais, &
Delorme,2015). Additionally, electroencephalography (EEG)
studies have shown that vigilance declines are accompanied by
a reduced P3b (Haubert etal.,2018). P3b is an event-related
potential (ERP) observed as a positive deflection peaking at
around 500ms after stimulus onset with a maximal distribu-
tion over the parietal area. It is thought to reflect the amount of
mental resources devoted to the primary task (Polich,2007).
Similar P3 reductions were found in people reporting to be
mind-wandering (Barron, Riby, Greer, & Smallwood, 2011;
Smallwood, Beach, Schooler, & Handy,2008).
Together, this demonstrates that vigilance decrements and
mind-wandering overlap in their consequences for behavior
and brain activity measured through EEG or functional neu-
roimaging. This suggests that either the two are different fac-
ets of the same phenomenon, or alternatively, that they are
two phenomena co-occurring with each other. Regardless,
the evidence discussed above suggests that mind-wandering
will occur when people have reduced vigilance.
Interestingly, there is other evidence suggesting that
mind-wandering is not necessarily linked to performance defi-
cits resulting from decreased vigilance (Neigel, Claypoole,
Fraulini, Waldfogle, & Szalma, 2019). For example, while
sleep deprivation is a clear cause of fatigue and lower vigi-
lance (Massar, Lim, Sasmita, & Chee,2019), sleepiness and
mind-wandering have been shown to each have independent ef-
fects on task performance (Stawarczyk & D'Argembeau,2016).
One goal of the current study is to address this contro-
versy about the relationships between mind-wandering and
vigilance. Our hypothesis starts from the aforementioned
“vigilance decrement == mind-wandering” assumption, ac-
cording to which a vigilance decrement could be an indica-
tion of mind-wandering. We have previously demonstrated
that it is possible to detect the occurrence of mind-wandering
on a single-trial level with a machine-learning classifier (Jin,
Borst, & van Vugt,2019). Such classifiers can also be trained
to detect other mental states, including potentially vigilance.
If mind-wandering and vigilance decreases are both mani-
festations of the same underlying process, then a classifier
that can successfully detect low-vigilance moments should
be able to detect the occurrence of mind-wandering as well.

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1.2
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Mind-wandering and task demands
Another factor that affects the occurrence of mind-wandering
is the level of task demands. Behavioral studies have demon-
strated that increasing task demands tend to reduce the mind-
wandering rate, provided that task demands do not exceed the
capacity of the participants (Randall, Beier, & Villado,2019;
Smallwood et al., 2011). Interestingly, neuroscience studies
show that mind-wandering relies on similar neural structures
as being on task; both are associated with the activation of vis-
ual cortex and the prefrontal cortex—which is one of the main
reasons why it is difficult to distinguish mind-wandering from
other mental processes (Christoff, Ream, & Gabrieli, 2004).
In addition, recent evidence shows that the DMN is involved
in both on-task and off-task processing (Sormaz etal., 2018).
These overlapping neural structures can be explained by the
fact that both successful task performance and the generation
of mind-wandering episodes require the involvement of execu-
tive attention (Smallwood & Schooler,2006). A decline in task
performance resulting from mind-wandering could be viewed
as the result of the mind-wandering process “winning” the
competition for cognitive resources from the main task. If the
occurrence of mind-wandering depends on a competition with
task goals, then increasing the importance of these goals should
also decrease the amount of mind-wandering because there are
fewer cognitive resources left to support mind-wandering (van
Vugt, Taatgen, Sackur, & Bastian,2015). If this is true, then
low levels of task demands should be accompanied by high
levels of mind-wandering, while high levels of task demands
should be accompanied by low levels of mind-wandering.
The goal competition account gives rise to the second hy-
pothesis we address in the current study. Following the same
logic as in the “vigilance” section, if the “low demands ==
mind-wandering” assumption is true, a trained classifier de-
tecting people's attentional state when performing tasks of low
versus high demands should be able to detect mind-wander-
ing as well. That is, low task demands should predict a higher
likelihood of mind-wandering, while high task demands are
expected to be associated with more “on-task” responses.
1.3
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Alpha oscillation as a neural marker
While most of the previously discussed studies of mind-
wandering relied on neuroimaging or event-related poten-
tials, there have been also several studies using oscillatory
EEG information. Oscillatory EEG studies have demon-
strated that vigilance, mind-wandering and task demands are
all sensitive to periodic activity in the 8–12Hz alpha range.
In vigilance research, alpha oscillations are regarded as an
index of alertness. When people enter a calm and relaxed state,
alpha oscillations tend to increase in amplitude to the extent
that it is often even visible to the naked eye. Reduced vigilance
is shown to be accompanied by increased alpha power (Molina,
Sanabria, Jung, & Correa,2017). In a simulated driving task,
the participants had increased alpha band activity as they were
fatigued (Craig, Tran, Wijesuriya, & Nguyen,2012).
In research on attention, task performance on a percep-
tual task was shown to be better when alpha power, espe-
cially at posterior sites, was observed to be lower (Hanslmayr
etal., 2005). Alpha power has also been shown to be lower
for detected compared to undetected stimuli (Ergenoglu
etal.,2004), predictive of visual discrimination performance
(van Dijk, Schoffelen, Oostenveld, & Jensen, 2008) and
lower contralateral to the attended visual hemisphere (Thut,
Nietzel, Brandt, & Pascual-Leone,2006). In that sense, alpha
power could index the deployment of mental effort. If peo-
ple devote more effort or resources to the external task, their
alpha oscillatory activity at the parietal–occipital sites is ex-
pected to be inhibited.
In mind-wandering research, alpha oscillations have
been shown to be a consistent predictor of this mental state
(Baldwin etal., 2017; Jin etal., 2019; Macdonald, Mathan, &
Yeung,2011). In a recent study, we verified this idea by contrast-
ing the ability of different EEG features to predict mind-wander-
ing. Oscillatory power in the alpha band from occipital channels
turned out to be the most predictive EEG feature for mind-wan-
dering (Jin etal.,2019). This result is compatible with findings
from neurofeedback research, which show that a reduction in
alpha oscillatory power is accompanied by an increase in peo-
ple's focus level in a post-training task (Ros etal.,2013).
The primary role attributed to alpha oscillations is atten-
tional suppression: Alpha power enhancement allows the par-
ticipant to ignore irrelevant stimuli more effectively (Kelly,
Lalor, Reilly, & Foxe,2006) and is thought to reflect sup-
pression of the visual dorsal stream during the construction
of internal operations (Tuladhar etal., 2007). In that sense,
alpha oscillations might function to prevent the attentional re-
sources from being captured by “unwanted” outside stimuli,
thereby keeping the internal operations from being interfered
with. The top-down modulation of attention during the early
encoding phase has been reported to be supported by the pre-
frontal cortex (PFC). Contributing to the selectivity of the
attention when resources are guided toward the relevant stim-
uli and ignoring the distractions, this top-down modulation is
found to be associated with frontal–parietal coherence in the
alpha band (Zanto, Rubens, Thangavel, & Gazzaley,2011).
1.4
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Current study
Based on the similarity of reported alpha oscillation activ-
ity between mind-wandering versus on-task, low versus high
vigilance, and low versus high task demands, combined with
our previous two assumptions (“mind-wandering == low
vigilance,” “mind-wandering == low task demands”), we

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hypothesized that a successful classifier based on alpha os-
cillations that is trained on a vigilance or task demand cat-
egorization should be able to detect mind-wandering as well.
To test this idea, we designed a visual search task in which
we manipulated the levels of task demands. The main stim-
uli were visual search panels with geometric graphics. In the
low-demand condition, participants passively viewed the search
panel and hit a key when done. In the high-demand condition,
participants counted a specific target during the presentation of
the search panel and entered their count on the keyboard. The
reason for employing such a paradigm is to ensure that partici-
pants receive the same input to their perceptual system while at
the same time having to devote varying amounts of mental effort
between conditions. Therefore, if we find any difference in their
brain signal during sensory processing, we can rule out the pos-
sibility that it was caused by the different stimuli or response key
presses per se. Crucially, participants were intermittently probed
to provide a subjectively rated estimate of the extent to which
they were currently mind-wandering or on-task.
After the visual search task, participants performed a sus-
tained-attention-to-response task (SART), which is a classic
paradigm used to study mind-wandering. In this task, partici-
pants are presented with a frequent GO stimulus requiring a re-
sponse and an unpredictable rare NOGO stimulus, which is used
to detect momentary lapses of attention since participants have
to overcome the automatic tendency to respond using inhibitory
control. The same thought probe questions as used for the visual
search task were inserted into the trial sequence of the SART.
In our machine-learning approach, the training data always
came from the visual search task. Based on this, three models
could be built: a vigilance classifier, a task-demand classifier
and a self-rated mental state classifier. The test data always
came from the other task, SART, and were only labeled by the
self-reported attentional state. If our hypothesis is correct, the
classifier trained on the vigilance labeling or task demands la-
beling should achieve an equivalent level of prediction accu-
racy as the classifier trained on mind-wandering in predicting
mind-wandering in the SART. If not, or in other words, if the
vigilance and/or task demands classifier score lower than the
self-report classifier, this would indicate that mind-wandering
is a different mental state from low vigilance or being in a task
with low demands as indicated by different EEG patterns.
The goal of the current research is to determine whether
vigilance decrement and low task demands are similar to
mind-wandering, or whether these are qualitatively different
processes. We aim to achieve this goal through detecting the
relevant EEG patterns using machine learning. If mind-wander-
ing is shown to be different from the other two mental phenom-
ena, this allows us to examine how the classifiers differentiate
between them. The results will help us to further understand
the commonalities and distinctions between mind-wandering,
vigilance decrement and performing a task with low demands
while they all show a similar “attentional decline” behaviorally.
2
|
METHODS
2.1
|
Participants
Thirty participants (16 females, age 18–31years, M=23.73,
SD = 3.47) took part in the current study. They reported
normal or corrected-to-normal vision. The research was
conducted in accord with the Declaration of Helsinki and
approved by the Research Ethics Committee of the Faculty
of Arts (CETO), University of Groningen. Participants gave
written informed consent. They were paid 16 euros for their
participation in the two-hour experiment (duration includes
the EEG setup time). They were debriefed with the main goal
of the task after the experiment.
2.2
|
Experimental procedure
The experiment consisted of two tasks: a visual search
task and a SART. Participants performed the experiment
in a sound-attenuated booth. Both tasks were programmed
and presented in the PsychoPy3 standalone version (Peirce
etal.,2019). Continuous EEG was recorded during the task
with a Biosemi 32-channel system.
The visual search task involved two conditions that manipu-
lated task demands: a counting (high demand) and a non-count-
ing (low demand) condition. In both conditions, a display with
geometric shapes was shown to the participants (Figure 1).
Task conditions were varied on a block-by-block basis. Each
block started with the instruction for the current block. In a
counting block, the instruction was “Count the targets and press
the number to indicate the answer after the display disappears.”
In the non-counting block, the instruction was “Press J after
the display disappears.” The experiment was divided into four
sessions. Each session consisted of five blocks of the same con-
dition. The session sequence followed either an “A-B-A-B” or
“B-A-B-A” style, which were balanced between participants.
There were in total 10 blocks for each condition.
All stimuli were shown in white against a gray background
(Figure1; PsychoPy3 default). Each trial started with a fixation
cross shown for a random duration sampled from the interval
between 0.5 and 1.5s. After the fixation cross disappeared, par-
ticipants saw a trial instruction for 2s about what target to count
in the following search panel in the counting condition. A target
could be a triangle, square or pentagon. Following the instruc-
tion, a display consisting of geometric graphics was presented.
Participants were asked to count the specified target or just pas-
sively view it. The search panel lasted for 4s. After that, there
was a blank screen during which participants had infinite time
to make a response: In the counting task, they were required to
input a number on the numeric keypad to indicate their counting
result; in the non-counting task, they simply pressed “j.” The
inter-trial-interval was on average 1s (range 0.5–1.5s).

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The search panel was a centrally localized 10.6×10.6cm
square (visual angle 5.0°×5.0°), equally divided over an in-
visible 10×10 grid (100 stimulus holders). Each geometric
graphic was randomly assigned to one holder. The number
of copies of each shape varied between two and eight in each
search panel. The target shape was counterbalanced between
trials. There were 21 trials in each block, where each block
lasted for approximately 3min.
As in previous mind-wandering studies, we examined par-
ticipants' mental states by means of thought probes. Unlike
previous studies, however, the thought probes were not ran-
domly interspersed between trials but rather were placed at
the end of each block. In each thought probe, participants
were shown a continuous rating scale, asking “To what de-
gree were you focusing on the task just now?”. Participants
could choose an integer between −5 and 5 to indicate their
momentary attentional level, with anchors “−5” for “totally
mind-wandering,” “−2” for “mind-wandering,” “0” for “un-
certain,” “2” for “focused” and “5” for “highly focused.”
The SART is a GO-NOGO-based experiment paradigm,
in which participants are supposed to press a button when
they encounter GO stimuli (which are presented most of time)
and withhold their response to occasionally presented NOGO
stimuli. Here, the main stimuli in the SART were single digits
from 1 to 9. Participants were required to press “j” as soon as
they saw the number with an exception for “3” (around 11%
chance), which was the NOGO stimulus. Numbers were coun-
terbalanced across trials. Each trial started with a cross fixation
lasting for 0.5–1.5s, followed by a digit for 250ms and a blank
screen for 1,750ms. Participants could press the button any
time after the digit stimuli onset. The SART consisted of 12
blocks with block length varied between 2 and 7 repetitions of
the 9 digits (18–63 trials, approximately for 1–3min). At the
end of each block, they were asked to rate their momentary at-
tentional level on the same scale used in the visual search task.
Participants were seated at a distance of approximately 60cm
from the display. Before the experiment, they were told the aim
of the experiment was to study people's attentional fluctuations.
They were further explained about the rating scale they would
encounter during the tasks and were asked to use this rating
scale honestly and to indicate the content of their thoughts seri-
ously. Participants performed practice trials until they reached
a certain level of accuracy (for the visual search task, 4 out of 5
correct responses in the most recent 5 consecutive trials; for the
SART, one correct withholding of their response to the NOGO
stimulus “3”; on average, participants practiced 11.8±2.3 trials
for the visual search task and 14.7± 9.3 trials for the SART)
before they performed the real experiment. Accuracy feedback
was given during the practice to facilitate learning of the task
but withheld during the actual experiment. After finishing each
block, participants could take a short break if they so desired.
2.3
|
Trial classification
The training of classifiers to detect mind-wandering, vigilance
and task demands is based on three different ways of labeling
the data. The task demands classifier labels are based on the
experimental conditions, in which counting task trials reflect
high task demands and non-counting task trials reflect low
FIGURE 1 Trial sequences of the
visual search task (top) and the sustained-
attention-to-response task (SART; bottom).
In the counting visual search task (difficult
task condition), participants were required
to count the number of occurrences of the
target stimulus in the search panel and press
the number key indicating the counting
result as a response. In the non-counting
visual search task (easy task condition),
participants were asked to passively view
the search panel and simply press “j” after
its termination. In the SART, participants
were required to press “j” every time they
saw a non-three digit and to withhold their
response when the digit “3” occurred

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task demands. The vigilance classifier labels are based on the
time course of the task, which categorizes the first half of the
trials, during which the participant is still relatively fresh, into
the high-vigilance category, and the second half of the trials
into the low-vigilance category. The mental state classifier la-
bels were based on the self-rated attentional level, which la-
bels the three trials preceding each probe (comprising a total
duration of approximately 24s1) as mind-wandering if the rat-
ing is smaller than 0, and as on-task if the rating is larger than
0 (thus, ratings were converted into a binary classification of
on-task vs. mind-wandering; ratings of 0 were removed be-
cause they indicated “uncertain”; this removed 9.6% of the
ratings).
Data of the SART were only classified as either mind-wan-
dering or on-task based on the participant's self-report ratings of
their mental state—and not in terms of vigilance or task difficulty.
2.4
|
Behavioral data analysis
Individual accuracy was computed and a group-level compari-
son within each categorization was performed using paired t
tests. Individual mean response time (RT) was computed on the
basis of correct trials only, additionally excluding 2.5% of the
data at each end of the RT distribution to exclude outliers. Due
to the non-normality, RT was log-transformed before being
compared with paired t tests.2 The effect size was reported as
Cohen's d.
Individual focus-level rating scores were computed ex-
cluding ratings of “zero,” which indicated the participant was
“uncertain” about their mental state. A group-level one-factor
repeated-measures ANOVA was performed to examine the dif-
ferences between the counting, non-counting and SART. When
we observed a significant difference, pairwise comparisons
were performed using paired t tests and reported with adjusted
p-values using the Bonferroni correction for multiple compar-
isons. Meanwhile, the focus-level rating scores were also ex-
amined using a group-level t test between the first half (“high
vigilance”) and the second half (“low vigilance”) of the visual
search task.
2.5
|
EEG recording and offline
preprocessing
EEG was recorded by a Biosemi 32-channel system with six
additional electrodes to detect eye movements and mastoid sig-
nals. The scalp channel locations are within the International
10–20 System. The sampling rate was 512Hz. An electrode
next to the vertex electrode was used as the reference during
recording. Impedances were kept below 40kΩ. The Biosemi
hardware does not have any high-pass filtering. An anti-alias-
ing filtering (low-pass filtering) is performed in the ADC's dec-
imal filter (hardware bandwidth limit), which has a fifth-order
cascaded integrator-comb filter response with a -3 dB point at
1/5th of the selected sample rate. For more, see the Biosemi
website (https://www.biose mi.com/faq/adjust_filter.htm)
Offline EEG preprocessing was performed using the
EEGLAB toolbox (v14.1.1, Delorme & Makeig, 2004) on
MATLAB (v2013b). Bad channels were defined as channels
with excessive spikes or obviously noisier than surrounding
channels. There were two participants with bad channels—
each having exactly one bad channel. Data for each of these
two bad channels were replaced through spherical interpo-
lation based on the neighboring electrodes. Continuous data
were re-referenced to the average signal of both mastoids,
band-pass-filtered (0.1–42Hz), down-sampled to 256Hz and
segmented to epochs of 4s (1s before and 3s after stimulus
onset). Baseline correction was performed using the interval
of −200 to 0ms as a baseline. Data were visually inspected for
large movement artifacts. An infomax independent component
analysis (ICA) was used to detect and remove ocular artifacts.
EEG epochs were visually inspected again after the ICA arti-
fact removal to ensure the remaining signal to be clean.
An EEG data analysis pipeline including preprocessing,
feature extraction, machine learning and feature testing pro-
cedures is illustrated in Figure2.
2.6
|
Feature computation
The classifier features consisted of the time window from
600ms before to 600ms after stimulus onset (stimulus onset
is defined as the search panel onset in the visual search task
and the digit onset in the SART).3 In these windows, we com-
1The window length was based on previous work predicting mind-
wandering in EEG (Jin etal.,2019), in which we tested the difference in
the classification results when using data from 30 and 15s before each
probe question. In that study, we found that the classifier trained using the
shorter time window (15s) did not outperform the one trained using the
longer time window (30s). We therefore decided to use a 24-s time
window before each probe in the current study. Another consideration is
that shrinking the time window would cut the training data sample size,
thereby potentially causing trouble for the machine-learning procedure.
2The log-transformed RTs of each group for t tests were examined by the
Shapiro–Wilk tests of normality. None of the null hypothesis that the
population is normally distributed was rejected for an alpha level of 0.05.
3We used the interval up to 600ms after the stimulus because that was close to
the time when participants finished making a response in the SART (mean
response time is around 500ms as shown in Figure3). In the visual search task,
participants were still processing the stimulus at this time because in this task
participants were required to wait with responding until the search panel
disappeared. After participants finished a response, their cognitive state and
motor actions would be difficult to control. Therefore, we decided to cut the
window at 600ms after the stimulus onset. To make a symmetric time window,
we also took 600ms before the stimulus onset into consideration because
pre-stimulus activity may be predictive of our variables of interest as well.

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FIGURE 2 EEG data analysis pipeline. After preprocessing, we performed band-pass filtering on the clean EEG signals to obtain the
alpha oscillatory activity (8.5–12Hz). A further independent component analysis (ICA) was performed on the resulting alpha power over all the
participants to isolate the time series into 32 independent components (ICs), of which we took 100ms as a bin and computed the mean power over
each bin as the final features for classification. The classifier training consisted of two stages. First, we used the power of all 32 ICs as features
and trained three types of the classifiers—task demands classifier, vigilance classifier and self-report classifier—using the data from the visual
search task (whole modeling). All the classifiers were validated through both the 10-fold cross-validation and the leave-one-participant-out cross-
validation (LOPOCV). For the across-task predictions, we tested all the three classifiers on the data from the SART, which were only labeled based
on the self-reports. Secondly, we took the power of each IC to train the three classifiers, respectively (feature testing). The performance of each IC
during the LOPOCV was compared to the chance level using the one-sample t tests to obtain the significant ICs in each type of classifier. Further,
we applied the dipole-fitting analysis to each of the significant ICs to unveil the underlying neural correlates [Colour figure can be viewed at
wileyonlinelibrary.com]
FIGURE 3 Response time (in seconds)
and accuracy for conditions differing in
task demands (top left), vigilance (top
right) and self-reported mental state
(bottom). Response time is shown bars,
and accuracy is shown as points. Both error
bars indicate one between-subject standard
error (SE) [Colour figure can be viewed at
wileyonlinelibrary.com]

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puted alpha band activity based on a frequency range of 8.5–
12 Hz. The ideal filter kernel was in a “plateau” shape in
which 8.5–12Hz were set as 1, while the other frequencies
were set as 0 with 20% as transition width. The kernel length
was set to at least three cycles of the lowest frequency
(8.5Hz). The kernel was constructed by the MATLAB func-
tion firls(). The sum of squared errors (SSE) of the con-
structed kernel and the ideal kernel was 0.1. To obtain
oscillatory power, the band-pass-filtered data were Hilbert-
transformed by means of the MATLAB function hilbert().
From this analytical signal (in a complex form), the oscilla-
tory activity was computed through taking the real parts, and
power was computed as the square of the vector length at
each time point (Cohen,2014).
Independent component analysis was used to compute
features in the current study. The main reason to use such IC-
based features is to allow good fitting of the follow-up source
localization analysis. ICs are assumed to be unmixed signals
from different sources (Cohen,2014) and are recommended
in reconstructing the signal source (Delorme, Makeig, Fabre-
Thorpe, & Sejnowski,2002). IC loadings were obtained by
applying ICA on the alpha band oscillations from all partic-
ipants. Features in our classification analyses are the IC ac-
tivities, which can be regarded as a weighted sum of signals
from each channel using the obtained IC loadings during the
ICA. Thus, each IC activity of each trial is a single oscillatory
time course signal of which power can be computed. We took
a step size of 100ms and separated the 1,200ms epoch into
12 bins, for each of which the average power was computed.
This resulted in 12 features for each IC and 32 ICs leading to
12×32=384 features in all.
2.7
|
Classifier training and testing
We used a support vector machine classifier (SVM) due to
its excellent performance in EEG classification in previous
studies of mind-wandering (Jin etal.,2019; Lotte, Congedo,
Lecuyer, Lamarche, & Arnaldi,2007). An important reason
for using an SVM is that this classifier does not require a lin-
ear relationship between the features, which is an advantage
in classification when we cannot make a specific assumption
about the shape of the interacting pattern between the fea-
tures. We applied the radial basis function (RBF) kernel dur-
ing the training to take advantage of the non-linear power of
the SVM. The regularization term C was set to be 1. The pa-
rameter γ of the RBF kernel was set to be 1/(data dimension).
The training and testing procedures ware mainly performed
by the e1071 package in R combined with customized code.
Each feature was z-transformed, and the training sample was
balanced between the two classes using the random over-sam-
pling method. The training was performed on the pooled data
of the visual search task from all participants and validated in
a combined 10-fold cross-validation (CV) and a leave-one-par-
ticipant-out cross-validation (LOPOCV) framework. In each
iteration, one participants' data were kept aside for validation
purposes, while the remaining data constituted the training sam-
ple, on which a classifier was built and validated by means of
10-fold CV. Based on the different categorizations as described
in Section 2.3, we trained three classifiers in each iteration: a
task-demand classifier, a vigilance classifier and a self-reported
mental state classifier (to assess mind-wandering).
Furthermore, the classifiers were tested in an across-task
fashion (Jin et al., 2019) to predict the data of the SART.
We expected that the task demand classifier or the vigilance
classifier could achieve an equivalent level of accuracy to the
self-reported mental state classifier in predicting being on-
versus off-task in the SART if task demands and vigilance
are comparable mental states to mind-wandering. The theo-
retical chance level of a binary classification problem is 0.5.
However, we decided to use a corrected chance level consid-
ering the data size according to Combrisson and Jerbi (2015).
At the conventional significance threshold of p<0.05, the cor-
rected chance level is 50.77% for the task demands/vigilance
classifiers (both n = 11,436) and 52.14% for the self-report
classifiers (n=1,494). The chance levels in the later sections
of the manuscript all refer to these corrected chance levels.
2.8
|
Feature testing and source localization
To investigate the possible neural interpretation of the
trained classifier, we did a feature testing analysis, for
which each IC was used as the only predictor in training
the classifier. The predictiveness of each feature (IC) was
quantified by the accuracy during the LOPOCV. For each
feature, we could then determine whether it contributed
significantly to the prediction of interest by comparing its
accuracy to the chance level using one-sample tests. A fur-
ther dipole-fitting procedure was performed on those sig-
nificant ICs to unveil the underlying neural structure. The
dipole fitting and visualization were performed using the
DIPFIT2 plug-in of EEGLAB. A boundary element model
(BEM) was used to fit the data. As our electrode locations
are all within the International 10–20 System, we simply
used the standard channel coordinates associated with the
head model. Dipole positions were reported in Montreal
Neurological Institute (MNI) coordinates.
3
|
RESULTS
3.1
|
Behavioral results
Figure 3 shows the average accuracy and response time.
Paired t tests were performed to compare the data between

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the two conditions. To assess the effectiveness of our task
manipulation, we compared counting to non-counting
task performance (our manipulation of task demands).
As we intended, there was a significant difference be-
tween the two tasks in both the accuracy (t(29)=−9.54,
p<0.001, d=1.74) and the log-transformed response time
(t(29)= 7.16, p<0.001, d=1.31). Participants obtained
lower accuracy and made slower responses in the count-
ing task (accuracy: M=0.92, response time: M=0.654s)
compared to the non-counting task (accuracy: M =1.00,
response time: M=0.480s).
For the comparison between the first and second half of
the experiment (our proxy for vigilance), we found no signif-
icant difference in the accuracy (t(29)=0.33, p=0.742) but
a significant difference in the log-transformed response time
(t(29)=2.23, p=0.034, d=0.41). Participants made faster
responses in the second half (M =0.555) compared to the
first half (M=0.569s) of the visual search task.
Because only 25 out of the 30 participants reported
mind-wandering moments (defined as ratings<0) to the
thought probe in the visual search task, the difference be-
tween the on-task and mind-wandering state could only be
compared for these 25 participants (the exact trial count
in each condition for each participant can be found in
Appendix S2). We did not find any significant differences
in task performance between the two self-reported mental
states (mind-wandering vs. on-task); neither in accuracy
(t(24)=0.53, p=0.597) nor in log-transformed response
time (t(24)=−0.04, p=0.966). A Bayes factor analysis
of these non-significant differences suggests that accuracy
is equivalent between mind-wandering and on-task (BF10
is 0.24, indicating that the null hypothesis is more than 4
times as likely as the alternative hypothesis) and the same
applies to the log-transformed response time (BF10 is 0.21,
indicating that the null hypothesis is almost 5 times as
likely as the alternative hypothesis).
Similarly, as there were only 14 participants for whom the
reports contained both mind-wandering and on-task cases in
the SART, we performed paired t tests on the data from those
14 participants to examine the effect of the self-reported men-
tal state on SART performance. The results did not show any
significant difference between mind-wandering and on-task;
neither in accuracy (t(13)=1.31, p=0.214), nor in log-trans-
formed response time (t(13)= −1.25, p= 0.235). A Bayes
factor analysis shows that this failure to find a significant dif-
ference is the result from too much uncertainty in the data to
draw any definitive conclusion (Bayes factor BF10 is 0.55 for
accuracy and 0.52 for log-transformed response time, in both
cases suggesting that the null hypothesis is about twice as
likely as the alternative hypothesis).
The averaged focus-level rating scores were computed
excluding ratings of “zero,” which indicated the participant
was “uncertain” about their mental state. A group-level re-
peated-measures ANOVA with the factor “task” was per-
formed to examine the differences between the counting,
non-counting and SART, which turned out to be significant
(F(2, 58)=11.28, p<0.001, η2=0.1). Pairwise comparisons
using paired t tests suggested a significant difference between
the counting (M=1.24) and non-counting tasks (M=0.002,
pcorrected= 0.003) and between the non-counting task and the
SART (M=1.38, pcorrected=0.002). Participants rated them-
selves as being more focused in the counting task and the SART
compared to the non-counting task (Figure4, left column).
A paired t test was performed to examine whether there
was a significant difference between the first half (“high vigi-
lance”) and second half (“low vigilance”) of the visual search
task. A significant effect of time course was found, indicating
that the focus level was higher in the first half (M =1.29)
than the second half of the visual search task (M =−0.09,
t(29)=5.52, p<0.001, d=1.0) (Figure4, right column).
3.2
|
Classification results
We then examined whether there were systematic differences
in brain activity that could be picked up by a classifier. A sum-
mary of the different classifiers' performance can be found in
Figure 5. t tests between the mean classification accuracy and
chance level indicated that all the classifiers surpassed chance
level in the 10-fold CV and the LOPOCV analyses (ts>2.80,
ps<0.01). However, when predicting the self-reported mental
states in the visual search task or the SART, the task demands
FIGURE 4 Mean focus-level rating
scores across tasks (left) and across time
(right). Error bar indicates one between-
subject standard error (SE). SART,
sustained-attention-to-response task; VS,
visual search task

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classifier and the vigilance classifier remained just around
chance level (ts<1.38, ps>0.13); the self-report classifier
showed above-chance performance in the SART (t = 2.89,
p=0.007).4 In other words, neither the vigilance nor the task-
demand classifiers could predict mind-wandering while the
self-report classifiers were able to do so across tasks.
3.3
|
Feature testing and dipole fitting
We then asked on what EEG features the classifiers based their
predictions. The contribution of every feature to the classifica-
tion was assessed by examining the accuracy of classification
based on that single feature alone, computed through LOPOCV.
These accuracies are shown in Figure6. t tests comparing the
prediction accuracy of each feature to chance level indicated
that IC 2, 3, 4, 5, 6, 7, 9 and 15 were able to predict the level
of task demands when assessed using LOPOCV. IC 1 and 26
could predict the participants' vigilance level when assessed
using LOPOCV. IC 2, 4 and 17 could predict the participants'
self-reported mental state when assessed using LOPOCV.
There were no ICs that could predict all three types of labels.
Next, significant ICs were divided into groups based on
whether their importance is restricted to one single classifier
or whether it plays an important role in multiple classifiers.
The results are shown in Figure7 (left). We found IC 3, 5, 6,
7, 9 and 15 predicted only the level of task demands. IC 1 and
26 predicted only vigilance. IC 17 predicted only the self-re-
ported mental state. Besides, we found that IC 2 and 4 pre-
dicted both the self-report and task demands. The scalp maps
of all the significant ICs can be found in Figure7 (right).
A summary of the dipole-fitting results of all the signifi-
cant ICs can be found in Table 1. IC 1 had clear symmetric
activity; thus, we fine-tuned to fit two dipoles for this compo-
nent following the recommended practice.5 After fitting IC1
with two dipoles, the residual variance (RV) dropped from
4Because we trained the task demands and vigilance classifiers over all
trials while the self-report classifier was built upon three trials before each
probe, to rule out the possibility that the higher performance in the
self-report classifier in prediction the self-reported mental states in the
SART could be caused by a lower amount of data, we performed the same
analysis with all classifiers trained with three trials before each probe. The
results can be found in Appendix S1. Just as the results in the main text,
both the task demands and vigilance classifiers performed above the chance
level during the 10-fold CV and LOPOCV (ts>2.08, ps<0.04). When
predicting the self-reported mental state in both tasks, the task demands and
the vigilance classifier did not surpass chance level (ts<0.37, ps>0.164).
5https://sccn.ucsd.edu/wiki/A08:_DIPFIT
FIGURE 5 A comparison of performance of the three classifiers in the 10-fold cross-validation (CV), the leave-one-participant-out cross-
validation (LOPOCV) and in predicting the self-reported mental states in both the visual search task (Reports VS) and the SART (Reports
SART). Note the self-report classifier (green bar) is identical in the LOPOCV and Reports VS; the repetitive graphing is to allow for easy visual
comparison. The horizontal dashed line indicates chance levels (0.5077 for task demands/vigilance and 0.5214 for self-report). The error bar
reflects one between-subject standard error (SE). Sensitivity and specificity as complementary indicators of classifiers' biased detection can
be found in Appendix S3 (An inspection of the sensitivity and specificity of the classifiers indicates that the vigilance/task demands classifiers
achieved relatively unbiased detection [comparable sensitivity and specificity] while the self-report classifier had an obvious biased detection to
the on-task trials [low sensitivity and high specificity]. Similar result has been observed before—the SVM seemed to be good at detecting the
majority cases, Jin etal.,2019. However, such biases were not an outcome of the unbalanced dataset since we performed balancing procedure
during the training. Instead, it is likely to be caused by the noise during the labeling using the self-report methodology that increases the difficulty
in differentiating mind-wandering from on-task, Jin etal.,2019.). Asterisks indicate the difference between the accuracy and chancel level is
significant using one-sample t tests (***p<0.001, **p<0.01) [Colour figure can be viewed at wileyonlinelibrary.com]

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16% to the current 2.8%. The two fitted dipoles of IC1 over-
lapped in their coordinates, but their moments were oriented
in different directions (Figure7). The other components were
all fitted with a single dipole. The dipole coordinates of IC 5
and 26 could not be matched to defined brain regions, and
hence, those components were removed from further discus-
sion. The mapping of the remaining dipoles to an MRI brain
image can be found in Figure8.
To understand how the ICs might differ between the two
conditions within each categorization, we plotted the power
of the significant ICs as a percentage change relative to a
[−600 –200] millisecond baseline for both conditions in the
respective categorizations in Figure9.
4
|
DISCUSSION
The current research examined the hypothesis that low vigi-
lance or performing a task with low demands is associated
with a similar neural state as mind-wandering. In order to
FIGURE 6 Feature testing results quantified as accuracy measured with LOPOCV separately for each independent component (IC) sorted by
their mean accuracy. Accuracies that were significantly above-chance level (vertical dashed line) are marked by asterisks before the corresponding
IC label that also indicate the level of significance (*p<0.05, **p<0.01, ***p<0.001). The error bar indicates 95% confidence interval
FIGURE 7 Left: Overlap in ICs used
in prediction by the three classifiers. Right:
Topoplots of the 11 significant ICs with
dipole positions. The number in brackets
indicates the residual variance (RV)
associated with the respective component,
essentially a measure of the error in the
dipole fits [Colour figure can be viewed at
wileyonlinelibrary.com]

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test this hypothesis, we asked participants to perform a visual
search task and a SART while measuring their momentary
focusing state through intermittent probes. The analysis of
the attentional rating scores to the probes indicated we were
successful in manipulating participants' attentional state
(Figure4). Specifically, we found that participants reported
a general decline in attention over the course of the task, as
well as in a low- relative to high task demand situation. This
rating result matched our assumption that low task demands
or low-vigilance levels would indicate a decrease in focus
level.
However, despite the fact that behaviorally mind-wan-
dering, vigilance and task demands covaried, these factors
were not associated with similar brain states (Figure 5)
since—contrary to our expectation—neither a vigilance
classifier nor a task demands classifier could predict the
occurrence of mind-wandering as measured by self-report
above-chance level while a trained classifier trained specif-
ically on self-report could. This result is in contrast to both
our hypotheses—that the neural correlate of mind-wander-
ing is equal to the neural correlate of low vigilance and of
low task demands. The feature testing results (Figures6 and
7) provided us with clues about where these mental states
associated with vigilance, task demands and mind-wander-
ing may differ. A dipole-fitting analysis of the significant
ICs (Figures8 and 9) indicated that the differences between
mind-wandering, low vigilance and task demands can be
connected to specific neural correlates: Mind-wandering
is associated with the left superior temporal gyrus (STG,
IC17); task demands are associated with the right precu-
neus (IC3), the left temporal lobe subgyral (IC6), the bilat-
eral middle temporal gyrus (IC7, IC9), and the left middle
occipital gyrus (IC15); vigilance is associated with the
brainstem (IC1).
TABLE 1 MNI coordinates and brain area of the significant
independent components (ICs)
IC MNI-xMNI-yMNI-zBrain structure
1 0 −28 −38 Pons (brainstem)
2 −12 −77 35 Left precuneus
3 24 −62 30 Right precuneus
4 7 −23 56 Right medial frontal gyrus
5 0 −61 55 —
6 −27 −65 28 Left temporal lobe subgyral
7 −58 −40 −9 Left middle temporal gyrus
9 49 −61 3 Right middle temporal
gyrus
15 −28 −84 −16 Left middle occipital gyrus
17 −68 −27 6 Left superior temporal
gyrus
26 73 −37 16 —
Note: An empty place in the “area” column indicates a localization outside the
defined brain regions. Note that IC1 was fitted to two dipoles with the same
coordinate but different orientations.
FIGURE 8 Source localization of
the fitted dipoles of ICs that are significant
exclusively in a single classifier and
those that are significant in multiple
classifiers [Colour figure can be viewed at
wileyonlinelibrary.com]

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Despite the fact that we were in general not able to pre-
dict the occurrence of mind-wandering by using classifiers
trained on task demands or vigilance, we found that alpha
power generated from the left precuneus (IC2) and the right
medial frontal gyrus (IC4) was a common feature for both
task demands and mind-wandering.
In the remainder of this article, we will first discuss the ob-
served neural distinctions between mind-wandering and vigi-
lance, followed by the similarities and differences between the
neural correlates of mind-wandering and task demands. We
will end the discussion with limitations and future directions.
4.1
|
Mind-wandering and
vigilance decrement
We found alpha band power generated from dipoles located
in the pons of the brainstem (IC1) to be a predictive feature
of the vigilance classifiers. The brainstem is well known for
its crucial role in regulating the cycle between waking and
sleep (Buysse et al., 2004; Jones, 2011) and maintaining
alertness through its connectivity to the thalamus (Mottaghy
etal.,2006). Researchers further proposed a frontal–parietal–
thalamic–brainstem network for internally motivated and con-
trolled maintenance of alertness, and the right insula was found
to be a key structure in that network (Clemens etal.,2011).
While the current findings corroborate previous neuroscien-
tific studies of alertness, we found IC1, which was localized in
the brainstem, to have a stronger alpha desynchronization after
stimulus onset in a high versus low-vigilance state (Figure9).
To our knowledge, this link between brainstem alpha oscilla-
tion and vigilance has not been reported before.
For the self-report classifiers, we found that alpha power
generated from dipoles located in the left STG (IC17) was
predictive only in this classifier. The left STG has been found
to be an important neural correlate of semantic processing
(Friederici, Rueschemeyer, Hahne, & Fiebach, 2003), so-
cial cognition (Grace, Rossell, Heinrichs, Kordsachia, &
Labuschagne, 2018), and self-related information process-
ing (Qin et al.,2016). Moreover, the STG has been associ-
ated with memory recall (Mallow, Bernarding, Luchtmann,
Bethmann, & Brechmann, 2015) and with problem-solv-
ing (Tian etal.,2011). Together this suggests that the STG
might help to organize and generate thinking content during
mind-wandering. We found that when mind-wandering oc-
curs, STG-generated alpha band power desynchronized more
after stimulus onset compared to when the person is in an on-
task state (Figure9). Although reports about the alpha band
activity on temporal sites are rare, there is one study examin-
ing intracranial EEG in the hippocampus, which suggests that
alpha power decreases distinguished associative recognition
from non-associative item recognition (Staresina etal.,2016).
This is somewhat consistent with our finding that alpha de-
synchronization in the STG might indicate a memory retrieval
processes needed in the construction of daydreams.
To summarize, our study indicates that mind-wandering
and low-level vigilance are distinctive phenomena, at least in
their neural representations. While mind-wandering requires
the activity of the linguistic system and a memory retriev-
al-associated neural structure (left STG), vigilance decrement
is more about a general decline in the arousal system (brain-
stem), which manifests as reduced motivation and alertness.
While reduced vigilance can give rise to mind-wandering,
our data suggest it does not reliably do so.
FIGURE 9 Time courses of IC differences in each categorization as a function of time [−600 600] millisecond relative to stimulus onset.
Alpha power is indicated as a percentage change (%) compared to a [−600 –200] millisecond baseline. Note that baseline correction was used only
for visualization purposes; during the machine learning, the raw power without baseline correction was used for feature computations. Plots of
non-baseline corrected alpha power can be found in Appendix S4. Shaded area indicates one between-subject standard error (SE). The vertical and
horizontal line indicates zero in both axes, respectively [Colour figure can be viewed at wileyonlinelibrary.com]

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4.2
|
Mind-wandering and task demands
Alpha power from several cortical areas was found to be
predictive of task demands. Specifically, the areas that pre-
dicted task demands were the right precuneus (IC3), the left
temporal lobe subgyral (IC6), the bilateral middle temporal
gyrus (IC7, IC9) and the left middle occipital gyrus (IC15),
forming a wide parietal–temporal–occipital network, span-
ning both hemispheres (see also Figure8). This network has
previously been suggested to be associated with general cog-
nitive abilities (Ferreira etal.,2017), to facilitate the decod-
ing of visual inputs and the preparation of motor processes
(Lebar, Bernier, Guillaume, Mouchnino, & Blouin, 2015),
and could be involved in visuoperceptual and visuospatial in-
tegration (Cerami etal.,2015). The current result that alpha
power originating in multiple brain regions can predict task
demands supports the above findings that the activity of the
parietal–temporal–occipital network might functionally sup-
port the internal processes in performing a visual search task.
In this network, we note the involvement of the bilateral me-
dial temporal gyrus (MTG). The MTG has been recognized as
part of the DMN (Burkhouse etal.,2017), which is a distrib-
uted network of brain regions that is more active during rest
than when performing a task (Raichle,2015). Its deactivation
has been correlated with improved attentional performance
(Whitfield-Gabrieli & Ford,2012), and the DMN also plays a
key role in generating daydreaming thoughts and mind-wander-
ing (Kucyi & Davis,2014). Interestingly, the current evidence
indicates the MTG could also be critically activated when per-
forming tasks instead of mind-wandering. This observation is
consistent with the more recent view that the DMN is actively
involved in task-related mental activity as well as detailed rep-
resentations of task relevant processes (Sormaz etal.,2018).
For example, according to Turnbull et al. (2019), enhanced
DMN-occipital connectivity might be involved in maintaining
task-related details over time when the task required lower de-
mands of visual processing. In line with this idea, the DMN has
been found to be more decoupled from primary visual areas
for participants who mind-wandered more often in a reading
task (Zhang, Savill, Margulies, Smallwood, & Jefferies,2019).
These examples also highlight the difficulty of detecting and
classifying mind-wandering: Many neural structures are in-
volved both in on-task and off-task states.
We also found that the left precuneus (IC2) and the right
medial frontal gyrus (IC4) are likely generators of alpha os-
cillations that were predictive in both the self-report and the
task demands classifiers. This finding is consistent with re-
search that shows that the precuneus is involved in sensory
processing (Clancy, Ding, Bernat, Schmidt, & Li, 2017),
conscious visual perception (Bisenius, Trapp, Neumann,
& Schroeter, 2015) and directing selective attention to
goal-related stimulus (Dosenbach etal.,2007). Interestingly,
mind-wandering has been proposed to be a sensory
decoupling process (Schooler etal.,2011), in which corti-
cal responses during sensory processing are reduced (Kam
etal.,2011). In our research, the inhibited sensory processing
was indicated by an enhanced alpha power in the precuneus
(IC2). As shown in Figure9, the effect was apparent through
our experimental manipulation, in which the presented stim-
uli were kept consistent, and only the required mental ef-
fort could differ between the counting and the non-counting
tasks. In other words, reduced sensory processing could not
be attributed to the external stimulus characteristics but only
to an internal process.
The right medial frontal gyrus has been reported to be in-
volved in inhibitory control (O'Brien etal.,2013). Previous
work showed that deactivation of the medial frontal gyrus was
associated with improved performance on a spatial working
memory task (Liu etal.,2018). We found that alpha power in
IC4 was higher in a low demand compared to a high-demand
condition, while not differentiating between the two self-re-
ported mental states (mind-wandering vs. on-task; Figure9).
This result seemed to match the behavior, in which we found
a significant difference between task performance under dif-
ferent levels of demand, while no behavioral difference was
found between the two self-reported mental states.
To sum up, we found that the activity of a parietal–tem-
poral–occipital network might be a neural marker of handling
increasing task demands. We also found that the left precu-
neus and the right medial frontal gyrus might help to clar-
ify the correlation between mind-wandering and performing
low-demanding tasks.
4.3
|
Limitations and future work
Although we found that participants reported a general atten-
tional decline in the second half of the experiment and the
low-demanding task as we expected, the average rating scores
of attentional states were relatively high. The mean mind-
wandering scores for both the second half (low vigilance) and
the low task demands was nearly zero, and rarely negative,
which would have indicated mind-wandering (Figure4). This
indicated that participants may have been influenced by the
phrasing of the question “To what degree were you focusing
on the task just now?”—nudging them to report in the “fo-
cused” direction when rating on the scale. A similar effect
was reported before, as asking for “mind-wandering” rather
than for “focused” would increase participant's reports of
mind-wandering (Weinstein, De Lima, & van der Zee,2018).
Although we also presented “mind-wandering” in the rating
scale as the label for the negative values, we cannot rule out
the possibility that the reports were framed by the probe.
Individual participants varied strongly in their attentional
fluctuations. Some participants kept a relatively strong task
focus during the experiment, which was indicated by a higher

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JIN et al.
rating score, while some others had attentional drift during
the experiment, so that their reports included an equal amount
of negative and positive values. Since one of the current ma-
chine-learning goals was to test the classifiers in the left-out
participant, not only those data with varied mental fluctu-
ation were included, but also those with a relatively stable
attentional state (e.g., always focused), so that the predictiv-
ity of the classifier was tested on a quite heterogeneous data
sample. This might have limited our prediction performance.
It might be interesting to explore in the future if keeping the
data sample relatively homogenous, for example, training and
testing the classifiers among the data of the participants who
reported large attentional fluctuations, could improve the pre-
diction accuracy in general.
The third limitation is that the current analysis was per-
formed on a relatively narrow time window around the stimu-
lus. It is likely that the mental state changes (e.g., on-task shifts
to mind-wandering) happen later after the stimulus onset, not
necessarily time-locked to visual stimuli. In the current study,
to rule out the possibility that the classifiers used motor-re-
lated potentials or artifacts to classify, we decided to use a
short time window. It would be interesting for future research
to expand the time window used for classification and thereby
to explore this more dynamic view of mind-wandering.
One special note here is that the objective of our current
research was not to obtain maximal accuracy in predicting
mind-wandering (or any of the other experimental manipula-
tions, e.g., vigilance, task demands). The goal of the current
research is to compare three classifier's performance in predict-
ing mind-wandering, to examine whether vigilance decrement/
low task demands could predict mind-wandering, instead of
achieving the optimal classification performance. As long as the
classifiers performed above-chance level during the LOPOCV
and 10-fold CV, we regarded the classifiers as valid. Meanwhile,
when predicting mind-wandering, the vigilance/task demands
classifier performed worse than the self-report classifier, lead-
ing us to reject our hypothesis that “vigilance decrement/ low
task demands == mind-wandering.” Nevertheless, this does
not mean that better performance of classifiers is not possible
with more complex classifiers, other features or larger datasets.
For example, Zanesco, Denkova, Witkin, and Jha (2019) used
Markov chain modeling to reveal the hidden states underlying
time series of probe ratings (mind-wandering rating scale sim-
ilar to the current study). It would be interesting to explore if
such models can be combined with EEG to reveal the temporal
dynamics of the changing mental states.
Future work could also consider using other dimensions
of thoughts such as the levels of detail, the modality or the
emotional valence of the thoughts to classify the thought con-
tent in relating it to brain activity (Ho etal.,2019). For exam-
ple, the connectivity pattern within the DMN has been found
to discriminate between levels of detail of ongoing thought
(Sormaz et al., 2018) and the medial orbitofrontal cortex
(mOFC) has been shown to code for the affective tone of the
thought (Tusche, Smallwood, Bernhardt, & Singer, 2014).
Exploring the neural correlates of these features could further
help to understand the generation and prevalence of different
kinds of mind-wandering.
5
|
CONCLUSION
Our study has shown that mind-wandering is a different men-
tal phenomenon from both low vigilance and being in a low-
demand task situation, because each of these processes had
unique neural correlates. Mind-wandering is associated with
activity arising from the left STG, possibly reflecting the
thought generating process. Low vigilance is characterized
by alpha oscillations arising from the brainstem. Low task
demands are associated with activity in a parietal–temporal–
occipital network. Mind-wandering and performing a low-
demanding task both activate the left precuneus and the right
medial frontal gyrus, possibly reflecting a process of sensory
decoupling. More generally, this study demonstrates how
machine-learning classifiers can help to unveil similarities
and differences between mind-wandering and other mental
processes by comparing their characteristic neural markers.
CONFLICT OF INTEREST
There was no conflict of interest with respect to the publica-
tion or authorship of this article.
DATA ACCESSIBILITY
The data that support the findings of this study are available
from the corresponding author upon reasonable request.
PEER REVIEW
The peer review history for this article is available at https://
publo ns.com/publo n/10.1111/ejn.14863
AUTHOR CONTRIBUTIONS
Christina Y. Jin involved in conceptualization, methodology,
software, formal analysis, investigation, writing—original
draft, and visualization. Jelmer P. Borst and Marieke K. van
Vugt involved in conceptualization, methodology, resources,
writing—review and editing, and supervision.
ORCID
Christina Yi Jin https://orcid.org/0000-0002-1482-4444
Marieke K. van Vugt https://orcid.
org/0000-0003-3200-0059
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SUPPORTING INFORMATION
Additional supporting information may be found online in
the Supporting Information section.
How to cite this article: Jin CY, Borst JP, van Vugt
MK. Distinguishing vigilance decrement and low task
demands from mind-wandering: A machine learning
analysis of EEG. Eur J Neurosci. 2020;52:4147–4164.
https://doi.org/10.1111/ejn.14863