Methods
Participants
Thirty-two undergraduate students
in their third year studying sport sciences at the University of
Poitiers participated in this study. The participants were randomly
assigned into two groups: a control group and an experimental group.
Their characteristics can be found in Table 1. All of the participants
were native French speakers and right-handed except one in the
experimental group who was left-handed. All participants had normal to
corrected-to-normal vision with no reports of color blindness. None of
the participants revealed a history of language difficulties, learning
impairments, or severe neurological, metabolic and/or psychiatric
disorders. For the sake of our handgrip endurance-dependent task, we did
not recruit individuals who had been engaged in a muscle reinforcement
activity for more than 6 months or anyone who had injuries or functional
problems with the dominant hand. The experimental procedure was
explained to the participants via a written information sheet. Then,
they signed a consent form to indicate their agreement.
Finally, the participants received
course credit in exchange for their participation. This experiment was
conducted in accordance with the Declaration of Helsinki and with the
approval of the University of Tours-Poitiers ethics committee (serial
number 2019-01-02).
Procedures
This experiment occurred during a
single session, and the participants in both groups underwent the exact
same procedures. The session started with asking the participants to
complete a sociodemographic questionnaire (height, weight and age) and
a hand laterality scale (Oldfield,
1971) to determine their dominant hand. The participants were also
tested for color blindness (Ishihara, 1918) to determine whether they
were able to perform the Stroop task. Then, the participants answered
questions regarding their general state and health. Certain experimental
precautions related to any parameters that could have an effect on
mental strain and fatigue were considered, such as not consuming
alcohol, caffeine, nicotine or psychostimulant substances; avoiding
stressful events and/or intense physical activity several hours before
the session; and having a sufficient amount of sleep the night before
the experiment. Later, the participants underwent a familiarization
period to understand the flow and instructions of the mental and
physical tasks. In the familiarization phase, the participants performed
48 trials of the incongruent Stroop task and tried out the handgrip
device. Afterward, they were equipped with EEG and ECG electrodes. Next,
they underwent the sequential stages of the experimental protocol while
their electrophysiological data were recorded on a continuous basis, as
shown in Figure 1. First, the participants were asked to perform a
time-to-exhaustion handgrip task to determine their physical performance
baseline, while their maximal voluntary contraction (MVC) was assessed
before and after this task. Second, they performed a 30-min mental task
that differed in each of the two groups. The experimental group
performed a 30-min modified incongruent Stroop task tapping two
executive functions: inhibitory control and cognitive flexibility.
The control group watched a
30-minute documentary video. After the completion of the mental task,
the participants repeated the time-to-exhaustion handgrip task and the
MVC measurements before and after this physical task. Every 30 seconds
during each handgrip task, the participants were asked to announce the
amount of effort that they invested in the task, as well as the level of
pain that they experienced in their dominant forearm and hand. Several
other subjective measurements were made throughout the session at four
different times: T1 = Baseline, T2 = Pre-Mental Task, T3 = Post-Mental
Task, T4 = Post-Handgrip Task. We used a computerized visual analog
scale (VAS) with an adapted scale (0% to 100%) to rate the level of
their perceived fatigue (T1 to T4), the motivation to perform the
physical task (T1 and T3), the perceived difficulty of the mental task
(T3) and finally the level of boredom experienced during the mental
tasks (T3). The motivation to perform the dependent task and subjective
mental fatigue were already assessed with a similar VAS (Brown & Bray,
2017), and this type of VAS was validated by Wewers and Lowe (1990).
Finally, a computerized version of the Brief Self-Control Scale (Tangney
et al., 2004) was used to assess the participants’ trait self-control
due to the probable influence of this trait on the ego-depletion effect.
Previously, it has been argued that individuals with a high self-control
trait tend to demonstrate a lower ego-depletion effect than individuals
with a low self-control trait (DeWall et al., 2007).
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Materials
Time-to-exhaustion task
To measure the isometric handgrip force of the individuals’ dominant
hand, we used a dynamometer (TSD121C, BIOPAC). AcqKnowledge software,
version 4.2 (BIOPAC Systems Inc., Goleta, CA, USA), as well as an
MP160WSW data acquisition unit, was used to record the force signal.
Data were recorded online at a sampling rate of 2000 Hz and later stored
and analyzed offline. The participants were asked to sit on a fixed
chair with an arm support allowing for the imposition of an angle of
~90 degrees with their elbow and forearm, as shown in
Figure 2. The participants were asked to remain in this anatomically
neutral position throughout the handgrip task.
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Insert Figure 2 about here
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The participants started the physical task by performing an MVC to
measure their true maximal force. For this measurement, they had to
squeeze the dynamometer as strongly as possible for a duration of 3
seconds. This process was repeated until the time that the participants
could not score higher in contraction peak force compared to their
previous performances. Between each MVC measurement, there was a 30-s
rest. The highest MVC measured before the first time-to-exhaustion task
was used throughout the session as the reference for force feedback to
calibrate the target zone for the time-to-exhaustion handgrip task (see
the gauge on Figure 2). The target zone area was defined as a green arc
representing 12% to 14% of the MVC. The participants were asked to
maintain the contraction of their forearm in this area until exhaustion.
Time to exhaustion was evaluated offline and considered the duration of
the isometric contraction from the onset of the force signal to the
exhaustion time (considered as staying out of the zone for more than 2
s). To assess the extent of muscle
fatigue and to ensure that the participants performed the handgrip task
as much as they could, we compared the difference between the MVC peak
force measured before and after the time-to-exhaustion task. During the
time-to-exhaustion task, every 30 s, we asked the participants to rate
their perception of effort, which is referred to as the effort intensity
necessary to squeeze the dynamometer while breathing and remaining in
the green zone (Mangin et al., 2021) on the CR100 scale (Borg &
Kaijser, 2006), as well as their perception of muscle pain, defined as
the perceived pain intensity in their forearm muscles during the
endurance task performance (Mangin et al., 2021) on the Cook scale
(O’Connor & Cook, 2001). The ”individual isotime” method (Nicolo et
al., 2019) was used to analyze the participants’ perceptions of muscle
pain and effort. This method considers the shortest performance record
of the participant on the handgrip task as his or her 100% individual
isotime. Subsequently, the corresponding isotime points to 0%, 33%,
and 66% of the individuals were calculated for their shortest
performance. According to the calculated isotime points, their level of
muscle pain and effort was assessed as a function of time on task.
Mental tasks
We used a computerized version of a modified Stroop task on a computer
equipped with an S-R response box and E-prime software, version 2.0
(Psychological Software Tools, Pittsburgh, PA, USA). The participants
sat in front of a screen and responded orally to the visual stimuli that
appeared at the middle of the screen while their voices were recorded
via two microphones, one to record the oral response of the participant
(headset) and the other to measure his or her response time (fixed
microphone). The participants underwent 888 trials of 2 seconds each.
Every single trial started with a fixation point in the form of a cross
that lasted 400 ms. The fixation point could be enclosed in a circle or
a square for a duration of 50 ms, and then the cross remained on the
screen alone. Immediately thereafter, a color name (green, red, yellow
or blue) written in another ink color (e.g., “yellow” written in red)
was displayed on the screen. The participants had to read the word if
they saw a square (reading trials) or name the color of the ink if they
saw a circle (naming ink color trials). The color word lasted on the
screen until the participant’s response. If the participants did not
answer (omission) or had a reaction time longer than 1250 ms, the color
word lasted 1250 ms and was followed by a fixation cross lasting 300 ms.
When the participant answered before 1250 ms had elapsed, the fixation
cross remained in the middle of the screen for the same amount of time
(1250 ms + 300 ms) to have a duration of 2 s for each trial. The
presentation order of the two categories of trials was completely
random. The block of trials included 50% trials in which the
participants had to read the color word and 50% of trials in which they
had to name the font color for the sake of increasing the task
difficulty by engaging cognitive flexibility and inhibitory control
(Mangin et al., 2021) and limiting the learning effect (Dulaney &
Rogers, 1994). Concerning the modified Stroop task, we were interested
in analyzing performance as a function of time on task. Consequently,
data were divided into 4 consecutive periods of 222 trials lasting 7
minutes and 30 seconds. For each time period, we calculated different
performance parameters, such as the mean reaction time for correct
responses (RT) and the error rates. For the video task, we chose a
documentary movie named Earth by Fothergill and Linfield (2009).
Later, the participants were asked to complete a multiple-choice
questionnaire related to the content of the documentary to verify
whether they were actively watching the movie or not. They did not know
in advance that they will have to complete this questionnaire.
EEG Acquisition and Pre-Processing
Pipeline
The EEG data were collected from a 64-channel Biosemi EEG headset with
the electrode distribution based on the international 10-20 system. For
the sake of better eye movement artifact detection, the participants’
ocular activities were recorded via three electrooculogram (EOG)
electrodes (two on the outer canthus of each eye and one on the
infraorbital region of the right eye). The EEG and EOG signals were
continuously recorded online throughout the whole experiment using the
ActiView Biosemi version 6.05 acquisition system at a frequency of 2000
Hz referenced online to the average of the right and left mastoids. The
MATLAB R2020b programming platform (MathWorks Inc., Natick, MA, USA),
together with the open source EEGLAB 2021.0 toolbox (Delorme & Makeig,
2004), was used for offline data analysis. Data preprocessing steps and
artifact rejection methods were adopted from a very recent reproducible
workflow by Pernet and colleagues (2021). First, data were down sampled
to 250 Hz, and a basic low-pass FIR filter to the higher edge of 40 Hz
was applied to avoid 50-Hz line noise. Then, the clean_rawdata plugin
in EEGLAB (version 2.2) was used to high-pass filter the data at 0.5 Hz
(transition band [0.25 0.75]), as well as remove the bad channels
(any channel with at least a 5-s flat line and/or with less than 0.8
robust estimate correlation to the other channels were considered bad
channels). Next, the data were rereferenced to the average of the
existing channels. Afterward, the data were decomposed into independent
components using the ICA algorithm (runica algorithm with rank reduction
based on the number of channels = -1, considering the average
reference). An automatic algorithm was applied to label the components
using ICLabel (Pion-Tonachini et al., 2019), and components labeled as
muscle activities and eye movements with greater than 80% probability
were omitted from the data. Then, the residual artifacts were removed
using the artifact subspace reconstruction (ASR) algorithm parametrized
to a 20-burst detection criteria threshold (Chang et al., 2018, 2020).
Finally, we inspected the data visually and rejected any nonbrain
components or artifactual portions of the data that were not detected
hitherto by the automatic algorithm. No more than half of the components
in each data point were rejected (the maximum number of component
rejections was 32 of 64). A power spectral analysis was applied to
observe the differences in the power of theta wavebands (4-7 Hz) during
the Stroop task performance as opposed to the video task. The
“spectopo” function in EEGLAB was used to perform the power spectral
analysis and to compute each component power spectrum using the fast
Fourier transform (FFT) with the following parameters: a window size of
1 second with a 50% overlap. We were also interested in the effect of
time on task during the Stroop task on the theta waveband power;
therefore, we also studied the data by dividing the 30 minutes
corresponding to the Stroop task into 4 consecutive task periods of 7.5
minutes.
Task-related theta
activity
According to the methodology used by Arnau et al. (2021), we conducted
an analysis of stimulus-locked theta power during the Stroop task.
Throughout the Stroop task, when the color word was displayed on the
screen, a marker was sent to ActiView Biosemi with the help of the
Eprime program indicating that the ongoing trial is either a ‘naming ink
color’ trial or a ‘reading’ trial. Later, during the EEG offline
analysis, the data were analyzed in the following time window: from 100
ms before the stimulus onset to 2000 ms after this onset. After removing
the baseline recorded during the time window from 100 ms before the
stimulus to the onset of the stimulus, we separated the 444 trials
belonging to the ‘naming ink color’ condition from the 444 trials
belonging to the ‘reading’ condition for further analyses. Then, the
power spectral analysis was applied to observe the changes in theta wave
band by comparing these two types of trial (reading vs. naming ink
color) as the function of TOT.
Source localization
technique
The DIPFIT plugin (Oostenveld & Oostendorp, 2002) in the EEGLAB 2021.0
toolbox (Delorme & Makeig, 2004) was used to calculate an equivalent
current dipole model for each independent component through a four-shell
spherical head model. A bilaterally symmetric dual dipole model was
fitted for the components with bilaterally distributed scalp maps. Only
components located inside the model of brain volume, for which their
best-fitting single or dual equivalent dipole showed less than 15%
residual variance from the spherical forward-model scalp projection,
were contemplated for further analysis.
ECG
Heart rate variability (HRV) was continuously recorded using the
Electrocardiograph (BIOPAC Systems Inc., Goleta, USA) and Acqknowledge
4.2 software (BIOPAC Systems Inc., Goleta, CA, USA) at a frequency of
2000 Hz by placing three EL503 electrodes on the participant’s thorax as
recommended by the American Heart Association (Kligfield et al., 2007).
The Kubios HRV Premium software, version 3.5.0 (Tarvainen et al., 2014),
was used to analyze the data. After applying the medium automatic
artifact correction algorithm available in Kubios, the residual
artifacts were rejected via manual inspection. One subject in the
control group was excluded from further analysis due to the low quality
of his or her ECG data. We studied the changes in HRV on the temporal
basis of four equal time windows of 7 minutes and 30 seconds. We
analyzed the data by examining the time and frequency domain parameters
of HRV. For the time domain analysis, we chose the SDNN (standard
deviation of NN intervals), and for the frequency domain analysis, the
LF power (log power of the low-frequency band from 0.04 to 0.15 Hz), as
well as HF (log power of the high-frequency band from 0.15 to 0.4 Hz),
was assessed (Shaffer & Ginsberg, 2017). The SDNN demonstrates the
components that are responsible for the variability in the recording
period (Malik, 1996). High frequency can be considered the cardiac
parasympathetic tone index (Reyes del Paso et al., 2013) since it
reflects vagal tone (Laborde et al., 2017; Malik, 1996). Low frequencies
are assumed to be markers of cardiac outflow, which is under the
influence of both the sympathetic and parasympathetic autonomic nervous
systems (Laborde et al., 2017; Malik, 1996).
Statistical analysis
The statistical analyses were performed with Jamovi software, version
2.2.5, and Jasp software, version 0.16.3.0. The statistical analysis of
EEG data was performed in EEGLAB using EEGLAB Study parametric
statistics. We set the alpha level for statistical significance to α =
.05. When the results were significant and marginal, the effect sizes
were calculated: Cohen’s d for the t test (only Student’s t test was
used in this study), rank biserial correlation for its equivalent
nonparametric Mann-Whitney U test and partial eta square
(ηp2) for analysis of variance
(ANOVA). When testing an effect involving a repeated-measures factor
with more than two levels (e.g., time on task), we applied a
Greenhouse-Geisser correction to consider any violation of the
sphericity assumption. For EEG data, the FDR multiple comparison
correction available in EEGLAB statistics was considered.