Discussion
The principal aim of this study was to replicate the ego-depletion
effect while controlling for effort engagement in depleting and control
tasks with the use of psychophysiological measurements, such as
electrophysiological changes in mid-frontal theta and cardiac
reactivity. The ego-depletion effect did not escape the replication
crisis (Alós-Ferrer et al., 2019; Hagger et al., 2016; Lurquin et al.,
2016; Vohs et al., 2021; Xu et al., 2014), and testing one more time its
reproducibility in more favorable conditions seems pertinent for the
advancement of the discipline. Consequently, we used a protocol that
already successfully obtained the ego-depletion effect (Mangin et al.,
2021). This protocol included a long and effortful depleting task (i.e.,
a modified computerized incongruent Stroop task), a nonboring control
task (i.e., a documentary viewing task) and an effortful physically
dependent task (i.e., time-to-exhaustion handgrip task at 13% of MVC).
Overall, the participants in each group followed the instructions during
the mental task. On the one hand, the participants who performed the
Stroop task had a percentage of errors less than 12% for the reading
trials and 20% for the ‘naming ink color’ trials throughout the
cognitively demanding task. On the other hand, the participants who
performed the documentary viewing task had a percentage of correct
responses significantly greater than the chance level. In addition, the
participants’ MVC significantly decreased after the time-to-exhaustion
handgrip task compared to beforehand, regardless of the group and the
moment of the session (before or after the mental task). This decrease
in MVC reflects muscular fatigue and suggests that the participants
truly contracted their forearm muscles until exhaustion. This inference
is confirmed by the significant increase in the perception of pain and
effort throughout the time-to-exhaustion handgrip task. It is also
interesting to note that the motivation to perform the
time-to-exhaustion handgrip task did not change regardless of the group
and the moment of the session. Several theoretical models predict a
decrease in motivation to perform an effortful task after the completion
of a first effortful task (Inzlicht et al., 2014; Kurzban et al., 2013).
Our experiment did not support this hypothesis.
The mentally demanding Stroop task, which requires repeated inhibitory
control and cognitive flexibility, led to a faster performance drop in
the subsequent dependent task. Furthermore, the subjective data showed
that the Mental task performed by the experimental group was considered
more difficult than that performed by the control group. In a previous
study (Mangin et al., 2021), the ego-depletion effect was replicated in
a within-subjects design with a large sample size (N = 55). In the
current study, we observed the ego-depletion effect in a
between-subjects design with a smaller sample size (N = 32; 16 in each
condition). This outcome could be an argument to support the
reproducibility of the ego-depletion effect when using the
aforementioned experimental protocol, even with a small number of
participants. In addition, a recent meta-analysis assessing the effect
of mental fatigue on physical performance also indicated that
between-subjects designs lead to larger effect sizes compared to
within-subjects designs (Brown et al., 2020).
The results concerning mid-frontal theta confirmed that the Stroop task
required more mental effort than the documentary viewing task. We
observed higher theta waveband power mainly in the frontal, prefrontal
and central areas, specifically at the AF7, AF3, FC5, FC3, FP2, AF4,
AF8, F8, F6, FC6, C6, C4, C2 and P4 electrodes, during the Stroop task.
These results were in accordance with the results that have been
observed in previous studies as an index of mental effort investment
during other mentally demanding tasks (Fairclough & Ewing, 2017; Puma
et al., 2018).
The results of our source localization also showed that the sources of
cerebral activation originated mainly from the prefrontal, frontal and
central areas and, more precisely, regions close to the anterior
cingulate cortex (ACC), thalamus and posterior cingulate cortex (PCC).
Therefore, these results support one of the hypothesis of the
integrative model of effortful control (André et al., 2019), according
to which an increase in the mid-frontal theta waveband (4–7 Hz)
generated by the ACC, one of the principal nodes of the salience
network, is an indicator of effort engagement in a task requiring
effortful control.
However, contrary to our expectations, but in agreement with the results
observed by Arnau et al (2021), the results of time on task assessment
of task-related theta power showed a decrease in theta power throughout
the Stroop task, and regardless the type of trial (reading vs. naming
ink color), suggesting a progressive disengagement of effort. In
accordance with the EEG results, we also noted a gradual increase in HRV
parameters (SDNN, HF & LF) toward the end of the task for the
participants who performed the Stroop task. This observation suggested
that parasympathetic activity was more dominant toward the end of the
Stroop task, indicating that the participants were habituated to the
process of the task and were performing the task more spontaneously.
Surprisingly, we observed a negative correlation between the increase in
reaction time for the trials that required more inhibitory control
(i.e., the ‘naming ink color’ trials) and the increase in heart rate
variability assessed with SDNN: the higher the increase in RT throughout
the Stroop task, the lower the increase in SDNN (r = -.546; p
< .05). This negative correlation suggests that participants
who showed a higher decline in performance, also showed a lower decrease
in parasympathetic activity. This result does not support the hypothesis
of a progressive disengagement of effort throughout the Stroop task and
is more compatible with the view that in parallel to the habituation to
the Stroop task resulting in an increase in parasympathetic activity,
participants who showed a higher decline in performance need to invest
more effortful control to stay committed to the Stroop task.
The EEG results during the video condition showed a higher theta power
mainly in the occipital areas, more precisely in the POz, O1, Iz, Oz,
PO7 and PO8 electrodes. During this task, the participants were watching
an emotionally neutral movie showing animals in their natural
environment, and at the end of the task, they were asked to answer some
simple questions about the content of the movie. Since this task was
relatively easy and did not require much effort, the occipital areas
were mainly activated to have a good perception of the visual scenes.
Although other brain areas could also be activated to store pertinent
information in long-term memory to answer the questions at the end of
the movie, activation was not particularly observed in our analysis. The
electrophysiological results of the video task mainly established less
engagement of effort in the mid-frontal areas and more engagement in
visual processing areas in the occipital regions. Our main focus was on
the theta waveband as far as it is an index of effort, and we wanted to
ensure that the control task required significantly less effort. On that
account, we can confirm that the control task was less mentally
demanding than the experimental task, as argued in our previous study
(Mangin et al., 2021).
Overall, this study showed that it is crucial to control performance and
effort engagement during depleting and control tasks to verify whether
the depleting task requires more effort than the control task, and
whether the participants stayed engaged throughout the whole depleting
task. In the present study, although we observed a disengagement of
effort during the Stroop task, we still observed the ego depletion
effect.
Limits
As a limitation of this study, we first note the small sample size.
Although, as mentioned earlier, we have successfully replicated the
ego-depletion effect, it is difficult to generalize the results to the
whole population with our limited sample size.
The dissimilarity between the experimental and control tasks can be
mentioned as the second limitation of this study. In the documentary
viewing task, the participants were more passive and confronted with a
large variety of visual stimuli. In contrast, in the depleting Stroop
task, they were more active (i.e., naming and reading aloud) and
confronted with the repetition of a small number of different visual
stimuli. However, it was shown in our previous study (Mangin et al.,
2021) that the video task was an effective control task because it was
not boring but was less effortful than the Stroop task. For instance, it
has been shown that the congruent version of the Stroop task is highly
boring and, in this way, taxing the self-control resources of the
participants to continue the task. Finding a control task as similar as
possible to the depleting task but requiring little effort and inducing
little boredom is not easy.
According to the TOT results observed in the depleting task in the
present study, we can view the disengagement of effort that occurred in
the Stroop task as the third limitation of this study. The integrative
model of effortful control (André et al., 2019) predicts that the higher
and the longer the engagement in effort during the depleting task, the
larger the size of the ego depletion effect. According to this
prediction, if the participants of our experiments had maintained their
effort throughout the depleting task, the ego-depletion effect would
have been larger. Previous studies showed that knowledge of results
(Sanders, 1983) and rewards (Herlambang et al., 2019) allows
participants to maintain effort throughout long effortful task.
Manipulating knowledge of results and/or rewards during the depleting
task could be an interesting way to test this prediction.
Future perspectives
The increase in parasympathetic activity observed during the depleting
task can also be interpreted as a habituation and/or automatization of
the Mental task. For future studies aiming to induce an ego-depletion
effect, it would be crucial to use a depleting task that is as little
automatable as possible to observe a TOT effect throughout the depleting
task. The beneficial effect of practice and automatization can reduce or
nullify the detrimental effect of mental fatigue. During the
computerized Stroop task, certain participants with short reaction times
were able to rest between two trials for a few hundred milliseconds.
Throughout the task, these participants could have learned to benefit
from these microbreaks to invest less effort and save energy. A task
constraining the participants to maintain information in working memory
between two trials, such as a dual 2-back task requiring the encoding of
two characteristics of each stimulus – for instance, its color and
spatial location in a 4x4 matrix – could be a strategy to force
participants to maintain a high level of concentration throughout the
task and then reduce task automatization.
In the present study, we assessed the deployment of mental effort during
a depleting task and a control task through three different indices of
heart rate variability (HRV): one time-domain index (SDNN) and two
frequency-domain indices (LF and HF). As a general rule, HRV indices
decrease when the sympathetic nervous system is activated in response to
stressors, such as performing a task that requires effortful control. In
contrast, HRV indices increase when the parasympathetic nervous system
is activated during resting periods (Appelhans & Luecken, 2006;
Berntson et al., 1997). However, several authors have suggested that the
low frequency component (LF), which highly correlates with SDNN and HF,
is largely determined by the central autonomic outflow and, more
particularly, the parasympathetic nervous system (Cooley et al., 1998;
Reyes del Paso et al., 2013). Consequently, heart rate variability
indices would be indirect, inversely related indices of sympathetic
activity. More recent works have shown that another cardiac reactivity
index would be a more direct index of sympathetic activation and
therefore of mental effort engagement (Drost et al., 2022; Mallat et
al., 2020): the pre-ejection period (PEP). This index corresponds to the
time interval between the beginning of the depolarization of the
ventricles (Q point on the ECG) and the ejection of blood into the aorta
(B point on the impedance-cardiogram) (Brenner & Beauchaine, 2011). The
shorter that the PEP is, the higher that the effort engaged in the
Mental task is. Because Goedhart et al (2008) demonstrated that LF and
HF did not show the expected negative correlation with PEP, it seems
more appropriate to use PEP in future studies aiming to assess mental
effort during the depleting, control and/or dependent task of the
sequential task protocol.
Another point that can be addressed in future studies is the assessment
of the event-related potential (ERP) components in addition to theta
power density, such as the N2 component during a depleting task
eliciting a stimulus-response conflict, which has been associated with
dorsal ACC-related control processes (Cavanagh & Frank, 2014). From
that perspective, variations in the amplitude of the N2 component could
reflect variations in effortful control throughout the task.
In other respects, the integrative model of effortful control (André et
al., 2019) assumes that the connectivity of the large-scale neuronal
networks involved in effortful control, such as the salience network and
the central executive network, can be weakened in long, depleting tasks.
Therefore, in future studies, other brain imaging methods, such as
resting-state functional magnetic resonance imaging (r-fMRI), can be
used to assess the connectivity of brain networks involved in the
ego-depletion effect. Several studies have used activation fMRI (e.g.,
Friese et al., 2013; Gergelyfi et al., 2021) to examine the pattern of
activation in different brain regions during the dependent task of a
sequential task protocol. However, to our knowledge, only one study has
already used the sequential task protocol with the aim of examining the
influence of mental fatigue on brain connectivity at rest (Esposito et
al., 2014). This study reported a decrease in connectivity in the left
and right frontoparietal executive attentional networks (bilateral
middle frontal gyrus and right angular gyrus). These results must be
confirmed by other studies varying the depleting and dependent tasks.
Finally, it could also be interesting to conduct studies with
noninvasive brain stimulation methods, such as transcranial direct
current stimulation (TDCs), that can assess the question of effort
capacity amelioration by stimulating the ACC region of the brain. We can
question whether, in the case of boosting the salience network capacity
to generate the mid-frontal theta or the effort signal, it is possible
to reduce the ego-depletion effect and mental fatigue. To this extent,
since humans are frequently engaged in depleting Mental tasks, it would
be interesting to find different countermeasures to reduce the cognitive
fatigue caused by effortful tasks and then improve individuals’
performance in their daily lives.