Introduction
Every day, we engage in cognitive and physical activities that challenge
us to move beyond our habits and require greater effort, such as
learning a new language, solving a complex mathematical equation,
running a marathon, manipulating information in working memory, and
managing multiple priorities. Such effortful tasks require higher-order
executive functions, such as self-control, inhibitory control of our
thoughts, emotions, impulses, and behaviors, to reach desired goals
(Baumeister et al., 2007). When self-control is weakened by a first
effortful task, a drop in a subsequent effortful task is generally
observed. This phenomenon is known as the ”ego-depletion effect”
(Baumeister et al., 1998).
However, over the past decades, the existence of the ego-depletion
effect has been challenged by meta-analytic studies and faced a
replication crisis (Carter et al., 2015; Dang, 2018). For example, a
worldwide replication study from 12 different laboratories (N = 1,775)
showed a small effect size (d = 0.16) for the ego-depletion phenomenon
(Dang et al., 2021). Another recent multilaboratory project (k = 36; N =
3,531) reported no evidence for the ego-depletion effect (d = 0.06)
(Vohs et al., 2021). The ego depletion effect was first evidenced in
social psychology with the sequential task protocol (Hagger et al.,
2010). Then, sport sciences used the same protocol to examine the
effects of mental fatigue induced by an effortful task on a subsequent
physical task (Brown et al., 2020; Giboin & Wolff, 2019; Hunte et al.,
2021; Van Cutsem et al., 2017).
Three theoretical approaches mainly explained the subsequent task
performance decrease observed in the sequential task protocol. Based on
the strength model of self-control, the first approach assumes that the
ego-depletion effect occurs as the result of resource depletion, such as
brain glucose (Baumeister et al., 2007). According to the second model,
the ego-depletion effect is caused by the switching of motivation and/or
attention toward a more pleasant task (Inzlicht et al., 2014). The third
approach refers to the cost-benefit model and suggests that, when the
costs to achieve the task goal are higher than the benefits associated
with the achievement of the task goal, participants decrease their
engagement in effortful control or drop out (Kurzban et al., 2013;
Shenhav et al., 2017). In this regard, mental or cognitive fatigue can
be viewed as a cost (Boksem & Tops, 2008), which has also been
considered the cause of the ego-depletion effect experienced in
prolonged cognitive activities (Muraven & Baumeister, 2000).
In a more recent model, the main cause of the decrease in the functional
capacity to exert effortful control observed after a long and effortful
task is explained by a weakening of the connectivity within and between
large-scale neuronal networks underpinning effortful control, such as
the salience network and the executive control network, and an
accumulation of metabolites in brain regions involved in effortful
control, such as the anterior cingulate cortex (André et al., 2019).
Three possible metabolites have been proposed to be byproducts of
neuronal activity that could decrease the capacity to exert effortful
control: (1) the adenosine that operates in a negative feedback loop on
neuronal activity and then decreases it (André et al., 2019; Cunha,
2001; Martin et al., 2018; Smith et al., 2019) ; (2) the glutamate, the
neurotransmitter released by pyramidal neurons in cortical columns
involved in cognitive control (Wiehler et al., 2022); (3) the amyloid-β
(Aβ) peptides continuously secreted into the interstitial fluid by
active neurons (Holroyd & Umemoto, 2016).
The integrative model of effortful control reconciles the three models
cited in a previous paragraph: (1) in accordance with
the strength model of
self-control (Baumeister et al., 2007), it predicts that exerting
effortful control leads to a progressive weakening of the capacity to
exert effortful control; (2) in accordance with the process model of
self-control depletion (Inzlicht et al., 2014), it conceives that ego
depletion and mental fatigue can be accompanied by a decrease in
motivation to exert effortful control; (3) in accordance with the
cost-benefit models of effort-based decision-making (e.g., Shenhav et
al., 2017), it views effortful control as costly and mental fatigue as
an intrinsic cost generated by the sustained deployment of mental
effort.
However, the integrative model of effortful control differs from these
three models in different ways: (1) contrary to the strength model of
self-control, it assumes that the duration of the effortful task and the
executive control required to perform this task are two critical
parameters to observe ego depletion (i.e., only long and demanding tasks
induce ego depletion and mental fatigue); (2) contrary to the process
model of self-control depletion, it assumes that the shift in motivation
to exert effortful control is not the cause but a possible consequence
of ego depletion and mental fatigue; (3) contrary to the cost-benefit
models, it assumes that exerting effortful control is not intrinsically
aversive.
According to the integrative model of effortful control, we assume that
the replication studies aiming to examine the ego depletion effect did
not use adequate protocols and that the ego-depletion effect can be
replicated by considering some methodological precautions. First,
according to the previous literature (Mangin et al., 2021), the
depleting task must be sufficiently effortful to observe the
ego-depletion effect. In general, tasks that involve core executive
functions, including cognitive flexibility, updating of working memory,
and inhibitory control, are good candidates for generating effortful
control costs (André et al., 2019; Hofmann et al., 2012) and inducing
the ego-depletion effect (Dang et al., 2021). For instance, while the
choice of the letter-crossing task as a depleting task was not
successful (Etherton et al., 2018; Wimmer et al., 2019; Xu et al.,
2014), a modified Stroop task tapping two executive functions, cognitive
flexibility and inhibitory control, was effective at inducing the ego
depletion effect (Dang, 2018; Hagger et al., 2010; Mangin et al., 2021,
2022).
Second, the duration of the depleting task also plays an important role
in the occurrence of the ego-depletion effect. In most of the previous
ego-depletion studies, the duration of the depleting tasks was short: an
average of 6.27 min (SD = 3.22 min) (Hagger et al., 2010). However,
healthy subjects must exert their self-control long enough to be
depleted (Blain et al., 2016). Recently, Boat et al (2020) showed that
the longer the duration of the depleting task (4, 8, and 16 min), the
greater the effects of deterioration on a subsequent physical task. In
the same way, sport science studies using the sequential task protocol
have observed that the performance of a physical task is generally
degraded after a long (> 30 min) and continuous effortful
cognitive activity (Van Cutsem et al., 2017). In this regard, assessing
the engagement of the participants in the depleting task as a function
of time on task can be helpful to examine if the task is sufficiently
depleting.
Third, a recent study showed that the choice of the control task is also
an essential factor in replicating the ego-depletion effect (Mangin et
al., 2021). This study emphasized that the use of a boring control task,
such as a congruent Stroop task, can prevent the observation of the
ego-depletion effect. Performing a boring task seems as costly as a task
involving executive functions and requires a certain amount of effortful
control to be completed.
According to the three aforementioned points, an adequate protocol to
replicate the ego-depletion effect would be a sequential task protocol
that uses a 30-min effortful modified Stroop task tapping inhibitory
control and cognitive flexibility as the depleting task, a
time-to-exhaustion handgrip task as the dependent task, and a not boring
30-min documentary video-watching task requiring little effort as the
control task.
The integrative model of effortful control also assumes that an increase
in mid-frontal theta wavebands (4–7 Hz), a control signal generated by
the salience network, more specifically the anterior cingulate cortex
(ACC), one of its central nodes, is an indicator of effort engagement
and compensatory effort. André et al. (2019) also claimed that the
decrease in the capacity to exert effortful control induced by
ego-depletion and mental fatigue can be observed in changes in the
density of mid-frontal theta waves and performance drop throughout the
depleting task. In this respect, two patterns of results induced by
ego-depletion and mental fatigue can be expected during a long effortful
task: (1) a decrease of performance associated with a disengagement of
effortful control; (2) a stability of performance associated with an
increase of effortful control (i.e., compensatory effort). These changes
in theta activity throughout the depleting task should be observed with
electroencephalography (EEG).
In this respect, Umemoto et al (2019) assumed that the ACC is
responsible for regulating control levels by balancing the
reward-related benefits of control in contrast to its effort-related
costs. These authors measured reward valuation and cognitive effort with
two electrophysiological indices of ACC function during a 2-hour time
estimation task (i.e., participants had to estimate 1 second on every
trial), respectively the reward positivity (RewP) and frontal midline
theta. Reward positivity (RewP) is an event-related potential (ERP)
component produced by fast, phasic midbrain dopamine reward prediction
and error signals that are modulated by ACC activity (Holroyd & Coles,
2002), and it is aroused by feedback stimuli with negative versus
positive valence (Miltner et al., 1997). The authors observed the
participants’ performance throughout the time estimation task, giving
them error and reward feedback based on their performance while their
neural activities were assessed with EEG. The results showed a decrease
in the reward positivity (RewP) amplitude with time on task in contrast
to an increase in frontal midline theta power throughout the effortful
task. According to their findings, when a long cognitively-demanding
task is performed, two different phases can be distinguished. During the
first phase of the task, high control levels are associated with strong
reward valuation, which both contribute to significant improvements in
task performance. In the later phase of the task, high control levels
counteract the decrease in reward valuation, thereby helping to maintain
stable task performance.
In addition, other EEG studies have also shown an increase in theta
power during tasks requiring mental effort (e.g., Smit et al., 2005).
Cavanagh and Frank (2014) also argued that the mid-frontal theta signal
reflects diverse cognitive operations, including those involved in
cognitive control. In a later study, they proposed that frontal-midline
theta reflects midcingulate cortex (MCC) activity, which is involved in
cognitive control and anxiety (Cavanagh & Shackman, 2015). An
association of mid-frontal theta activity with different cognitive or
motor sustained attention tasks is assumed to originate from the ACC
(Kao et al., 2013; Onton et al., 2005; Sauseng et al., 2007). Evidence
from a recent systematic review with a meta-analysis of 21 EEG studies
performed on healthy individuals reported that frontal-midline theta is
a robust biomarker of mental fatigue, with significant increases in
theta activity in frontal, central, and posterior areas mainly observed
(Tran et al., 2020). Another literature review also showed an increase
in frontal midline theta wavebands as the demand for the task,
alertness, arousal and mental fatigue increase (Borghini et al., 2014).
We can conclude from the two reviews mentioned above that the increase
in theta during a long effortful task is an index of compensatory effort
to maintain the level of performance instead of mental fatigue.
In addition to mid-frontal theta assessed by EEG, other
psychophysiological indices, such as pupil diameter, contraction of
specific muscles, and sympathetic arousal (e.g., cardiac reactivity),
can also be considered different indices of effort engagement (Shenhav
et al., 2017). Heart rate variability (HRV), or the analysis of
beat-to-beat intervals, is one of the most common and reliable
physiological measurements used to assess mental effort (Aasman et al.,
1987; Mukherjee et al., 2011; Mulder & Mulder, 1981; Veltman &
Gaillard, 1993). The sympathetic and parasympathetic nervous systems
influence HRV, which can vary depending on the subject’s physiological
and psychological state (Ernst, 2017). During the performance of a task
that requires mental effort, the previous literature has mainly reported
a decrease in heart rate variability (Capa et al., 2008; De Rivecourt et
al., 2008; Wang et al., 2005; Weippert et al., 2009).
Accordingly, the principal aim of this study was to replicate the
ego-depletion effect while controlling for the engagement of effort
during the depleting and control task and using a similar sequential
task protocol as in a previous study that successfully replicated this
effect (Mangin et al., 2021). The electrophysiological changes in effort
engagement throughout the depleting task will be assessed through
mid-frontal theta density and cardiac reactivity. We expected higher
mid-frontal theta density during the depleting task than during the
control task. We also predicted a progressive increase in mid-frontal
theta power as a function of time on task (TOT) during the depleting
task. Finally, we assumed that we would observe a decrease in HRV
throughout the depleting task compared to the control task. Along with
these physiological measures, we hypothesized that the participants
might report a higher level of subjective fatigue and a lower motivation
to perform the handgrip task after performing the mentally demanding
task.