# Cardiovascular responses to mental fatigue in a sequential task paradigm

Ugo Place, Pierre-Vincent Paubel, Rémi L Capa

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Ugo Place, Pierre-Vincent Paubel, Rémi L Capa. Cardiovascular responses to mental fatigue in a sequential task paradigm. International Journal of Psychophysiology, 2025, 215, pp.113210. 10.1016/j.ijpsycho.2025.113210. hal-05283326

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# Cardiovascular responses to mental fatigue in a sequential task paradigm

Ugo Place<sup>a,*</sup>, Pierre-Vincent Paubel<sup>b</sup>, Rémi L. Capa<sup>a,c,**</sup>

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

### Keywords:

Mental fatigue

Effort intensity

Cardiovascular reactivity

Pre-ejection period

Working memory

## ABSTRACT

This study investigated mental fatigue within a sequential task paradigm using cardiovascular measures and predictions derived from Motivational Intensity Theory. Forty-two undergraduate students were assigned to either a fatigue group (n = 21) or a control group (n = 21). The fatigue group completed a difficult task, while the control group watched a documentary, before both groups performed the same difficult subsequent task. Subjective fatigue, performance, and cardiovascular responses were assessed. Participants in the fatigue group reported significantly higher levels of subjective fatigue and lower effort intensity during the subsequent task compared to the control group, as evidenced by reduced PEP and HR reactivity. Despite these differences, performance on the subsequent task did not differ between groups. These findings suggest that a prior difficult task can increase the perceived difficulty of subsequent tasks. When the subsequent task is also difficult, the effort required may be viewed as no longer worthwhile, leading to reduced effort investment. Cardiovascular measures and theory-driven approaches, such as Motivational Intensity Theory, provide valuable insights for understanding mental fatigue and should be employed rather than relying exclusively on performance-based data.

## 1. Introduction

In modern societies, the pursuit and achievement of personal goals often involve completing a variety of daily tasks that require different levels of effort. When these tasks follow one another with little time for recovery, they can lead to mental fatigue, which is characterized by feelings of exhaustion and a reduced capacity to perform (Boksem and Tops, 2008; Wright, 2014). In laboratory settings, mental fatigue is typically induced using either time-on-task protocols or the sequential task paradigm. In the latter, mental fatigue is assessed by comparing the performance of participants who have previously engaged in a difficult task with those who have not. Numerous studies have reported performance declines exclusively in participants who initially invested a substantial amount of effort (Baumeister et al., 1998; Schmeichel, 2007; Luethi et al., 2016). In social psychology, these effects are often interpreted through the lens of “ego depletion,” which is a temporary decrease in self-control. In other fields, they are considered signs of mental fatigue. However, the underlying mechanisms—specifically, why and how they occur—remain poorly understood. The present study aims to address this gap by investigating the effects of mental fatigue on

effort intensity within the sequential task paradigm. This question is particularly relevant given ongoing concerns about replication challenges (Hagger et al., 2016; Vohs et al., 2021).

Effort is commonly defined as the degree to which individuals invest their resources to perform goal-directed behavior (Wright, 2014; Gendolla et al., 2012). It is widely considered a multidimensional construct, influenced by factors such as task difficulty, individual abilities, motivational states, and situational context (Shenhav et al., 2017; Wolpe et al., 2024). Many theories have been proposed to explain why effort deployment leads to mental fatigue. To clarify this complexity, the present study focuses on two main frameworks: early resource-based models and more recent cost-benefit models.

Early resource-based models propose that initial effort deployment depletes a limited, global resource. As this resource is consumed, it results in a state of exhaustion, leading to a decline in performance (Baumeister and Vohs, 2016). Although this idea is intuitive and widely cited, it has been criticized for lacking a clear theoretical foundation, particularly because the nature of the “resource” remains unspecified (Inzlicht et al., 2014). More recent work has refined this view by suggesting that intense or prolonged effort deployment may trigger the

<footnote><sup>a</sup> University of Toulouse Jean Jaures, CERPPS Laboratory, Toulouse, France</footnote>

<footnote><sup>b</sup> University of Toulouse Jean Jaures, CLLE CNRS, Toulouse, France</footnote>

<footnote><sup>c</sup> INU Champollion, France</footnote>

<footnote>* Correspondence to: U. Place, University of Toulouse Jean Jaurès, CERPPS Laboratory, Maison de la recherche, 5 allées Antonio Machado, 31058 Toulouse, France</footnote>

<footnote>** Correspondence to: R.L. Capa, University of Toulouse Jean Jaurès, CERPPS Laboratory, INU Champollion, Place de Verdun, 81012 Albi, cedex 9, France.
E-mail addresses: ugo/place@univ-tlse2.fr (U. Place), remi.capapa@univ-jfc.fr (R.L. Capa).</footnote>
buildup of harmful metabolic byproducts (e.g., adenosine), which impair the brain's ability to maintain effortful control (see André et al., 2019). Rather than conceptualizing effort as consuming a limited fuel that is depleted through use, these newer models emphasize neurobiological changes that alter neural functioning and influence effort perception and decision-making.

Cost-benefit models suggest that people naturally choose options that offer the most significant benefits while avoiding those associated with excessive costs. When tasks feel externally imposed or offer little reward, especially if they require substantial effort, people often lose interest and disengage (Boksem and Tops, 2008; Hockey, 2011; Inzlicht and Schmeichel, 2012; Kurzban et al., 2013). Mental fatigue plays a key role in this process by increasing the perceived cost of effort and reducing motivation. As a result, attention may shift toward more rewarding activities, like daydreaming (Kurzban et al., 2013; Ainslie, 2013). However, introducing a reward or avoidance cues can counteract these effects, highlighting the central role of internal cost-benefit evaluations in regulating mental fatigue (Hopstaken et al., 2015; Tang et al., 2024). While cost-benefit models provide a sound theoretical framework, they often lack clear methods for examining how effort is regulated over time, especially in sequential task paradigms commonly used to study mental fatigue (Baumeister and Vohs, 2016). Building on this observation, the present study employs a theoretical approach that enables a more precise investigation of cost-benefit modulation under mental fatigue. Specifically, we draw on Motivational Intensity Theory (MIT) to examine how prior effort deployment influences individuals' willingness to invest effort in a subsequent task.

According to MIT (Brehm and Self, 1989; Wright, 1996), when task difficulty is known in advance, people adjust their effort based on how hard they perceive the task to be. Thus, the more difficult the task is perceived, the more effort is invested, leading to a proportional relationship between perceived difficulty and effort. However, a key feature of the theory is that effort is not mobilized monotonically. If success appears impossible or not worth the required effort, individuals tend to disengage, resulting in a drop in effort. Put differently, effort deployment increases with perceived task difficulty, but only up to the point where success is viewed as both possible and worthwhile, as individuals tend to conserve their energy and invest effort only when necessary and justified. Numerous studies have directly examined the interaction between perceived difficulty and the potential motivation (success importance) under conditions where task difficulty is clearly defined (see Richter et al. (2016) for a comprehensive review). This theoretical framework has been extended to include variables that affect perceived difficulty, such as abilities, mood, affect, hedonic experience, and mental fatigue (Richter et al., 2016; Gendolla et al., 2019). Wright (Wright, 2014; Wright and Mlynski, 2019) suggests that mental fatigue alters the perceived difficulty and has key implications for effort deployment (see Fig. 1). Specifically: (1) Fatigued individuals should exert more effort than rested ones, as long as success is viewed as possible and worthwhile; (2) They are likely to disengage at lower difficulty levels, reducing the range within which effort is maintained; (3) Effort should remain low for all individuals when success exceeds what rested performers can achieve; and (4) The importance of success should determine the difficulty threshold at which both fatigued and rested individuals withdraw, thus moderating the impact of fatigue on effort. In summary, MIT aligns with a cost-benefit framework but offers more precise and testable predictions regarding the mechanisms of mental fatigue.

To evaluate predictions derived from MIT in the context of mental fatigue, it is crucial to accurately assess effort deployment. Although performance is frequently used as a proxy for effort, it is not a reliable indicator. As an indirect and discontinuous measure, performance is influenced by multiple factors, including individual ability, persistence, and the use of strategy (Locke and Latham, 1990). As a result, changes in effort may occur without producing immediate or observable changes in performance. In contrast, physiological measures enable the real-time

assessment of effort deployment and provide continuous data recording over time (Ranchet et al., 2017).

Building on Wright's integration of MIT within the effort deployment framework (Wright, 1996), effort is closely linked to activity in the sympathetic branch of the autonomic nervous system, particularly its beta-adrenergic effects on the heart. The more accurately an indicator captures this beta-adrenergic influence, the better it reflects changes in effort (Wright, 1996; Richter et al., 2016; Gendolla et al., 2019). One of the most reliable noninvasive indicators of this activity is the pre-ejection period (PEP) — the time interval between the onset of ventricular depolarization and the opening of the aortic valve. PEP is sensitive to cardiac inotropy, which reflects the strength of heart muscle contraction and is directly influenced by beta-adrenergic stimulation (Sherwood et al., 1990; Cacioppo et al., 1994; Krohova et al., 2017a). A shorter PEP duration should be observed, indicating higher effort intensity with increasing task difficulty as long as success is possible and justified (Czarnek et al., 2020; Mallat et al., 2020). Conversely, PEP should increase when a task is perceived as impossible or no longer worth the effort, depending on the perceived importance of success (Richter et al., 2008). A recent study found that PEP increased following 30 min of fatigue induction, which was interpreted as a sign of effort disengagement (Lorcery et al., 2024).

PEP offers distinct advantages over other physiological markers. However, it is essential to acknowledge that PEP can be influenced by cardiac preload and vascular afterload under specific conditions. It thus should be interpreted in conjunction with other measures, including heart rate (HR) and blood pressure (i.e., systolic and diastolic blood pressure). Despite this, a recent meta-analysis supports the reliability of PEP for assessing effort deployment, while highlighting the need for further research across diverse cognitive contexts (Albinet et al., 2024). In the present study, PEP is employed as a primary measure to evaluate MIT predictions related to mental fatigue within a sequential task protocol.

## 2. Overview and predictions

The present experiment aimed to explore mental fatigue mechanisms within a sequential task paradigm from the integrative perspective of MIT. We focused on cardiovascular measures, with PEP reactivity as the primary indicator, to assess changes in effort deployment. This study is particularly valuable because few have explored these mechanisms within a sequential task protocol (Gieseler et al., 2021).

To test this hypothesis, we used a sequential task design with two groups: a fatigue group and a control group. Participants in the fatigue group initially performed a working memory (WM) task that was difficult, while those in the control group watched a documentary and answered several simple questions afterward. Then, both groups completed a difficult WM task (e.g., *n*-back task).<fnref n="1" /> The main prediction, which aligns with findings from Wright (Wright et al., 2003), is that participants in the fatigue group, who experienced a difficult initial task, will enter a state of mental fatigue that alters their perception of task difficulty. Given the low importance of success in this context, they are expected to perceive the required effort as not worthwhile, leading to reduced effort intensity and lower PEP reactivity during the subsequent *n*-back task. In contrast, control participants, who first completed an easy task (i.e., watching a documentary), are expected to be less mentally fatigued. Thus, they should perceive the difficult *n*-back task as

<footnote n="1">¹ Both groups completed an extended familiarization phase of the *n*-back task, which is considered difficult (Hopstaken et al., 2015; Chen et al., 2008) and sensitive to practice effects (Jaeggi et al., 2008). This was done to reduce learning-related improvements and better capture actual effort deployment. However, this phase may have also increased fatigue in both groups, possibly raising it from high to extreme in the fatigue group, and from low to moderate in the control group.</footnote>
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Effort intensity patterns for low- and high-fatigue individuals. Adapted from Wright (2014).</figcaption></figure>

both challenging and worthwhile. Because the task remains within their potential motivation, they are likely to invest more effort, leading to higher PEP reactivity. In summary, we expect a crossover pattern of effort deployment: a decrease from the first to the *n*-back task in the fatigue group (contrast: difficult task [+1] → subsequent task [-1]), and an increase in the control group (contrast: documentary [-1] → subsequent task [+1]). We also anticipate between-groups differences on the *n*-back task, with lower effort in the fatigue group compared to the control group (contrast: fatigue group [0 → -1] vs. control group [0 → +1]).

# 3. Method

## 3.1. Participants and design

The following criteria were required for participation: (a) age between 18 and 25 years, (b) absence of any cardiovascular or neurological disease, (c) no use of any medication that could influence cardiovascular and psychological responses, and (d) no intense physical activity or coffee consumption within the last 24 h. Prior to the experiment, all participants provided written informed consent and received a 15-euro voucher as compensation for their time. The study followed the Declaration of Helsinki, and the local ethics review committee at the University of Toulouse approved the protocol.

Forty-two healthy students (M = 21.67 years, SD = 1.88) from the University of Toulouse, France, were randomly assigned to either the fatigue or control group. Cardiovascular data from one participant could not be collected due to technical issues at the end of the session. Additionally, data from two other participants could not be analyzed due to poor signal quality in their impedance cardiograms. The final sample thus consisted of *N* = 39 participants, with the following cell counts: fatigue group (*n* = 19 participants) and control group (*n* = 20 participants). The study employed a 2 (Group: Fatigue vs. Control, between-subjects) * 2 (Task: Initial vs. Subsequent, within-subjects) mixed design. A sensitivity analysis conducted using G*Power (Faul et al., 2007) for a 2 × 2 mixed-design ANOVA (with a medium effect size, f = 0.25, α = 0.05, and power = 0.80) indicated that our final sample of 39 participants was sufficient to detect significant main effects, interaction effects, and planned contrasts.

## 3.2. Procedure

Participants were randomly assigned to either the “fatigue” or “control” group and seated comfortably in a soundproof, dimly lit room to ensure a controlled environment. To minimize methodological biases,

several precautions were taken. Initially, a physiological baseline phase took place, during which participants viewed an animal documentary and were instructed to remain as still as possible, minimizing their movements. (Piferi et al., 2000; Laborde et al., 2017). This was followed by a familiarization period that included 10 blocks of 30 trials, interspersed with 30-s rest periods, to minimize learning effects on the subsequent task. A reduced learning phase of 5 trials was also conducted under the fatigue condition for the cognitively difficult task. Subsequently, all participants took a standardized five-minute break. The sequential task paradigm was then introduced. Those in the fatigue condition undertook a difficult working memory (WM) task. In contrast, participants in the control condition viewed a documentary. They were informed in advance that they would be asked a few simple questions about the content later in the experiment. In both groups, this was followed by the same subsequent *n*-back task (sequential task paradigm, Fig. 2). Visual analog scales (VAS) measuring fatigue were completed before and after the first task of the paradigm. Cardiovascular activity was measured continuously during the experiment, except during the learning phase.

## 3.3. Stimulus material

### 3.3.1. Difficult WM task (Fig. 3a)

Participants in the fatigue group completed a modified version of the updating task developed by Capa et al. (2011). Each trial began with the presentation of a fixation cross. Participants were then instructed to memorize four individual numbers. During each trial, they updated each number independently based on a sequence of five arithmetic operations, which consisted of simple additions or subtractions (e.g., ±1 or ±2). To maintain a consistent difficulty level, all intermediate and final results were constrained to remain within the range of 0 to 9. At the end of each trial, participants entered the final updated values using the keyboard. Across the 20 trials, the initial numbers, the sequence of operations, and the expected responses varied for each trial.

### 3.3.2. Documentary

Participants in the control group watched a 10-min Technology, Entertainment, and Design (TED) talk. This video was emotionally neutral and focused on the subtleties of the French language.

### 3.3.3. Subsequent task (Fig. 3b)

After completing the difficult WM task (fatigue group) and the documentary (control group), all participants engaged in a visual letter *n*-back task using a 2-back variant. They were asked whether the letter displayed on the screen was a target or a nontarget stimulus. In the 2-
<figure><img src="image_5.png" /><figcaption>Fig. 2. Overview of the experimental protocol.</figcaption></figure>

3a

<figure><img src="image_6.png" /></figure>

3b

<figure><img src="image_7.png" /><figcaption>Fig. 3. a, b. Graphical representation of the difficult WM and the subsequent 2-back tasks with durations in milliseconds; ISI: interstimulus interval.</figcaption></figure>

back variant, a letter was considered a target if it matched the letter presented two trials earlier (e.g., M R S R). Participants were asked to respond as quickly and accurately as possible. Responses were recorded using a PST Serial Response Box (Psychology Software Tools). The stimuli were displayed in the center of the screen and consisted of the letters F, H, J, K, L, M, R, S, T, and V in Calibri point size 70. These letters were selected for their clear visual distinction in French. One trial consisted of one stimulus (500 ms) and one interstimulus interval (2750 ms). The letters were presented randomly with a target rate of 33 %. The n-back task has been widely used in previous research to assess sustained WM engagement (Owen et al., 2005).

## 3.4. Data collection and statistical analysis

### 3.4.1. Subjective data

Participants in each group completed a VAS on two occasions: before and after the first task of the sequential task paradigm (see Fig. 2). They rated their fatigue levels on a scale of 0 to 100, where 0 meant “no fatigue” and 100 indicated “extreme fatigue”. A two-way repeated-measure ANOVA was conducted to assess differences between a mentally difficult condition (fatigue group) and a less difficult condition (control group). The analysis included group (fatigue and control) as a between-

subjects factor and Time (first and second VAS) as a within-subjects factor. Post hoc t-tests (two-tailed) were conducted following a significant interaction, and the results were Bonferroni-corrected for multiple comparisons.

### 3.4.2. Behavioral data

Participants' reaction times (RT) and accuracy on the subsequent n-back task were analyzed. Independent t-tests were conducted for both behavioral measures (accuracy and RT) between the fatigue and control groups.

### 3.4.3. Cardiovascular data

Cardiovascular measures were collected during the baseline period and the sequential task paradigm (see Fig. 2). PEP and HR (both measured in milliseconds [ms]) were assessed by electrocardiogram (ECG) and impedance cardiography (ICG) with a sampling rate of 2000 Hz. For the ECG, three electrodes (Ag/AgCl; EL501, @BIOPAC Systems Inc., Goleta, USA) were placed on the left lower rib, left clavicle, and right clavicle and connected to an amplifier (ECG100 amplifier). For ICG, two pairs of disposable electrodes (Ag/AgCl; LEAD1, @BIOPAC Systems Inc., Goleta, USA) were attached to the left side of the participant's neck and chest in a tetrapolar configuration (left mid-axillary line
xiphoid level).

We processed the raw ECG and ICG data using AcqKnowledge 5.0.6, applying a low-pass filter with a cutoff frequency of 50 Hz. For the ICG signal, we further applied the Daubechies 8 wavelet denoising method with adaptive thresholding as implemented in the BIOPAC package. This method is considered one of the most effective for denoising the ICG signal while minimizing shape distortion (Chabchoub et al., 2016). R-peaks were automatically identified from the ECG signal using a threshold-based peak detection algorithm. Occasional ectopic beats were visually identified and manually replaced by interpolated adjacent RR intervals. HR was then calculated based on the resulting RR intervals. PEP was defined as the time interval between the onset of the R-wave in the ECG and the B-point in the ICG waveform (Berntson et al., 2004). B-point was estimated using a polynomial function based on the RZ interval of valid heartbeats, as proposed by Lozano et al. (2007), visually inspected, and if necessary, manually corrected (Sherwood et al., 1990).

ANOVA or ANCOVA was primarily conducted on PEP and HR with group (fatigue and control) as a between-subjects factor and task (initial and subsequent) as a within-subjects factor. When a significant interaction was identified, we conducted planned contrast analyses to evaluate our two specific, theory-driven hypotheses concerning effort deployment. This method enhances statistical power while testing targeted predictions about mean patterns (Rosenthal and Rosnow, 1985). As explained in the overview and predictions section, we expected a crossover pattern, with the fatigue group showing higher cardiovascular responses during the initial task, followed by a decrease, and the control group showing the opposition pattern; contrast: [1/-1]: [-1/1].<fnref n="2" /> We also expected, the fatigue group to exhibit weaker cardiovascular responses than the control group during the subsequent task; contrast [0/-1: 0/1].

# 4. Results

## 4.1. Familiarization

This methodological precaution was taken to minimize the learning effect. RT and accuracy on the subsequent *n*-back task were analyzed over the ten blocks. A two-way repeated measures ANOVA was calculated with group (fatigue and control) as a between-subjects factor and block (1 to 10) as a within-subjects factor. The results indicated a significant main effect of block on both RT *F*(9,351) = 68.624, *p* < .001, *ηp*<fnref n="2" /> = 0.638 and accuracy *F*(9,351) = 12.978, *p* < .001, *ηp*<fnref n="2" /> = 0.250, with a decrease in RT and an increase in accuracy across blocks (Fig. 4). Post-hoc analysis showed that RT significantly decreased up to block 8, after which it stabilized across the final three blocks (all *p* ≥ .062). In contrast, accuracy improved significantly up to block 2, after which it remained stable (all *ps* = 1.000). No significant differences were found between conditions (*Fs*(1,39) ≤ 0.035, *ps* ≥ .853) or in the interaction between group and block (*Fs*(9351) ≤ 0.536, *ps* ≥ .848). These results suggest that the groups were well matched and that potential learning effects were negligible. Descriptive data for both behavioral measures are presented in Fig. 4.

## 4.2. Subjective data

The two-way repeated measures ANOVA revealed a significant main effect of time, *F*(1,40) = 4.47, *p* = .041, *ηp*<fnref n="2" /> = 0.100, and a significant interaction between group and time on subjective fatigue, *F*(1,40) = 5.02, *p* = .031, *ηp*<fnref n="2" /> = 0.111. The main effect of time on subjective fatigue is reflected in higher values for the second VAS of fatigue (*M* = 49.4, *SE* = 3.9) than for the first (*M* = 43.8, *SE* = 4.0). For the significant interaction, post hoc tests revealed a difference only in the fatigue

group, which reported higher fatigue (*t* (40) = -3.078, *p* = .023, *d* = -0.450), after the first task of the sequential task paradigm. No such difference was found in the control group (*t* (40) = 0.089, *p* = 1.000, *d* = 0.013). There was no significant main effect of group on subjective fatigue, *F*(1,40) = 0.127, *p* = .723, *ηp*<fnref n="2" /> = 0.003. Fig. 5 shows the pattern of fatigue ratings on the VAS.

## 4.3. Behavioral data

The independent *t*-test (two-tailed) revealed no significant differences between groups on either behavioral measure during the subsequent *n*-back task [RT: *t* (40) = 0.227, *p* = .822, *d* = 0.070; accuracy, *t* (40) = 0.440, *p* = .662, *d* = 0.136]. Participants in both groups performed at a high level [Fatigue group: RT, *M* = 529.99, *SE* = 124.89, and accuracy, *M* = 91.59, *SE* = 6.99; Control group: RT, *M* = 539.85, *SE* = 155.00, and accuracy, *M* = 92.47, *SE* = 5.81]. Note, that this does not contradict our assumption that the task was difficult. A task can be considered difficult even when performance is high, as difficulty refers to the effort required to achieve the outcome. It is important to distinguish between effectiveness (i.e., observable performance) and efficiency (i.e., the intensity of effort required to reach that performance).

## 4.4. Cardiovascular data<fnref n="3" />

### 4.4.1. Cardiovascular baselines

Cardiovascular measures tend to stabilize toward the end of the baseline period. Therefore, we used the last three-minute average to define baseline scores for PEP and HR (all Cronbach's ≥ 0.987). Preliminary one-way ANOVAs with group (fatigue and control) as the factor revealed no significant differences in baseline scores between groups (*ps* ≥ 0.111). Baseline scores, means, and standard errors are presented in Table 1.

## 4.5. Cardiovascular reactivity

Cardiovascular change (delta) scores were obtained by subtracting baseline scores from their respective ten-minute task scores during the sequential task paradigm, which included a difficult WM task, documentary, and subsequent tasks (Llabre et al., 1991).

To assess whether baseline scores influenced reactivity scores, preliminary repeated measures ANCOVAs were conducted with baseline scores as covariates. A significant main effect of the covariate was observed for HR (*p* < .001); no other main or interaction effects emerged (all *ps* ≥ .315). Based on these findings, baseline scores were included in the reactivity analysis for HR but not for the PEP analysis.

### 4.5.1. PEP reactivity

A significant interaction was observed between group and task, *F*(1,37) = 21.849, *p* < .001, *ηp*<fnref n="2" /> = 0.371. One-tailed contrasts revealed a crossover pattern: responses were significantly stronger during the initial task than the subsequent *n*-back task in the fatigue group, while the opposite was observed in the control group, *t* (37) = -4.674, *p* < .001, *d* = -1.243, consistent with our first prediction. Moreover, participants in the fatigue group exhibited weaker PEP reactivity than those in the control group during the subsequent *n*-back task, *t* (37) = -2.221, *p* = .033, *d* = -0.780, consistent with our second prediction. Fig. 6a shows the pattern of PEP reactivity.

<footnote n="2">² Contrast: fatigue group [difficult WM task/subsequent *n*-back task]: control group [documentary/subsequent *n*-back task].</footnote>

<footnote n="3">³ For readers interested in gender differences in cardiovascular responses, we conducted independent-samples *t*-tests comparing both baseline values and reactivity scores between women and men. These analyses revealed no significant gender differences in either baseline values or cardiovascular reactivity (all *ps* ≥ 0.176).</footnote>
<figure><img src="image_8.png" /><figcaption>Fig. 4. Reaction time and accuracy familiarization blocks. Bars represent standard errors, *ns* = not significant.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 5. Subjective fatigue scores for VAS 1 and VAS 2 in the two experimental groups (fatigue and control). Values are presented as the mean and SE. VAS: visual analogic scale, 0 = no fatigue, 100 = extreme fatigue; \*p ≤ .05, \*\*p ≤ .01, \*\*\*p ≤ .001, *ns* = not significant.</figcaption></figure>

Table 1

Means and standard errors (in parentheses) of the cardiovascular baseline scores. N = 39; PEP, pre-ejection period (in ms); HR, heart rate (in beats/min).

<table><thead><tr><th></th><th>Fatigue</th><th>Control</th></tr></thead><tbody><tr><td>PEP</td><td>102.07 (2.03)</td><td>102.63 (1.54)</td></tr><tr><td>HR</td><td>80.50 (1.71)</td><td>76.07 (2.08)</td></tr></tbody></table>

4.5.2. HR reactivity

There is a significant interaction between group and task, *F* (1,36) = 12.977, *p* < .001, *η*<sup>2</sup> = 0.265. One-tailed contrasts revealed a crossover pattern: HR reactivity were stronger during the initial task than the subsequent *n*-back task in the fatigue group, while the reverse pattern was observed in the control group, *t* (36) = 3.602, *p* < .001, *d* = 1.189. No significant differences were observed between groups during the subsequent *n*-back task, *t* (36) = 1.168, *p* = .250, *d* = 0.409. These results support hypothesis 1 but do not confirm hypothesis 2 regarding effort-related predictions. Fig. 6b shows the pattern of HR reactivity. It is worth noting that additional descriptive statistics are provided in the Online Supplementary Material.

5. Discussion

The results of the present study support the hypothesis that mental

fatigue, as induced by initial effort deployment in a sequential task paradigm, can be understood through a cost-benefit perspective. A key asset of the study lies in the integration of MIT alongside cardiovascular indicators of effort deployment, which enabled us to formulate and empirically test specific, theory-driven hypotheses.

We demonstrated that participants who initially completed a difficult WM task exhibited reduced effort intensity during the subsequent *n*-back task, as indicated by lower PEP<fnref n="4" /> and HR reactivity. This finding is further supported by increased self-reported fatigue in this group. Higher reported fatigue was associated with greater perceived difficulty and effort, which may explain the reduced engagement during the second task (Engle-Friedman et al., 2018; Goh et al., 2022). In contrast, participants who initially watched a documentary (e.g., performed an easy task) reported similar levels of subjective fatigue, which were associated with increased effort intensity on the subsequent *n*-task, as indicated by heightened PEP and HR reactivity. This supports our first crossover pattern hypothesis. Additionally, effort-related cardiovascular responses during the subsequent *n*-back task were significantly higher in the control group (i.e., those who initially viewed a documentary) compared to the fatigue group, thus supporting our second hypothesis.

From the perspective of MIT, the reduced effort intensity in the fatigue group suggests that the subsequent *n*-back task was perceived as too difficult compared to the importance of success, making the required effort seem unjustified. In contrast, participants in the control group perceived the task as challenging but still worthwhile, which led to greater effort intensity and stronger cardiovascular responses, ultimately producing the expected crossover effort pattern (Brehm and Self, 1989; Wright, 1996; Richter et al., 2016; Gendolla et al., 2019). It is also worth noting that the extended familiarization period phase likely amplified these effects by inducing extreme fatigue in the fatigue group, while inducing moderate, manageable fatigue in the control group, who benefit from a 10-min rest period. (i.e., watching a documentary) between.

the familiarization and the subsequent *n*-back task. These findings align with one of the four predictions proposed by Wright (Wright, 2014; Wright and Mlynski, 2019), namely that fatigued individuals should exert more effort than rested ones as long as success is viewed as possible and worthwhile. This also aligns with Wright's earlier research (Wright et al., 2003), which demonstrated that participants fatigued by an initial difficult task tend to allocate greater effort, measured by SBP, when

<footnote n="4">⁴ Cardiac preload and vascular afterload can influence PEP reactivity; however, since PEP did not accompany HR decreases, the effects of preload are likely minimal (Albinet et al., 2024). However, the lack of direct vascular afterload measurement represents a limitation of this study.</footnote>
6a

<figure><img src="image_10.png" /><figcaption>**Fig. 6.** a, b. Means values, and standard errors for (a) pre-ejection period (PEP) and (b) heart rate (HR) reactivity during the initial task – i.e., Difficult WM task for the fatigue group or documentary for the control group – and the subsequent task (n-back) of the sequential paradigm. ms: milliseconds.</figcaption></figure>

subsequently facing easier challenges rather than more difficult ones. Notably, we replicate these findings using PEP, a more direct and specific measure of cardiac effort. Unlike SBP, which can be influenced by changes in peripheral vascular resistance (Gendolla et al., 2019), PEP remains unaffected by such factors. This strengthens the robustness of our results and highlights the added value of using PEP to assess effort deployment across various contexts (Gendolla et al., 2012; Wright, 1996; Mallat et al., 2020; Albinet et al., 2024; Krohova et al., 2017b; Lurquin et al., 2016; Kaur, 2019). HR also showed relevant results that reinforced the findings from PEP. However, because HR variations can also reflect decreases in parasympathetic activity, their interpretation as a direct measure of effort intensity is limited (Richter et al., 2016).

Another key finding of our study is the absence of significant effects on task performance. This is consistent with recent large-scale studies that have failed to replicate the observed decline in performance using the sequential task paradigm (Hagger et al., 2016; Vohs et al., 2021; Lurquin et al., 2016). An explanation for the absence of behavioral decrements is that performance on our WM tasks may be supported by various strategies, such as monitoring and memory maintenance, passive processing, mental rehearsal, or visualization (Kaur, 2019). This strategic flexibility may also explain the observed effort disengagement, as individuals may switch between strategies to conserve cognitive resources, an idea consistent with MIT (Brehm and Self, 1989). Therefore, incorporating precise, continuous physiological indicators was essential due to the imperfect relationship between effort and performance (Ranchet et al., 2017; Wright et al., 2003; DeLuca, 2005). Indeed, physiological changes often precede observable performance decline, providing valuable insight into the mechanisms underlying mental fatigue.

Concerning the origins of mental fatigue, recent integrative models suggest that effort deployment relies on a cost-benefit analysis, and that mental fatigue may emerge from alterations in this balance caused by the accumulation of harmful metabolic byproducts that impair neural functioning (André et al., 2019; Ishii et al., 2014). This perspective moves away from outdated resource-based explanations and emphasizes how sustained effort can trigger neurobiological changes that influence effort perception and decision-making. For example, Wiehler et al.

(Wiehler et al., 2022) showed that prolonged cognitive effort leads to glutamate accumulation in the lateral prefrontal cortex, which impairs cognitive control and reduces the capacity to invest further effort. In our study, a similar mechanism may have contributed to the reduced willingness to engage in the subsequent task. The metabolic account and MIT should be viewed as complementary, while the former explains how sustained effort induces neurobiological changes that alter effort perception, the latter describes how these altered perceptions influence future engagement decisions. Together, they offer a more integrated and dynamic view of mental fatigue.

## 5.1. Limitations and future research

Some limitations of the present study should be acknowledged. First, although cardiac preload and vascular afterload effects are generally stable during seated cognitive tasks, we cannot completely rule out their influence. Because we did not directly assess hemodynamic changes, it is possible that some of our PEP findings were partly influenced by variations in preload or afterload rather than solely effort intensity. Second, future research would benefit from incorporating different types of rewards or punishments to understand better how the perceived importance of task success influences effort deployment. This approach could also clarify why some studies have reported recovery in performance following mental fatigue induction, especially within a sequential task paradigm. Additionally, it would be valuable to explore tasks beyond working memory to assess whether the observed effects on effort deployment generalize across various cognitive demands. Ultimately, a larger sample size would yield more robust results and provide more substantial support for our conclusions.

6b

<figure><img src="image_11.png" /></figure>

## 6. Conclusion

Investigating the sequential task paradigm through the lens of MIT and cardiovascular measures provides valuable insights into the mechanisms underlying mental fatigue. Participants who had previously completed a difficult WM task showed reduced effort intensity. In contrast, those who first engaged in an easy task (i.e., watching a
documentary showed greater effort intensity on the same subsequent difficult task. These findings suggest that a prior difficult task may increase the perceived difficulty of subsequent tasks. If this perceived difficulty exceeds the individual's potential motivation. In such cases, the task may be quickly deemed not worth the effort, leading to disengagement, especially in contexts where the importance of success is low. Therefore, mental fatigue should not be understood solely as a decline in performance, but rather as a complex and multidimensional phenomenon involving motivational, cognitive, and physiological processes.

### CRediT authorship contribution statement

**Ugo Place:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Pierre-Vincent Paubel:** Resources. **Rémi L. Capa:** Writing – review & editing, Supervision, Resources, Project administration, Funding acquisition, Conceptualization.

### Declaration of competing interest

None.

### Acknowledgements

The National Research Agency supported this study as part of the "Working Memory Resource Depletion Effect in Academic Learning" (WM-RDE) project (Project n° ANR-20-CE28-0014-02).

### Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ijpsycho.2025.113210.

### Data availability

Data will be made available on request.

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