An 8-week jump training did not boost effort value or the willingness to exert effort in a student sample

Johanna Stähler; Maik Bieleke; Wanja Wolff; Markus Gruber; Ursula Fischer; Julia Schüler

doi:10.1371/journal.pone.0352897

. 2026 Jul 16;21(7):e0352897. doi: 10.1371/journal.pone.0352897

An 8-week jump training did not boost effort value or the willingness to exert effort in a student sample

Johanna Stähler ^1,^*, Maik Bieleke ¹, Wanja Wolff ², Markus Gruber ¹, Ursula Fischer ³, Julia Schüler ¹

Editor: Christopher Kirk⁴

PMCID: PMC13375129 PMID: 42461954

Abstract

Physical inactivity remains highly prevalent, partly driven by the aversive nature of effort. However, effort can also be experienced as rewarding, which is associated with greater overall physical activity. Accordingly, the present study investigates whether regular physical exercise alters the self-reported value of physical effort, neural activation during exercise, and the willingness to exert effort. Sixty-two young adults were assigned to either an eight-week high-intensity jump training or a control group. Participants completed a two-task cycling ergometer exercise before and after the intervention. The first task assessed the value of effort at three pre-determined intensity levels, whereas the second task allowed participants to self-select the intensity to measure their willingness to exert effort. Participants’ perceived exertion and state value of physical effort were assessed, along with neural activation in the ventromedial prefrontal cortex and pre-supplementary motor area using functional near-infrared spectroscopy. Bayesian analyses provided evidence against main effects of condition, time, as well as their interaction, for self-reported value of physical effort, willingness to exert effort, and neural activity. This suggests that the value of effort may be relatively stable, as an eight-week training intervention did not alter the value of physical effort or the willingness to exert effort.

Introduction

Insufficient physical activity is widespread among both adults and adolescents [1,2]. Despite the well-documented physical and mental health benefits of being active [1,2] and numerous global initiatives and interventions aimed at increasing activity levels [e.g., 3, 4], inactivity has remained high or even increased in high-income countries [1,2]. One possible explanation is that sport requires physical effort [5], which is inherently costly [6], often perceived as aversive [7], and therefore frequently avoided [8]. For example, people may choose elevators over stairs or devalue desirable outcomes (e.g., improved fitness) because of the effort required to attain them [9]. At the same time, individuals do exert physical effort when striving for valued outcomes [10]. Yet for many, the long-term health benefits do not sufficiently outweigh physical effort’s costs and aversiveness, resulting in insufficient activity levels [11].

At the same time, accumulating evidence indicates that physical effort can simultaneously be perceived as valuable – at least to some degree, for some people, and in some situations [6,10]. Contrary to the effort minimization theory [12], some people seem to seek out effortful activities for the mere sake of exerting effort [13], such as choosing a physically challenging hiking route just because they like the effort. This apparent preference might reflect a higher subjective valuation of physical effort. Recent studies indicate that higher Value of Physical Effort (VoPE) [14] is associated with a more positive effort experience during exertion [15], reduced boredom in physically effortful tasks [16], and greater overall physical activity [14]. Enhancing VoPE could therefore be a promising way to help people be more physically active [17], with potential applications in rehabilitation, programs for older adults, or interventions targeting sedentary lifestyles. If VoPE can be modified, it may offer a complementary pathway to traditional approaches.

Theoretical work suggests mechanisms by which reduced effort aversiveness can increase individuals’ tendency to mobilize effort over time [see 6]. One prominent example is Learned Industriousness, which proposes that repeatedly pairing effort with a reward reduces effort’s aversiveness and increases willingness to exert effort [18]. Notably, effort aversiveness can in principle be reduced via two mechanisms: by decreasing perceived effort costs and/or by increasing VoPE. However, previous theoretical and empirical work has primarily focused on reductions in effort costs [e.g., 19, 20], whereas potential increases in effort valuation have received considerably less attention [10]. Thus, it remains unclear whether repeated physical exercise can increase VoPE.

A further open question concerns the generalizability of potential learning effects. Near-transfer describes greater valuation of effort in the same or similar tasks, whereas far-transfer refers to generalization to different tasks or contexts (e.g., from playing volleyball to other ball games). Findings on both types are mixed [e.g., 20, 21], and some evidence suggests that VoPE may be relatively stable [21]. To date, only one study has examined this mechanism in the context of exercise, focusing exclusively on near-transfer effects. In Bernacer et al. [19], initially inactive participants completed a three-month low-intensity exercise program. After the program effort costs had less influence on effort-based decisions, accompanied by decreased effort-related activity in the anterior cingulate cortex (ACC) [19]. These findings suggest that regular exercise may reduce effort costs and alter neural responses to effort.

This interpretation should be considered in light of the relatively low training intensity employed in the intervention. This may be relevant because effort intensity could play an important role in how effort is evaluated. Given that higher intensities are often valued less [15] and perceived as more aversive [22], they present greater potential for effort to become less aversive through training. In addition, high-intensity exercise may engage neurophysiological mechanisms, such as increased activation of the endogenous opioid and endocannabinoid systems, which have been linked to affective responses and reward processing during intense exertion [23,24]. However, it remains unclear whether training at higher intensities yields stronger effects on effort valuation.

To better understand potential changes in VoPE, it is useful to consider how effort can be assessed at different levels. Effort can be examined in behavioral, subjective, and neurological terms. Research distinguishes between objective effort and subjectively perceived exertion [25]. Objective effort refers to the actual physical effort that can be assessed through physiological markers such as heart rate [26]. Subjective effort includes perceived intensity, typically measured using self-report instruments such as the Rating of Perceived Exertion (RPE) scale [27], as well as valence (positive/negative), assessed, for example, with the Value of Physical Effort (VoPE) Scale [14]. Neurophysiological research further indicates that different brain regions are associated with distinct aspects of effort processing. Activity in the pre-supplementary motor area (preSMA) serves as an indicator of effort intensity [28] and a transcranial magnetic stimulation study suggests that disrupting preSMA activity reduces perceived exertion [29], indicating a key role in effort perception. Regarding effort valence, the ACC primarily tracks decision difficulty [30] or effort costs [19], whereas the ventromedial prefrontal cortex (vmPFC) signals net value (i.e., reward after accounting for effort). Overall, meta-analytic evidence suggest that physical effort exertion and valuation are associated with distinct neural correlates, particularly preSMA and vmPFC [28].

The aim of the present study therefore was to examine whether regular physical exercise alters the value of physical effort during actual physical exertion. Specifically, we investigated whether changes in subjective, behavioral and neural indicators of effort valuation generalized beyond the training task.

Methods

Study overview

This study was part of the research project ProPELL and approved by the ethics committee of the University of Konstanz (ref. 31/2022). Participants provided written informed consent prior to participation. This study investigated whether regular physical effort alters individuals’ VoPE and neural responses to physical effort, or whether VoPE remains relatively stable over time. Participants in the training group (TG) completed a multi-week jump training protocol, and VoPE was assessed pre/post using two cycling tasks, allowing us to test far-transfer effects across task domains [18,20]. Subjective, neural, and behavioral measures of VoPE were collected [20]. We hypothesized that (a) state VoPE during exercise would increase in the TG, potentially varying by effort intensity, (b) vmPFC oxygenation during cycling would increase in the TG, with no changes in preSMA [28], and (c) voluntary exertion (power output and heart rate) and RPE would increase in the TG, but not in the control group (CG).

Participants

Participants were recruited at the local universities between August 22, 2022, and August 11, 2023 (see S1 for inclusion/exclusion criteria). The final sample comprised N = 62 participants (52% female, 48% male) with a mean age of 22.8 years (SD = 3.1), randomly assigned to either a TG (n = 34) or a CG (n = 28). Complete fNIRS data were available for 59 participants (TG n = 32; CG n = 27). A sensitivity analysis using G*Power indicated that, for our 2x2 mixed design and given the actual sample size and 80% power, detectable effect sizes were f = 0.31 for between-subjects effects, and f = 0.18 for within-subject effects and interactions. Participants were compensated for their participation in the ProPELL project and additionally received 10€ per session for participating in this study.

Design and procedure

A ten-week longitudinal design was used with pre- and post-measurements and an eight-week training period. The TG performed high-intensity interval jump training (3x/week, ~ 15 minutes), while CG maintained daily routines. Jump training is well-suited for student samples, efficiently improves fitness [e.g., 31], is simple, time-efficient, and can be performed independently. Pre- and post-testing included a standardized warm-up and two cycling tasks (~2h total). Cycling was chosen for assessment because it permits individualized workload adjustments, precise effort manipulation, and more reliable functional near-infrared spectroscopy (fNIRS) recording, which is not feasible during jumping.

Training intervention.

Training was adapted to participants’ fitness and comprised a warm-up, hoppings, countermovement jumps (CMJs), and high-intensity CMJs, with volume and intensity progressively increased [see [32] for the protocol]. On average, TG participants reached 92% of maximal heart rate and 92% adherence [32]. The training proved partly effective, yielding specific improvements in CMJ height [32], but failed to increase overall physical fitness (e.g., increase in VO_2peak (for details see [32])).

Cycling tasks.

The interval task (15 minutes) consisted of low-, moderate-, and vigorous-intensity bouts (order randomized across participants), each lasting three minutes and separated by two-minute recovery breaks. Workload was individualized using the anaerobic threshold (AT) and the respiratory compensation point (RCP) derived from a Cardiopulmonary Exercise Test (CPET) (performed 2–4 days before both measurement sessions) [33]. Intensities were set to 85% of AT (low); the mean of AT and RCP, calculated as (AT[W] + RCP [W])/ 2 (moderate), and 5% above RCP (vigorous) [33,34]. To ensure the intended intensity, participants maintained a cadence of 85–95 revolutions per minute (rpm), while resistance levels were determined by the Powerbike software [Powerbike; 35]. Participants received real-time visual feedback on cadence and target power. At the end of each interval, RPE and state VoPE were assessed. After a ten-minute recovery break, participants completed a free ride task (10 minutes), cycling a virtual route (Allensbach to Langenrain; southern Germany). They freely selected their intensity without a predefined goal. Every two minutes, RPE and state VoPE were assessed. Heart rate (interval and free ride task) and hemodynamic oxygenation in the vmPFC and preSMA (interval task) were continuously recorded.

Materials

Self-report assessment.

In both tasks, single items were presented via an automated recording using NIRSStim (Version 4.0, NIRx Medizintechnik GmbH, Berlin, Germany, 2016). To optimize timing, the verbal prompts were condensed into brief cues (“Exertion?” for RPE rating, “Joy in exertion?” for state VoPE rating, see below), while full questions and response scales were displayed visually. Participants responded verbally.

Perceived exertion (RPE) was assessed with the question “How strong do you exert yourself right now?” using the Category Ratio 10 scale [27,36], ranging from 0 (“no effort at all”) to 10 (“maximal”) or 11 (“even more than max”) [37]. Test-retest reliability was ICC = 0.61 for low, ICC = 0.51 for moderate, and ICC = 0.54 for vigorous intensity.

State value of physical effort (state VoPE) was assessed with “How much do you like exerting yourself right now?” using a bipolar 10-point scale, ranging from −5 (“I don’t like it at all”), 0 (“neutral”), to +5 (“I like it very much”). Test-retest reliability for state VoPE was ICC = 0.74 for low, ICC = 0.75 for moderate, and ICC = 0.78 for vigorous intensity.

Cyclus 2 ergometer and powerbike software.

Cycling tasks were performed on a Cyclus 2 ergometer (RBM ElektronikAutomation GmbH, Leipzig) controlled via the Powerbike software [35]. Powerbike enabled predefining interval protocols (resistance and time), and simulated outdoor routes by integrating slope and body weight. For both tasks, a virtual mountain bike was used (MTB Modern 22 Spd; 26/36 and 40/11 gears).

Cardiorespiratory measurement.

ECG was measured using the Equivital EQ02 + LifeMonitor (EQ02 + ; Equivital; Cambridge, UK), integrated into a vest and synchronized with PowerLab (PowerLab 16/35; ADInstruments; Oxford, UK). Signals were sampled at 400 Hz and saved via LabChart (Version 8.1.22; ADInstruments, 2022).

Functional near-infrared spectroscopy.

Oxygenation in the vmPFC and preSMA was measured using a multichannel continuous-wave fNIRS system (NIRSport, NIRx Medical Technologies LLC, NY, USA) and NIRStar software (Version 15.3, NIRx Medical Technologies LLC, NY, USA 2020) at 7.81 Hz (760/850 nm) with eight emitters and eight detectors forming 16 channels (25–35 mm). Optodes were arranged according to the international 5/10 system [38] (see Fig 1a) using a custom elastic NIRScap (NIRScap, EASYCAP GmbH, Herrsching, Germany) in multiple sizes and secured with an overcap (EASYCAP GmbH, Herrsching, Germany) to reduce motion artifacts and ambient light. Sensitivity was verified via Atlas Viewer simulations [39] (see Fig 1b) and optode performance was tested prior to each testing day by running a series of diagnostic procedures (e.g., gain modulation).

Fig 1 — a) For the fNIRS measurement, sources (S) and detectors (D) were placed following the international 5/10 system: S1 at Fp1, S2 at AFp3h, S3 at AF3, S4 at FCz, S5 at Cz, S6 at AF4, S7 at AFp4h, S8 at Fp2, D1 at Fpz, D2 at FC1, D3 at FCC1h, D4 at C1, D5 at C2, D6 at FCC2h, D7 at FC2, D8 at AFz. This setup was designed to measure oxygenation in the ventromedial prefrontal cortex (vmPFC) and the pre-supplementary motor area (preSMA). b) The sensitivity map [Atlas Viewer, [39]] indicates a reasonably good capture of the vmPFC and preSMA with the used optode placement.

Data Analysis

Electrocardiogram preprocessing.

ECG data were processed with Kubios HRV Scientific [version 4.1.0;[40]]. Artifacts were visually inspected and manually corrected [41,42]. Noisy segments were excluded from the analysis, and heart rate values were not computed for intervals with excessive noise. 88% of intervals were noise-free, 9% had minor artifacts and were corrected, 1% had significant noise, and 2% were excluded due to technical issues.

fNIRS preprocessing

fNIRS data were preprocessed using HOMER3 [43] (MathWorks Inc., 2017b). First, the hmrR_pruneChannels Homer function excluded channels with too weak or too strong intensity (dRange: 1e-02, 3e + 00; SNRthresh: 2; SDrange: 0.0; 45.0). Negative intensity values were corrected using hmrR_PreprocessIntensity_Negative, and missing data were replaced by spline interpolation with hmrR_PreprocessIntensity_NAN. Optical intensity was converted to optical density using hmrR_Intensity2OD. Motion artifacts were addressed using a hybrid approach [44] employing methods that vary in strictness and methodological angle, enabling sensible noise removal without risking filtering out the neural signal altogether. Artifacts were first detected with hmrR_MotionArtifactByChannel (tMotion = 0.5, tMask = 1.5, STDEVthresh = 50.0, AMPthresh = 0.20) and corrected using hmrR_MotionCorrectSpline (p = 0.99), using relatively liberal parameters appropriate for the high-movement cycling task. Subsequently, hmrR_MotionCorrectWavelet was applied with a stricter-than-usual IQR (Iqr = 1.0 vs. typical ~1.5) to attenuate residual motion spikes, counterbalancing the preceding liberal settings. A bandpass filter (hmrR_BandpassFilt) with cutoff frequencies of 0.01 Hz (high-pass) and 0.5 Hz (low-pass) was applied to remove physiological noise, such as cardiac and respiratory artifacts. Subsequently, data were converted to changes in oxy- and deoxyhemoglobin concentration using hmrR_OD2Conc, which applies the modified Beer-Lambert law [45] with differential pathlength factors of 6.0 for both wavelengths. Finally, the hemodynamic response function (HRF) for relevant intervals (t = 60 sec for each interval) was calculated using hmrR_BlockAvg: Block_Average_on_Concentration_Data. Only the second minute of each interval was used, excluding the initial 60-second ramp-up phase, during which heart rate increases and only stabilizes over time [46], as well as the query period at the end of each interval, where participants gave verbal responses.

Overall, 6.3% of channels were rejected due to insufficient signal quality. Of these rejected channels, 29% were excluded automatically by the preprocessing stream, while the remaining 71% were identified and removed through manual inspection. (To assess the robustness of the findings with respect to preprocessing choices, the data were additionally processed using an alternative fNIRS preprocessing pipeline previously applied in physical effort paradigms [47]. This alternative approach yielded highly similar results and did not change the overall pattern of findings, indicating that the reported results were not driven by specific preprocessing parameter choices.)

Analyses.

Analyses were conducted in R [version 4.3.1, 48] and JASP [49]. Data, R-script, packages, and JASP files are available in OSF (https://osf.io/acp9d/overview?view_only=9b3a9ed691804ab78450486158fe7572). To test comparability of the three effort intensities across conditions and measurement points, heart rate and perceived exertion (RPE) were analyzed using a 2-between (condition: TG, CG) x 3-within (intensity levels: low, moderate, vigorous) x 2-within (time: pre- and post-training) mixed ANOVA in R.

Training effects on VoPE and neural responses were analyzed using Bayesian repeated-measures ANOVAs in JASP using default Cauchy (0, 0.5) priors for fixed effects [49]. This approach allows to quantify evidence both for the hypotheses and in favor of the null hypothesis [50]. Normal residuals with equal variance across groups were assumed. Bayes factors were estimated via 10,000 integration steps and posterior samples via 10,000 Markov Chain Monte Carlo (MCMC) iterations. Model comparisons were based on Bayes Factors (BF10/BF01) and posterior model probability as indicators of relative model adequacy. The complete output can be found in the JASP files on OSF.

For state VoPE and cerebral oxygenation (oxygenated (HbO) and de-oxygenated (HbR) hemoglobin concentration) of the interval task, mean values of the second minute of the ride were analyzed using the same 2-between (condition: TG, CG) x 3-within (intensity levels: low, moderate, vigorous) x 2-within (time: pre- and post-training) Bayesian repeated measures ANOVA design in JASP. For the free ride task, 2-between (condition: TG, CG) x 2-within (time: pre- and post-training) Bayesian repeated measures ANOVAs were conducted for heart rate (10-minute mean), RPE, rendered power (10-minute mean, normalized to the RCP), and state VoPE. All primary analyses were pre-specified, whereas post hoc analyses were considered exploratory and should be interpreted with appropriate caution given the number of comparisons.

In addition to Bayesian analyses, effect sizes were calculated to quantify the magnitude of observed effects. For ANOVA effects, ω² was reported (0.01 = small, 0.06 = medium, 0.14 = large) [51]. For pairwise comparisons, Cohen’s d was reported (0.2 = small, 0.5 = medium, 0.8 = large), along with corresponding 95% confidence intervals [51].

Results

Table 1 displays the descriptive statistics of the interval task and the free ride task for both conditions and both measurement points. The three intensity intervals differ significantly in produced power (all p < .001) and in heart rate (all p < .001), with the vigorous interval eliciting the highest values, followed by the moderate and low intervals. These results indicate the successful manipulation of physical effort intensity (for detailed results see S2). The TG and CG did not differ significantly in heart rate (p = .733), rendered power (p = .068), or RPE (p = .711) across measurement time points (pre-/post-training), suggesting that the task was comparably demanding in both conditions (see S2). Furthermore, baseline comparability between conditions was examined. Although baseline differences were observed for two variables (see S3), subsequent analyses indicated that these differences could not be attributed to condition.

Table 1. Means and standard deviations by interval and free ride task, for the training (TG) and control (CG) groups at both measurement points.

Variable		Low interval		Moderate interval		Vigorous interval		Free ride task
		TG	CG	TG	CG	TG	CG	TG	CG
Power [W]	M1	113.38 ± 29.62	102.79 ± 30.83	156.83 ± 37.17	143.10 ± 38.69	190.13 ± 45.22	174.72 ± 43.64	154.76 ± 45.89	145.61 ± 42.02
Power [W]	M2	115.78 ± 31.59	97.03 ± 27.54	160.35 ± 37.59	139.96 ± 34.32	193.81 ± 43.16	175.38 ± 40.48	150.88 ± 49.91	140.66 ± 40.21
Heart rate [min ^-1 ]	M1	146.47 ± 15.38	150.32 ± 15.16	157.13 ± 13.34	159.39 ± 13.28	162.24 ± 12.07	164.89 ± 9.87	160.00 ± 17.14	162.72 ± 18.20
Heart rate [min ^-1 ]	M2	144.13 ± 13.89	143.39 ± 12.51	155.86 ± 12.18	152.91 ± 13.30	160.29 ± 12.17	159.83 ± 13.51	158.20 ± 15.90	157.92 ± 17.16
RPE	M1	3.12 ± 1.39	3.18 ± 1.36	4.18 ± 1.41	3.82 ± 1.12	5.38 ± 1.53	5.14 ± 1.26	4.10 ± 1.79	3.99 ± 1.47
RPE	M2	2.85 ± 1.40	2.70 ± 1.08	3.75 ± 1.44	3.84 ± 1.43	5.16 ± 1.63	5.11 ± 2.01	3.65 ± 1.52	3.94 ± 1.63
State VoPE	M1	2.15 ± 1.73	1.50 ± 1.93	2.03 ± 2.05	1.36 ± 2.11	1.56 ± 1.80	1.00 ± 2.28	2.11 ± 1.76	1.21 ± 2.22
State VoPE	M2	2.18 ± 1.29	1.18 ± 2.45	2.21 ± 1.59	1.18 ± 2.09	1.54 ± 1.94	0.82 ± 2.54	2.21 ± 1.52	1.13 ± 2.13
preSMA HbO [µmol/l]	M1	−0.09 ± 1.06	0.20 ± 1.58	−0.49 ± 0.76	0.27 ± 2.17	−0.54 ± 0.82	−0.38 ± 1.23	–	–
preSMA HbO [µmol/l]	M2	−0.12 ± 0.71	−0.21 ± 0.84	−0.33 ± 1.03	−0.08 ± 0.93	−0.55 ± 1.18	−0.77 ± 1.24	–	–
preSMA HbR [µmol/l]	M1	0.06 ± 0.54	0.34 ± 0.49	0.27 ± 0.52	0.41 ± 0.82	0.34 ± 0.48	0.61 ± 0.63	–	–
preSMA HbR [µmol/l]	M2	0.11 ± 0.41	0.10 ± 0.39	0.15 ± 0.43	0.28 ± 0.42	0.50 ± 0.53	0.61 ± 0.46	–	–
vmPFC HbO [µmol/l]	M1	1.27 ± 3.81	0.42 ± 3.13	1.09 ± 2.56	0.80 ± 2.46	0.59 ± 3.17	0.52 ± 2.85	–	–
vmPFC HbO [µmol/l]	M2	0.50 ± 1.81	0.54 ± 2.40	1.42 ± 3.55	1.43 ± 2.79	0.72 ± 3.51	0.84 ± 3.19	–	–
vmPFC HbR [µmol/l]	M1	0.36 ± 1.04	0.29 ± 0.91	0.72 ± 1.12	0.70 ± 1.09	0.81 ± 1.00	0.75 ± 0.63	–	–
vmPFC HbR [µmol/l]	M2	0.27 ± 0.45	0.25 ± 0.74	0.63 ± 1.03	0.63 ± 0.75	0.85 ± 0.97	0.86 ± 0.75	–	–

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M1 = pre-training, M2 = post-training; RPE = Rating of Perceived Exertion, range 0–10; state VoPE = state Value of Physical Effort, range −5 - + 5; preSMA = pre-supplementary motor area; vmPFC = ventromedial prefrontal cortex; HbO = oxygenated hemoglobin; HbR = deoxygenated hemoglobin.

Interval cycling task

State VoPE.

A Bayesian repeated measures ANOVA tested whether state VoPE would increase in the TG while remaining stable in the CG. The best model including the Time x Condition interaction, also included main effects of Time, Condition, and Interval, BF₁₀ = 4.98, P(M/data) =.04, ω² = 0.017 (for complete results tables, see OSF), providing substantial evidence for the alternative hypothesis [52]. However, model-averaged inclusion probabilities indicated that only the main effect of interval should be retained, BF_incl = 16.60, P(incl/data) =.98. There was substantial evidence against the main effect of Time, BF_incl = 0.12, P(incl/data) =.25, and against the Time x Condition interaction, BF_incl = 0.13, P(incl/data) =.06. Evidence for the main effect of Condition was inconclusive, BF_incl = 0.51, P(incl/data) =.59.

The Interval effect was further supported by the second-highest Bayes Factor among candidate models, BF₁₀ = 37.97, P(M/data) =.31, providing very strong evidence. Exploratory post hoc comparisons indicated strong evidence that state VoPE ratings were lower during the vigorous interval (M = 1.26, SD = 2.13) compared to the low (M = 1.79, SD = 1.89), BF₁₀ = 167.43, Odds_post = 98.35, d = 0.26, 95% CI [0.04, 0.48], and moderate intervals, BF₁₀ = 140.77, Odds_post = 82.69, d = 0.23, 95% CI [0.04, 0.43], with both differences corresponding to small effect sizes. Substantial evidence supported equivalent ratings between the low and moderate (M = 1.73, SD = 1.98) intervals, BF₁₀ = 0.12, Odds_post = 0.07 (see Table 1 and Fig 2a; for complete results table, see OSF).

Fig 2 — CG = control group, TG = training group. a) The main effect of Interval was strongly supported by the data. Post hoc comparisons indicated strong evidence that state VoPE was lower during the vigorous interval compared to the low and moderate intervals. b) vmPFC HbR: The main effect of Interval was strongly supported by the data, with lower concentration in the low than in the moderate and vigorous intervals.

HbO concentration vmPFC.

A Bayesian repeated measures ANOVA tested whether the vmPFC HbO concentration would increase in the TG while remaining stable in the CG. The best model including the Time x Condition interaction, also included main effects of Time and Condition, BF₀₁ = 37.19, P(M/data) =.01, providing very strong evidence for the null hypothesis [52] (see Fig 2b). Additionally, model-averaged inclusion probabilities indicated that the null model was substantially more likely than all competing models that included the predictors Interval, BF_excl = 9.81, P(excl/data) =.78, Time, BF_excl = 10.62, P(excl/data) =.79, Condition, BF_excl = 8.10, P(excl/data) =.74, or their interactions, BF_excl = 26.82, P(excl/data) =.74 (for complete results table, see OSF).

HbR concentration vmPFC

A Bayesian repeated measures ANOVA tested whether the vmPFC HbR concentration would increase in the TG while remaining stable in the CG. The best model including the Time x Condition interaction, also included main effects of Time, Condition, and Interval, BF₁₀ = 8441.33, P(M/data) =.23, ω² = 0.000, providing very strong evidence for the alternative hypothesis. However, model-averaged inclusion probabilities indicated that only the main effect of Interval should be retained in the model, BF_incl = 151530.18, P(incl/data) = 1.00. There was evidence against the other predictors, such as Time, BF_incl = 0.10, P(incl/data) =.22, Condition, BF_incl = 0.12, P(incl/data) =.25, and their Interactions, BF_incl = 0.03, P(incl/data) =.02 (for complete results table, see OSF).

The Interval effect was further supported by the highest Bayes Factor among all candidate models, BF₁₀ = 401155.86, P(M/data) =.60, providing decisive evidence. Exploratory post hoc comparisons indicated strong evidence that the HbR concentration in the vmPFC was lower during the low interval compared to the moderate (BF₁₀ = 223.31, Odds_post = 131.17, d = 0.41, 95% CI [0.10, 0.67], small effect) and vigorous intervals (BF₁₀ = 7.23*10⁺⁷, Odds_post = 4.25*10⁺⁷, d = 0.58, 95% CI [0.33, 0.83], moderate effect). Evidence for a vmPFC HbR difference between the moderate and vigorous intervals was inconclusive, BF₁₀ = 0.91, Odds_post = 0.54 (see Table 1 and Fig 2b).

HbO concentration preSMA.

A Bayesian repeated measures ANOVA tested whether the preSMA HbO concentration would be stable across conditions and differ between intervals. The best model including the main effect of Interval and the Time x Condition interaction, also included main effects of Time and Condition, BF₁₀ = 12.93, P(M/data) =.04, ω² = 0.000, providing strong evidence [52]. However, model-averaged inclusion probabilities indicated that only the main effect of Interval should be retained, BF_incl = 56.14, P(incl/data) =.99. There was substantial evidence against including Time, BF_incl = 0.19, P(incl/data) =.34, and the Time x Condition interaction, BF_incl = 0.18, P(incl/data) =.08, while evidence against including Condition was inconclusive, BF_incl = 0.32, P(incl/data) =.47 (for complete results tables, see OSF).

The Interval effect was further supported by the highest Bayes Factor among all candidate models, BF₁₀ = 120.25, P(M/data) =.37, providing decisive evidence. Exploratory post hoc comparisons indicated strong evidence that the preSMA HbO concentration was lower during the vigorous compared to the low (BF₁₀ = 54483.89, Odds_post = 32003.89, d = 0.44, 95% CI [0.21, 0.67], small effect) and moderate intervals (BF₁₀ = 10.50, Odds_post = 6.17, d = 0.35, 95% CI [0.02, 0.68], small effect). There was substantial evidence in favor of similar preSMA HbO concentrations between the low and moderate intervals (BF₁₀ = 0.23, Odds_post = 0.14) (see Table 1 and S2 in the OSF).

HbR concentration preSMA.

A Bayesian repeated measures ANOVA tested whether the preSMA HbR concentrations would be stable across conditions and differ between intervals. The best model including the main effect of Interval and the Time x Condition interaction, also included main effects of Time, Condition, and the Time x Interval interaction, BF₁₀ = 1993.84, P(M/data) =.03, ω² = 0.027, providing decisive evidence [52]. However, model-averaged inclusion probabilities indicated that only the main effect of Interval should be retained, BF_incl = 8615.71, P(incl/data) = 1.00. There was substantial evidence against including Time, BF_incl = 0.13, P(incl/data) =.27, and the Time x Condition interaction, BF_incl = 0.12, P(incl/data) =.05, whereas evidence regarding Condition was inconclusive, BF_incl = 0.54, P(incl/data) =.60 (for complete results tables, see OSF).

The Interval effect was also supported by the second-highest Bayes Factor among all candidate models, BF₁₀ = 19943.26, P(M/data) =.31, providing decisive evidence. Exploratory post hoc comparisons indicated strong evidence for higher preSMA HbR concentrations during the vigorous compared to the low (BF₁₀ = 134766.05, Odds_post = 79161.72, d = 0.70, 95% CI [0.31, 1.09], moderate-to-large effect) and moderate intervals (BF₁₀ = 90.32, Oddspost = 53.05, d = 0.45, 95% CI [0.12, 0.78], small-to-moderate effect). Evidence was inconclusive about the difference between the moderate and the low interval, BF₁₀ = 2.01, Odds_post = 1.29 (see Table 1).

Free ride cycling task

Heart rate.

A Bayesian repeated measures ANOVA tested whether heart rate would increase in the TG while remaining stable in the CG. The best model including the Time x Condition interaction, also included main effects of Time and Condition, BF₀₁ = 14.66, P(M/data) =.03 (for complete results tables, see OSF), providing strong evidence for the null hypothesis [52]. Model-averaged results indicated substantial evidence against including the Time x Condition interaction, BF_excl = 8.05, P(excl/data) =.97, and the main effect of Condition, BF_excl = 3.60, P(excl/data) =.71, whereas evidence for Time was inconclusive, BF_excl = 2.31, P(excl/data) =.61 (see Fig 3a).

Fig 3 — In the raincloud plots, the mean values are shown with standard deviation instead of the typically displayed box plots. CG = control group, TG = training group. a) Heart rate in min-1. b) Relative power in percent of the threshold value of the RCP. c) RPE = Rating of Perceived Exertion. d) state VoPE = state Value of Physical Effort. The main effect of Condition was substantially supported by the data (BF₀₁ = 0.33), with higher state VoPE in the TG than the CG.

Relative power.

A Bayesian repeated measures ANOVA tested whether relative power would increase in the TG while remaining stable in the CG. The best model including Time x Condition interaction, also included main effects of Time and Condition, BF₀₁ = 15.46, P(M/data) =.03, providing strong evidence for the null hypothesis [52] (for complete results tables, see OSF). Model-averaged results indicated substantial evidence against including the Time x Condition interaction, BF_excl = 8.63, P(excl/data) =.97, whereas evidence for Time, BF_excl = 2.89, P(excl/data) =.66, and Condition, BF_excl = 2.66, P(excl/data) =.64, was inconclusive (see Fig 3b).

Perceived exertion.

A Bayesian repeated measures ANOVA tested whether RPE would increase in the TG while remaining stable in the CG. The best model including the Time x Condition interaction, also included main effects of Time and Condition, BF₀₁ = 4.75, P(M/data) =.07, providing substantial evidence for the null hypothesis [52] (for complete results tables, see OSF). Model-averaged results indicated that evidence for Time was inconclusive, BF_excl = 1.17, P(excl/data) =.44. There was substantial evidence against the inclusion of Condition, BF_excl = 3.43, P(excl/data) =.70, and the Time x Condition interaction, BF_excl = 3.39, P(excl/data) =.93 (see Fig 3c).

State VoPE

Bayesian repeated measures ANOVA tested whether state VoPE would increase in the TG while remaining stable in the CG. The best model including the Time x Condition interaction, also included main effects of Time and Condition, BF₀₁ = 6.15, P(M/data) =.03, providing substantial evidence for the null hypothesis [52] (for complete results tables, see OSF). Model-averaged results indicated evidence against including the main effect of Time, BF_excl = 6.27, P(excl./data) =.81, and the Time x Condition interaction, BF_excl = 7.50, P(excl./data) =.97 (see Fig 3d). The main effect for Condition, BF_excl = 0.48, P(excl/data) =.24, ω² = 0.039, was anecdotally supported. Exploratory post hoc comparisons for Condition further indicated substantial evidence that state VoPE was higher in the TG (M = 2.16, SD = 1.64) than the CG (M = 1.17, SD = 2.17) (BF₀₁ = 0.05, Odds_post = 0.05, d = 0.55, 95% CI [0.19, 0.92], moderate effect).

To account for baseline differences in VoPE, we conducted a Bayesian ANCOVA with baseline VoPE as covariate. Model comparison favored the model including only baseline VoPE, BF₀₁ = 6.37x10^-11, (P(M/data) =.64), over the model including baseline and Condition, BF₀₁ = 0.316, P(M/data = .36). Effect inclusion revealed evidence for excluding Condition as additional predictor, BF_excl = 1.74, P(excl/data) = 0.635, indicating that Condition did not explain variance beyond baseline VoPE.

Discussion

This study investigated whether regular exposure to physical effort through high-intensity exercise alters individuals’ value of physical effort (VoPE) and neural responses to physical effort. The design offers several strengths, including high ecological validity [19], the successful implementation of three effort intensities, and a rigorous multi-method approach to capture different facets of effort valuation. Contrary to our hypotheses, eight weeks of jump training did not yield reliable evidence for changes in state VoPE, vmPFC responses during exertion, or voluntary exerted effort in a transfer task. These findings suggest that VoPE may be less readily modified than anticipated. Interestingly, although the TG reported generally higher state VoPE than the CG in the free ride task, ANCOVA controlling for baseline VoPE indicated that this difference was independent of the training. This reinforces the notion that VoPE may be relatively stable, particularly when far-transfer is tested.

Importantly, evidence for transfer effects in the domain of physical effort is considerably more limited and less consistent than in cognitive effort research, with learning effects often appearing to be task- and context-specific [53]. Although previous studies have demonstrated both near- and far-transfer effects, like reduced perceived physical effort costs and increased industriousness, in both animals [54] and humans [19], the conditions under which such transfer effects occur remain poorly understood.

Several characteristics of the intervention provide useful starting points for explaining the absence of learning-related changes. First, the training protocol was considerably more intense than in earlier studies [19,54]. While we initially assumed that high-intensity training would provide a strong learning signal, lower-intensity training may offer a more favorable context for effort-based learning. Our findings suggest that the aversive nature of intense effort [22] could attenuate the development of positive effort valuation. Importantly, the effectiveness of a given training intensity may depend on individual differences such as preference and tolerance to physical effort [55], cardiorespiratory fitness, or prior levels of physical activity. Future research should therefore systematically examine how different intensities interact with individual characteristics to shape VoPE learning, and to identify which individuals benefit most from specific training intensities.

Second, despite the high intensity of the training protocol, its overall training effect was limited [32]. Although jump performance improved, general fitness did not increase, suggesting that the training might not have been sufficiently effective to induce broader changes affecting higher-order motivational processes such as VoPE.

Moreover, according to the theory of Learned Industriousness [18], effort must be paired with rewards to reduce its subjective costs and potentially enhance its value. Such reinforcement can be inherent to the task itself or provided externally. In the context of physical training, inherent rewards may include improvements in physical fitness, weight loss, or body toning. Previous studies reporting decreased perceived effort costs and increased industriousness without explicit external reinforcement [19,54] may therefore have benefited from such inherent rewards. Based on these findings, and to avoid interference with other aspects of the research project, we refrained from implementing external rewards, expecting the training itself to be sufficiently rewarding. However, our findings suggest that this may not have held true. Participants exerted substantial effort, yet experienced little objectively noticeable improvements [32], which may have weakened reinforcement and limited increases in VoPE to a transfer task. Importantly, participants’ perception of progress or sense of competence were not assessed, which might have varied between participants even in the absence of measurable gains. This represents a methodological limitation that constrains interpretations regarding whether reinforcement via inherent rewards contributed to learning-related mechanisms. Interestingly, we did not observe a decrease in VoPE either, indicating that unrewarded effort neither enhances nor diminishes VoPE in a transfer task. Although previous studies also lacked explicit external rewards [19,54], the considerably higher effort intensity of our training protocol might have contributed to the different results. Given that higher efforts are typically associated with more negative affect [22] and thus greater inherent costs, the lack of adequate reinforcement following such strenuous effort may have further limited learning-related changes in VoPE. However, evidence from the cognitive domain is mixed regarding whether secondary reinforcement alone can effectively alter effort valuation [20,21]. To test whether consistent pairing of effort and reward is necessary for VoPE learning, future studies could implement structured reinforcement strategies, such as increasing awareness of progress through fitness assessments, explicit feedback, or by providing rewards proportionally to exertion rather than outcomes. This would offer a more rigorous test of Learned Industriousness in the domain of physical effort.

Another factor may lie in the timing of state VoPE assessment. Theories on effort suggest that individuals are more likely to value effort retrospectively once it has been completed, rather than during the effort [6,17]. Effort might be processed differently before, during, and after an activity [17]. In particular, during physical exertion, individuals are often focused on performance optimization and minimizing discomfort [12,17]. In contrast, retrospective evaluations may allow for a more reflective and positively biased appraisal of the experience [17]. Notably, these conceptual distinctions were articulated more clearly in the literature only after data collection for the present study had been completed, whereas the theoretical landscape at the time of study planning was less differentiated. Accordingly, VoPE was assessed concurrently during active exertion in a separate laboratory task, and participants were not asked to retrospectively evaluate the effort involved in the training itself. Future studies could benefit from assessing VoPE at multiple times to more accurately capture the temporal dynamics of effort valuation and potential learning effects [17].

Another possible explanation lies in the developmental sensitivity of learning VoPE [10]. Adolescents appear to be less sensitive to effort costs and exert more effort relative to task demands and rewards at stake than adults [56]. This period of heightened responsiveness may facilitate the learning of the relationship between effort and reward, making it easier to internalize the contingency that greater effort leads to greater rewards. By contrast, by the time individuals reach early adulthood, as in the current sample, it may be more challenging to relearn or modify their VoPE.

While this observation opens an interesting avenue for developmental research, it is also important to consider potential moderating factors such as previous learning experiences in the sport context, or genetic predispositions (e.g., differences in the dopaminergic reward system) that may influence how VoPE is learned. The present study was designed to test whether VoPE can be modified through regular physical exercise. Consequently, potential moderation or subgroup effects were not systematically examined. Moreover, given the modest sample size, the study was not sufficiently powered to reliably detect moderation effects. Future studies with lager sample sizes should therefore test similar interventions in adolescent samples and systematically examine individual differences that may shape sensitivity to learning VoPE. In addition, it may be informative to consider further variables such as physiological indicators (e.g., VO₂max), prior physical activity levels, and individual differences in effort tolerance as potential additional moderators.

Finally, the current study assessed neural activation during periods of (partially) intense physical exertion, which offers valuable ecological validity. However, although fNIRS is relatively robust to motion artifacts [57], it is important to note that motion-related noise cannot be fully excluded, particularly in the context of physical demanding tasks such as a cycling task. Despite careful preprocessing and quality control procedures, residual motion artifacts may have influenced the signal and could have contributed to the observed null findings. Importantly, however, no corresponding effects were observed at the behavioral or subjective level, suggesting that the absence of neural differences is consistent across multiple levels of analysis and not solely attributable to measurement noise.

Furthermore, physical exercise involves multiple concurrent cognitive processes [58], which may obscure neural mechanisms specific to VoPE. Moreover, interpreting activation in specific brain regions necessarily relies on simplified heuristic functional attributions, which entails the risk of oversimplification and reverse inference. While this approach was useful for addressing the present research question at the current stage of the literature, it may have constrained our ability to draw clearer inferences, as network neuroscience emphasizes that functions likely emerge from interactions rather than one-to-one mappings between regions and processes.

Future research could expand on this by using whole brain functional analyses, examining neural activation during physical effort-related decision-making [19] or by employing more localized physical effort paradigms, such as grip force tasks, rather than whole-body exercises like cycling. Although such paradigms offer lower ecological validity, they would help disentangle distinct cognitive components and additionally allow the use of neuroimaging techniques like fMRI. In addition, the limitation of fNIRS to cortical surface activity [59] could be addressed in future studies by using fMRI to investigate subcortical regions known to be involved in effort and reward processing such as the amygdala [60], nucleus accumbens [61], and ventral striatum [62].

Taken together, our findings suggest that VoPE may be more stable and less malleable through training than previously assumed [e.g., [18][21]]. Nevertheless, limitations of the study design may have prevented capturing potential changes in VoPE, as suggested by previous research in both animals [54] and humans [19], as well as findings from the domain of cognitive effort [20]. Rather than providing a conclusive answer, our results open multiple avenues for future research to better understand the conditions under which VoPE potentially can be modified. Such efforts could help clarify whether VoPE is indeed systematically modifiable through training, or whether it represents a more stable, trait-like construct that resists modification under certain conditions.

Conclusion

In summary, this study provides initial insights into whether regular physical exercise can influence the valuation of physical effort during exercise across behavioral, subjective, and neural levels. Contrary to our expectations, eight weeks of high-intensity jump training did not alter participants’ willingness to exert effort, their self-reported value of physical effort (VoPE), or neural responses in the vmPFC associated with effort valuation. Together, these findings suggest a relative stability of concurrent effort valuation in the context of the present training paradigm. Future research should therefore examine whether different training characteristics (e.g., training intensity), timing of VoPE assessment, the role of intrinsic and extrinsic rewards, or other populations (e.g., active individuals, adolescents) influence the modifiability of effort valuation.

5.1. Use of Generative AI

During the preparation of this work, the authors used ChatGPT to improve the readability and language of the manuscript. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

Data Availability

Materials, data, and scripts used in this manuscript are available at OSF (https://osf.io/acp9d/overview?view_only=9b3a9ed691804ab78450486158fe7572).

Funding Statement

The authors gratefully acknowledge funding from the Committee on Research (AFF) at the University of Konstanz for the research initiative “ProPELL: Promoting Physical Exercise in Lab and Life“. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0352897.r001

Decision Letter 0

Christopher Kirk

10 Nov 2025

-->PONE-D-25-42417-->

An 8-week jump training did not boost effort value or the willingness to exert effort in a student sample

PLOS ONE

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Please pay particular attention to comments regarding how the fNIRS data have been analysed and presented.

There are also several comments requesting that some statements in the discussion need to be modified to ensure they are fully in keeping with the what the data show.

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Reviewer #3: Yes

Reviewer #4: Partly

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Reviewer #4: No

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Reviewer #4: Yes

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Reviewer #1: Thank you for the invitation to review. I have the following comments:

1. Introduction:

a. In several places the text uses “effort cost” and “effort value” interchangeably, although these are two distinct psychological and neurobiological constructs. It would be helpful to emphasise more clearly that effort can be both costly and valuable at the same time, and that the perception of this relationship depends on context, the individual, and prior experience.

b. The impact of training on VoPE (Value of Physical Effort) may be modulated by factors such as previous sporting experience, temperamental traits, intrinsic motivation, or genetic predispositions (e.g. differences in the dopaminergic reward system). The text focuses mainly on group-level effects while overlooking potentially important individual differences.

c. The authors correctly note that most studies concern low to moderate intensities. However, there is no clear indication that high-intensity efforts (e.g. maximal intervals) may involve different neurophysiological mechanisms (e.g. stronger engagement of the opioid and endocannabinoid systems), which could alter the relationship between effort cost and value.

d. The discussion of near- and far-transfer is valuable, but it lacks an explicit acknowledgement that in the literature on physical effort the transfer of effects is far harder to demonstrate than in cognitive effort research. Learning mechanisms in the context of physical activity may be more task-specific, which should be highlighted as a potential limitation.

e. The argument relies on a few fMRI studies concerning the ACC and vmPFC. However, the methodological limitations are not addressed, such as the low statistical power of neuroimaging studies or the problem of reverse inference. Assigning overly direct functions to brain regions (e.g. ACC = “cost”, vmPFC = “net value”) risks oversimplification.

f. The text provides a good summary of the state of knowledge, but practical implications are missing. If regular training can alter the neural valuation of effort, it would be important to discuss how this knowledge could be applied in designing health interventions, e.g. in programmes for activating older adults, in rehabilitation, or in strategies to counteract sedentary lifestyles.

2. I recommend shortening the Methods section – it is overly lengthy and repetitive.

3. Procedure and Materials:

a. Selection of time window for fNIRS – using only the second minute of each interval (excluding onset and offset) is methodologically justified in terms of signal stabilisation and limiting artefacts, but may restrict the ability to capture effort dynamics. A comparative analysis with the full interval duration should be considered to ensure that important changes were not missed.

b. Definition of exercise intensity – basing intensity on metabolic thresholds (AT, RCP) is a strength as it allows individualisation of loads. Nevertheless, adopting fixed values (e.g. 85% AT, mean of AT and RCP, RCP + 5%) may produce differences in the subjective perception of effort between participants. Additional indices (e.g. % VO₂max, % HRmax) would be valuable.

c. The use of single-item questions and verbal scales is convenient but may reduce measurement sensitivity. Participants might struggle to differentiate states precisely in short time frames. Was test–retest reliability (ICC) checked? – 10.1016/j.jcm.2016.02.012

d. fNIRS measurement – the chosen optode configuration and sensitivity simulations support measurement reliability, but fNIRS in the vmPFC and preSMA is particularly susceptible to motion artefacts and circulatory confounds. The procedures for signal quality control (e.g. filtering, artefact correction, trial rejection) should be described in more detail.

e. The whole session lasts around two hours and involves both high-intensity exercise and additional measurement tasks. This may induce fatigue in participants, potentially affecting RPE, VoPE and physiological parameters. It should be stated how fatigue effects were controlled (e.g. recovery breaks, monitoring of resting HR between tasks).

4. L241 – “training group (n = 34) or a control group (n = 28).” – According to the conducted analyses, this sample size was sufficient to detect a large effect with 80% power. Please indicate this in the manuscript – 10.1016/j.apmr.2025.05.013

5. L361 – “used Bayesian repeated measures ANOVAs” – Please calculate effect sizes and provide confidence intervals. For group differences (Cohen’s d or Hedges’ g), small, medium, and large effects should correspond to 0.1, 0.4, and 0.8, respectively – 10.1016/j.apmr.2025.05.013

6. L366 – Bayes Factors and Cohen’s d represent different approaches: BF indicates the strength of evidence for a hypothesis, while d describes the magnitude of the effect. Literature (e.g. Lakens, 2013; van Doorn et al., 2021) often recommends reporting both, as they complement one another: Bayes Factor = evidential strength, Cohen’s d = effect size irrespective of evidence sufficiency.

7. Results – Please highlight which findings fall within medium and which within large effect sizes. In the conclusions, please base interpretations only on significant p values and large effect sizes.

8. L383 – I suggest closing the paragraph with the statistical outcomes, while the procedural details described by the authors should, in my opinion, be moved earlier.

9. Discussion and Conclusions:

a. The authors suggest that the lack of change in VoPE indicates its relative stability. However, it is possible that the effect was masked by the specifics of the intervention (high intensity, lack of systematic rewards). Therefore, the conclusion about “stability” should be stated more cautiously, with limitations of protocol sensitivity underlined.

b. The point that high intensity may be less beneficial than low or moderate intensity is valuable. However, no moderation analysis was performed – e.g. whether individual tolerance to effort (VO₂max, previous physical activity) influenced the results. Such an analysis could provide important insights into who benefits most from the intervention.

c. The authors refer to the theory of Learned Industriousness and attribute the lack of effect to the absence of rewards. This is a coherent interpretation, but the question remains whether a sense of competence (e.g. improvements in fitness tests) may not have been sufficiently emphasised. The absence of monitoring participants’ perception of progress is a significant limitation.

d. The discussion that VoPE ratings during effort may differ from retrospective evaluation is very valuable. However, the study lacks even a single retrospective measure (e.g. a post-training-cycle questionnaire). This omission prevents verification of one of the authors’ key hypotheses.

e. The observation that young people may be more susceptible to VoPE modification opens an interesting research avenue. Nevertheless, potential sex, cultural or personality differences (e.g. temperamental traits) that may modulate the learning of effort value should also be discussed.

f. The authors rightly note that fNIRS is limited to cortical surface areas and does not capture subcortical structures (e.g. nucleus accumbens). However, their conclusion that no effect was observed “neuronally” is too far-reaching. It should be made clear that the findings refer only to vmPFC and preSMA activity, not the entire reward or effort network.

Yours sincerely,

Reviewer #2: However, the manuscript is methodologically accepted, I recommend an extensive revision of the language used across the manuscript starting from the title itself as it is ambiguous and poorly written. In addition, I find it challenging to address any added value to the existing knowledge. the rationale of the research should clearly fill gaps in the literature.

Reviewer #3: The topic of the study is relevant, the results are interesting to the reader, and the manuscript is generally well written.

However, in my opinion, the text contains an excess of detail and unnecessary information, which forces the reader to go back and forth to fully understand the context. A manuscript exceeding 20 pages becomes difficult to follow. I strongly recommend that the authors summarize and streamline the content to improve readability and focus on the main findings.

For example:

The Introduction consists of ten paragraphs, which is considerably more than the average research paper. Several of these paragraphs serve as a literature review rather than a justification for the study.

The subsection titled Present Research should not appear within the Introduction; instead, it should be incorporated succinctly in the Methods section.

The description of the experimental protocol is excessively long and could be condensed.

The Data Analysis section extends to six paragraphs, followed by further detailed descriptions of data processing for each signal, which is again overly elaborate.

In Table 1, the first two variables present means and standard deviations as rounded numbers. Please ensure that all variables use the same number of decimal places.

Each results table is followed by three to four paragraphs of explanation, which is too extensive for a single table.

In summary, my comments aim to encourage the reduction of redundant and irrelevant information to help maintain the reader’s attention on the study’s key findings and contributions.

Reviewer #4: This paper looks at whether an 8-week high-intensity jump training program can change how people value physical effort and their willingness to try hard. This is an important question, and the study is ambitious, with strengths like a randomized controlled design, real-world relevance, multiple assessment methods (self-report, behavior, and fNIRS), high participation, and open data access. The paper is well-written, and the discussion offers useful insights about no significant findings.

However, several key issues need major changes before the paper can be published. These issues affect how we understand and trust the main conclusions.

Key Issues Needing Major Changes

Inconsistency in Study Design Reasoning

The paper mentions that high-intensity effort is often unpleasant. Despite this, the study uses a high-intensity program as the main intervention. This is inconsistent and might have reduced the chance of seeing positive changes. This choice must be clearly explained in the introduction, and the risks and challenges should be discussed openly.

fNIRS Data Processing Concerns

The authors used "less strict" methods to correct motion artifacts. This increases the risk of noise in the neural data, which might hide real effects. The authors should either:

Provide extra analyses with stricter filtering to show the results are strong, or

Be cautious in interpreting the neural findings, acknowledging that the results might be unreliable due to noise.

Baseline Group Differences in VoPE Not Addressed

The training group had higher VoPE than the control group at the start in the free ride task. This is a major issue that affects the fairness of the randomization. The authors need to:

Clearly show baseline differences,

Use statistical methods (e.g., ANCOVA) with baseline VoPE as a factor, and

Discuss how this affects the idea that VoPE is "stable."

Intervention Fails to Produce Overall Fitness Improvement

While jump height improved, VO₂peak and general fitness did not. This suggests the program might not have been strong enough to change higher-level motivation like VoPE. This should be seen not just as a lack of reinforcement but also as a possible failure of the program's strength.

In summary, while this study makes an important attempt to test the malleability of VoPE, the above concerns must be resolved before the conclusions can be considered robust. Addressing these issues will require additional analyses, reframing of theoretical justification, and methodological clarification. Therefore, I recommend Major Revision.

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PLoS One. 2026 Jul 16;21(7):e0352897. doi: 10.1371/journal.pone.0352897.r002

Author response to Decision Letter 1

24 Jan 2026

Point-by-point reply

Manuscript number: #PONE-D-25-42417

Article title: An 8-week jump training did not boost effort value or the willingness to exert effort in a student sample

--------------------------------------------- comments Christopher Kirk ----------------------------------------

Dear XXX,

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Christopher Kirk, PhD

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1.Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Thank you for pointing us to these templates. We have revised the manuscript accordingly.

2. Thank you for stating the following financial disclosure:

If this statement is not correct you must amend it as needed.

Please include this amended Role of Funder statement in your cover letter; we will change the online submission form on your behalf.

We appreciate your comment and have now included the Role of Funder statement in the cover letter.

3. We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match.

When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section.

Thank you for pointing this out. We have now revised this information.

We appreciate this guidance and have accordingly removed the funding statement from the manuscript.

Thank you for this comment. We have accordingly included the requested information in the Methods section (p. 13).

Additional Editor Comments:

Please pay particular attention to comments regarding how the fNIRS data have been analysed and presented.

There are also several comments requesting that some statements in the discussion need to be modified to ensure they are fully in keeping with the what the data show.

We would like to express our sincere gratitude to you and the reviewers for your constructive feedback on our manuscript. We have carefully considered your thoughtful comments and made revisions accordingly. In particular, we focused on reviewing the fNIRS-related sections, clarifying the discussion of the results, streamlining the introduction and shortening the manuscript to about 20 pages.

We believe all changes have greatly improved the quality of our manuscript, and we truly appreciate your time and effort in helping us enhance our work.

In the following, you will find our responses highlighted in blue, along with citations of the manuscript in bold and italics. Newly added passages related to the comment are indicated in red to ensure clarity.

-------------------------------------------------- comments Reviewer #1 ----------------------------------------

Thank you for the invitation to review. I have the following comments:

1. Introduction:

We appreciate this helpful comment and fully agree that effort cost and value represent distinct constructs. We have carefully revised the manuscript to ensure that cost and value are no longer used interchangeably. In addition, we explicitly clarified that these constructs are conceptually distinct and may co-occur. We believe these revisions improve the conceptual clarity of the manuscript

“Conversely, accumulating evidence indicates that physical effort can simultaneously be perceived as valuable – at least to some degree, for some people, and in some situations (12,20).” (pp. 4-5)

“Consistent with this, theoretical work suggests mechanisms by which reduced effort aversiveness can increase individuals’ tendency to mobilize effort over time (see 12). One prominent example is Learned Industriousness, which proposes that repeatedly pairing effort with reward reduces effort’s aversiveness and increases willingness to exert effort (30). Notably, effort aversiveness can in principle be reduced via two mechanisms: by decreasing perceived effort costs and/or by increasing VoPE. However, whether Learned Industriousness also increases VoPE remains unclear, as both theoretical and empirical work have predominantly focused on reductions in effort costs (31–34) rather than potential increases in VoPE (20).” (p. 5)

“Thus, while previous research has begun to explore near-transfer effects in decision-making (31), it remains largely unclear whether regular physical effort alters how effort is valued (rather than its costs) during actual exertion, whether such changes generalize across tasks, and how they depend on exercise intensity (20).” (p. 7)

We agree that the impact of training on the value of physical effort may vary across individuals as a function of prior sporting experience, motivational traits and other individual differences. As our study focuses on group-level effects, we now explicitly acknowledge this in the discussion and note that future work is needed to examine such moderating influences. (see our answer to your comment 9e)

“Another possible explanation lies in the developmental sensitivity of learning VoPE (20). Adolescents appear to be less sensitive to effort costs and exert more effort relative to task demands and rewards at stake than adults (78,79). This period of heightened responsiveness may facilitate the learning of the relationship between effort and reward, making it easier to internalize the contingency that greater effort leads to greater rewards. By contrast, by the time individuals reach early adulthood, as in the current sample, it may be more challenging to relearn or modify their VoPE. While this observation opens an interesting avenue for developmental research, it is also important to consider potential moderating factors such as previous learning experiences in the sport context, or genetic predispositions (e.g., differences in the dopaminergic reward system) that may influence how VoPE is learned. Future research should therefore test similar interventions with adolescent samples and systematically examine individual differences that may shape sensitivity to learning VoPE.” (pp. 37-38)

This is an important comment, and we thank the reviewer for raising this point. In response, we have revised the Introduction to explicitly note that different exercise intensities may engage distinct neurophysiological mechanisms, particularly with respect to the endogenous opioid and endocannabinoid systems. These intensity-related differences may, in turn, influence the valuation of physical effort, thereby strengthening the rationale for considering high-intensity exercise (see also Reviewer #4, comment 1).

“In Bernacer et al. (31), initially inactive participants completed a three-month low-intensity exercise program, after which effort costs had less influence on effort-based decisions, accompanied by decreased effort-related activity in the anterior cingulate cortex (ACC) (31). These findings suggest that regular exercise may reduce effort costs and alter neural responses to effort (31). However, as effort intensity was low and higher intensities are often valued less (26) and perceived as more aversive (39–41), it remains unclear whether training at higher intensities, might yield stronger effects on effort valuation. Notably, high-intensity exercise may engages additional neurophysiological mechanisms, such as increased activation of the endogenous opioid and endocannabinoid systems, which have been linked to affective responses and reward processing during intense exertion (39,40). Thus, while previous research has begun to explore near-transfer effects in decision-making (31), it remains largely unclear whether regular physical effort alters how effort is valued (rather than its costs) during actual exertion, whether such changes generalize across tasks, and how they depend on exercise intensity (20).” (pp. 6-7)

Thank you for this important comment. We have revised the Discussion to explicitly acknowledge that evidence for transfer effects in the domain of physical effort is considerably more limited than in cognitive effort research. In particular, we now highlight that far-transfer effects are more difficult to demonstrate for physical effort and that learning mechanisms in the context of physical activity tend to be more task- and context-specific.

“Importantly, evidence for transfer effects in the domain of physical effort is considerably more limited and less consistent than in cognitive effort research, with learning effects often appearing to be task- and context-specific (75). Although previous studies have demonstrated both near- and far-transfer effects, like reduced perceived physical effort costs and increased industriousness, in both animals (76) and humans (31), the conditions under which such transfer effects occur remain poorly understood.“ (p. 34)

Thank you for highlighting this important conceptual and methodological concern. We have revised the Discussion and the interpretation of our results now explicitly takes these limitations into account. In line with your suggestions, we now clarify that the assignment of roles to specific brain regions represents a simplified framework, rather than definitive functional localization. We further emphasize that this simplification is useful at the current stage of research but should be interpreted with caution when discussing our findings.

Discussion: “Finally, the current study assessed neural activation during periods of (partially) intense physical exertion, which offers valuable ecological validity. However, physical exercise involves multiple concurrent cognitive processes (80,81), which may obscure neural mechanisms specific to VoPE. Moreover, interpreting activation in specific brain regions necessarily relies on simplified heuristic functional attributions, which entails the risk of oversimplification and reverse inference. While this approach was useful for addressing the present research question at the current stage of the literature, it may have constrained our ability to draw clearer inferences, as network neuroscience emphasizes that functions likely emerge from interactions rather than one-to-one map

Attachment

Submitted filename: Response to Reviewers.docx

pone.0352897.s002.docx^{(62.3KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0352897.r003

Decision Letter 1

Christopher Kirk, Christopher Kirk

6 Mar 2026

-->PONE-D-25-42417R1-->-->An 8-week jump training did not boost effort value or the willingness to exert effort in a student sample-->-->PLOS One

Dear Dr. Stähler,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.-->--> -->-->Please submit your revised manuscript by Apr 20 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Christopher Kirk, PhD

Academic Editor

PLOS One

Journal Requirements:

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Additional Editor Comments:

Two reviewers are asking for relatively simple amendements to the structure of the manuscript, and specific details regarding the interpretation of the reported effect sizes. The third reviewer has highlighted some more detailed requirements about how of the variables and measurement methods have been discussed and interpreted.

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Reviewer #3: All comments have been addressed

Reviewer #4: (No Response)

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Reviewer #1: (No Response)

Reviewer #3: Yes

Reviewer #4: Partly

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Reviewer #1: (No Response)

Reviewer #3: Yes

Reviewer #4: No

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-->4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: (No Response)

Reviewer #3: Yes

Reviewer #4: Yes

**********

-->5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: (No Response)

Reviewer #3: Yes

Reviewer #4: Yes

**********

-->6. Review Comments to the Author

Reviewer #1: In the section on statistical analyses, the authors state that ‘Effect sizes were calculated to quantify the strength of any observed effects using Cohen’s d along with corresponding 95% confidence intervals’. However, they do not specify which effect size magnitudes these calculations refer to, nor which interpretative thresholds were applied. This constitutes a methodological shortcoming, as there exists a broad spectrum of effect size magnitudes, and without explicit criteria it is unclear how the authors interpret their results. For this reason, I suggested providing concrete thresholds. Specifically, for Cohen’s d, the following cut-offs should be used: 0.1, 0.4, and 0.8, respectively, with the corresponding citation (DOI: 10.1016/j.apmr.2025.05.013). Additionally, for ANOVA, the following thresholds should be reported: Small: W ≥ 0.1, Medium: W ≥ 0.3, Large: W ≥ 0.5, with appropriate citation (DOI: 10.11613/BM.2026.010101). According to these guidelines, the analysis should have been conducted accordingly; however, this information is still absent from the manuscript. Yours sincerely

Reviewer #3: I appreciate the effort the authors have made in revising the manuscript. The text is now considerably clearer and more pleasant to read.

In this review, I will focus primarily on the revisions made in response to my initial comments, as I recognize that several additional modifications were implemented to address remarks from other reviewers.

Introduction: Although the section has been substantially reduced, the inclusion of subtopics within the introduction is not standard practice. I recommend restructuring this section to present a more direct and straightforward justification of the study.

Objectives: The objectives are currently presented as a justification for the study rather than as clear statements. I suggest rewriting them in an affirmative and explicit manner.

Conclusions: The conclusions refer only to VoPE. It would be appropriate to also incorporate the other variables examined in the study to provide a more comprehensive summary of the findings.

References: Approximately 87 references appear excessive for a single article. I recommend reducing the number to around 60, ensuring that only the most relevant and essential sources are retained.

Reviewer #4: Dear Authors,

Thanks to you, your work has been revised. The manuscript is improved; however, there are still several critical points that should be corrected to be published.

1. fNIRS Data Robustness (High Priority) you failed to perform the sensitivity analysis with more stringent motor-correction parameters. Signal quality aspects such as percent rejected channels, SNR or channel acceptance rates were not reported. Findings of neural nulls are not explained well in the light of potential motion artifact.

Required: Conduct more severe settings sensitivity analysis, report signal quality metrics, and talk more about motion related limitations.

2. Multiple Comparisons (High Priority) You compared a large number of behavioral and neural outcomes and at various levels and times. You have not mentioned whether you have made adjustments to several comparisons. The risk management of Type I errors is not clear.

Note: indicate whether you used multiplicity corrections. Otherwise, justify and distinguish between confirmatory and exploratory analysis.

3. Baseline Transparency (High Priority) Your ANCOVA, but no CONSORT-type baseline equivalence table. Group comparability on demographics and baseline variables were not clearly reported.

Necessary: Provide a detailed and presented Table 1 with a demographic and baseline comparison, statistical tests, and effect sizes.

4. VoPE Stability (Medium Priority) Conclusions may be excessive. Boundary conditions are not clearly spelt out.

Limitations: Conclusions may be made only pertaining to: High-intensity, non-reinforced training, Young, low-active adults, Concurrent measurement paradigm.

5. Limitations of Moderation Analysis (Medium Priority) You failed to perform subgroup analyses or moderation. It is a limitation that is brought up but not emphasized.

Additional: Add a limitation paragraph explaining clearly why moderation could not be done, why (e.g., VO 2max, prior activity, effort tolerance) should be measured in the future studies.

**********

-->7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.-->

Reviewer #1: No

Reviewer #3: No

Reviewer #4: No

**********

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PLoS One. 2026 Jul 16;21(7):e0352897. doi: 10.1371/journal.pone.0352897.r004

Author response to Decision Letter 2

12 Apr 2026

Point-by-point reply

Manuscript number: #PONE-D-25-42417

Article title: An 8-week jump training did not boost effort value or the willingness to exert effort in a student sample

--------------------------------------------- comments Christopher Kirk ----------------------------------------

Dear XXX,

Please submit your revised manuscript by Apr 20 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Christopher Kirk, PhD

Academic Editor

PLOS One

Journal Requirements:

Additional Editor Comments:

Two reviewers are asking for relatively simple amendments to the structure of the manuscript, and specific details regarding the interpretation of the reported effect sizes. The third reviewer has highlighted some more detailed requirements about how of the variables and measurement methods have been discussed and interpreted.

We sincerely thank you for your continued constructive and helpful feedback. We have revised the manuscript accordingly and improved the overall structure of the Introduction to enhance clarity and readability. In addition, we now report and more carefully interpret effect sizes.

Furthermore, we reprocessed the fNIRS data using an alternative preprocessing pipeline to assess the robustness of our findings. The results remained consistent, supporting the stability of our results. We have also strengthened the discussion by placing the findings more clearly within the broader context of the study.

Lastly, we reviewed the abstract to make it clearer, remove redundancies, and improve readability.

“Physical inactivity remains highly prevalent, partly driven by the aversive nature of effort. However, effort can also be experienced as rewarding, which is associated with greater overall physical activity. Accordingly, the present study investigates whether regular physical exercise alters the self-reported value of physical effort, neural activation during exercise, and the willingness to exert effort. Sixty-two young adults were assigned to either an eight-week high-intensity jump training or a control group. Participants completed a two-task cycling ergometer exercise before and after the intervention. The first task assessed the value of effort at three pre-determined intensity levels, whereas the second task allowed participants to self-select the intensity to measure their willingness to exert effort. Participants’ perceived exertion and state value of physical effort were assessed, along with neural activation in the ventromedial prefrontal cortex and pre-supplementary motor area using functional near-infrared spectroscopy. Bayesian analyses provided evidence against main effects of condition, time, as well as their interaction, for self-reported value of physical effort, willingness to exert effort, and neural activity. This suggests that the value of effort may be relatively stable, as an eight-week training intervention did not alter the value of physical effort or the willingness to exert effort.” (p. 3)

We believe that these revisions have again improved the manuscript and appreciate the opportunity to refine our work.

-------------------------------------------------- comments Reviewer #1 ----------------------------------------

In the section on statistical analyses, the authors state that ‘Effect sizes were calculated to quantify the strength of any observed effects using Cohen’s d along with corresponding 95% confidence intervals’. However, they do not specify which effect size magnitudes these calculations refer to, nor which interpretative thresholds were applied. This constitutes a methodological shortcoming, as there exists a broad spectrum of effect size magnitudes, and without explicit criteria it is unclear how the authors interpret their results. For this reason, I suggested providing concrete thresholds. Specifically, for Cohen’s d, the following cut-offs should be used: 0.1, 0.4, and 0.8, respectively, with the corresponding citation (DOI: 10.1016/j.apmr.2025.05.013). Additionally, for ANOVA, the following thresholds should be reported: Small: W ≥ 0.1, Medium: W ≥ 0.3, Large: W ≥ 0.5, with appropriate citation (DOI: 10.11613/BM.2026.010101). According to these guidelines, the analysis should have been conducted accordingly; however, this information is still absent from the manuscript. Yours sincerely

We thank the reviewer for this suggestion. However, we followed the conventional thresholds for Cohen’s d (0.2 = small, 0.5 = medium, 0.8 = large) as originally proposed by Cohen (1988), which remain the most widely used and broadly accepted benchmark across disciplines.

For ANOVA effect sizes, we used ω² instead of Kendall’s W, which is more appropriate for variance-based effect size estimation in mixed ANOVA designs.

“In addition to Bayesian analyses, effect sizes were calculated to quantify the magnitude of observed effects. For ANOVA effects, ω² was reported (0.01 = small, 0.06 = medium, 0.14 = large) (50). For pairwise comparisons, Cohen’s d was reported (0.2 = small, 0.5 = medium, 0.8 = large), along with corresponding 95% confidence intervals (50).” (p. 21)

Results – see for example:

“The best model including the Time x Condition interaction, also included main effects of Time, Condition, and Interval, BF10 = 4.98, P(M/data) = .04, ω² = 0.017 (for complete results tables, see OSF), providing substantial evidence for the alternative hypothesis (51).” (p. 26)

------------------------------------------------ comments Reviewer #3 ------------------------------------------

I appreciate the effort the authors have made in revising the manuscript. The text is now considerably clearer and more pleasant to read.

Thank you very much for this helpful feedback. We have addressed the comments accordingly and believe that these revisions have further improved our manuscript.

1. Introduction: Although the section has been substantially reduced, the inclusion of subtopics within the introduction is not standard practice. I recommend restructuring this section to present a more direct and straightforward justification of the study.

Thank you for this remark. We have revised the introduction so that separate subtopics have been integrated into the main text. The section has also been streamlined and more clearly focused on the present study, providing a more direct justification of our research. (See pp. 4-11)

2. Objectives: The objectives are currently presented as a justification for the study rather than as clear statements. I suggest rewriting them in an affirmative and explicit manner.

Thank you for this helpful suggestion. The objectives are now presented more explicitly and in an affirmative manner. Additionally, the study aims are now stated more clearly to more directly outline the purpose of the study.

“The aim of the present study therefore was to examine whether regular physical exercise alters the value of physical effort during actual physical exertion. Specifically, we investigated whether changes in subjective, behavioral and neural indicators of effort valuation generalized beyond the training task.” (p. 9)

3. Conclusions: The conclusions refer only to VoPE. It would be appropriate to also incorporate the other variables examined in the study to provide a more comprehensive summary of the findings.

We appreciate this helpful suggestion and revised the conclusion to reflect the broader set of variables. In particular, we now explicitly refer to the behavioral and neural measures alongside VoPE to provide a more comprehensive summary of the study’s findings. (See also our response to Reviewer 4, comment #4)

“In summary, this study provides initial insights into whether regular physical exercise can influence the valuation of physical effort during exercise across behavioral, subjective, and neural levels. Contrary to our expectations, eight weeks of high-intensity jump training did not alter participants’ willingness to exert effort, their self-reported value of physical effort (VoPE), or neural responses in the vmPFC associated with effort valuation. Together, these findings suggest a relative stability of concurrent effort valuation in the context of the present training paradigm. Future research should therefore examine whether different training characteristics (e.g., training intensity), timing of VoPE assessment, the role of intrinsic and extrinsic rewards, or other populations (e.g., active individuals, adolescents), influence the modifiability of effort valuation.” (p. 41)

References: Approximately 87 references appear excessive for a single article. I recommend reducing the number to around 60, ensuring that only the most relevant and essential sources are retained.

Thank you for this suggestion. We have carefully reviewed the references and reduced the number of citations to 61 by removing less essential and redundant references while retaining the most relevant sources necessary to support the argumentation and theoretical background of the manuscript.

------------------------------------------------ comments Reviewer #4 ------------------------------------------

Thanks to you, your work has been revised. The manuscript is improved; however, there are still several critical points that should be corrected to be published.

Required: Conduct more severe settings sensitivity analysis, report signal quality metrics, and talk more about motion related limitations.

We thank the reviewer for this important and constructive comment. To assess robustness of the present results against different preprocessing choices, we preprocessed the data with another processing stream that has been used in prior research that employed fNIRS in the context of a physical effort task (see, doi: 10.1038/s41598-018-34009-2). This did not change our results. If the editor wishes, we would be happy to provide these additional analyses as supplementary material.

enPruneChannels function: remove channels when the signal was too weak or too strong

Intensity_to_OD: optical intensity converted to optical density

Wavelet_Motion_Correction: remove motion artifacts (IQR of 1).

low pass filter (0.5 Hz)

converted to oxy- and deoxyhemoglobin with the modified Beer-Lambert law

Furthermore, we have now included explicit reporting of signal quality metrics in the Method section:

“Overall, 6.3% of channels were rejected due to insufficient signal quality. Of these rejected channels, 29% were excluded automatically by the preprocessing stream, while the remaining 71% were identified and removed through manual inspection.” (p. 19)

Finally, we have expanded the Discussion to more explicitly address the potential impact of motion-related artifacts:

“Finally, the current study assessed neural activation during periods of (partially) intense physical exertion, which offers valuable ecological validity. However, although fNIRS is relatively robust to motion artifacts (56), it is important to note that motion-related noise cannot be fully excluded, particularly in the context of physical demanding tasks such as a cycling task. Despite careful preprocessing and quality control procedures, residual motion artifacts may have influenced the signal and could have contributed to the observed null findings. Importantly, however, no corresponding effects were observed at the behavioral or subjective level, suggesting that the absence of neural differences is consistent across multiple levels of analysis and not solely attributable to measurement noise.” (p. 39)

Note: indicate whether you used multiplicity corrections. Otherwise, justify and distinguish between confirmatory and exploratory analysis.

Thank you for this important comment. We agree that the large number of comparisons requires careful consideration and transparent reporting.

All primary analyses were conducted within a Bayesian framework, in which multiplicity corrections are not required (e.g., 10.1080/19345747.2011.618213), as Bayes factors quantify the relative evidence for each model given the observed data, rather than relying on a repeated-sampling logic that necessitates control of Type I error rates (10.3758/s13423-017-1343-3). Nevertheless, we acknowledge that conducting multiple analyses increases the risk of overinterpretation. To further strengthen the interpretability of our findings and guard against over-interpretation, we now explicitly label analyses as confirmatory or exploratory in the Methods section.

“For state VoPE and cerebral oxygenation (oxygenated (HbO) and de-oxygenated (HbR), hemoglobin concentration) of the interval task, mean values of the second minute of the ride were analyzed using the same 2-between (condition: TG, CG) x 3-within (intensity levels: low, moderate, vigorous) x 2-within (time: pre- and post-training) Bayesian repeated measures ANOVA design in JASP. For the free ride task, 2-between (condition: TG, CG) x 2-within (time: pre- and post-training) Bayesian repeated measures ANOVAs were conducted for heart rate (10-minute mean), RPE, rendered power (10-minute mean, normalized to the RCP), and state VoPE. All

Attachment

Submitted filename: Response_to_Reviewers_auresp_2.docx

pone.0352897.s003.docx^{(47.4KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0352897.r005

Decision Letter 2

Christopher Kirk, Christopher Kirk, Christopher Kirk

21 Apr 2026

-->PONE-D-25-42417R2-->-->An 8-week jump training did not boost effort value or the willingness to exert effort in a student sample-->-->PLOS One

Dear Dr. Stähler,

Two reviewers have provided realtively minor amendments to make for clarity and confirmation. The third reviewer has some more substantial edits required, please consider each accordingly.-->--> -->-->Please submit your revised manuscript by Jun 05 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:-->

A letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

As the corresponding author, your ORCID iD is verified in the submission system and will appear in the published article. PLOS supports the use of ORCID, and we encourage all coauthors to register for an ORCID iD and use it as well. Please encourage your coauthors to verify their ORCID iD within the submission system before final acceptance, as unverified ORCID iDs will not appear in the published article. Only the individual author can complete the verification step; PLOS staff cannot verify ORCID iDs on behalf of authors.

We look forward to receiving your revised manuscript.

Kind regards,

Christopher Kirk, PhD, FCASES

Academic Editor

PLOS One

Journal Requirements:

If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

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PLoS One. 2026 Jul 16;21(7):e0352897. doi: 10.1371/journal.pone.0352897.r006

Author response to Decision Letter 3

12 May 2026