# The Impact of Acute High-Intensity Activity on Perceptual Decision-Making Dynamics

Davranche Karen, Dorian Giraud, Arnaud Hays, Thibault Gajdos Preuss

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Davranche Karen, Dorian Giraud, Arnaud Hays, Thibault Gajdos Preuss. The Impact of Acute High-Intensity Activity on Perceptual Decision-Making Dynamics. Cognitive, Affective, and Behavioral Neuroscience, In press, Special Issue/Neuroscience of Effort, 10.1101/2023.02.14.528466. http://05358060

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<figure><img src="image_1.png" /></figure>
1 **The Impact of Acute High-Intensity Activity on Perceptual Decision-Making Dynamics**

2 Davranche Karen¹, Giraud Dorian²,³, Hays Arnaud², Gajdos Preuss Thibault¹

3 ¹Aix Marseille Univ, CNRS, CRPN, Marseille, France

4 ²Aix Marseille Univ, CNRS, Université de Toulon, Institut Paoli-Calmettes, HIPE

5 HumanLab, Marseille, France

6 ³Aix Marseille Univ, CNRS, IUSTI, Marseille, France

7 Corresponding author:

8 Karen Davranche, Aix-Marseille Université, 3 place Victor Hugo,

9 13331 Marseille Cedex 03, France

10 Phone: +33(0)4 13 55 11 35

11 E-mail: karen.davranche@univ-amu.fr

12 ORCID: 0000-0003-4348-1265

13

14 Copyright license: For the purpose of Open Access, a CC-BY public copyright license has been
15 applied by the authors to the present document and will be applied to all subsequent versions up to
16 the Author Accepted Manuscript arising from this submission.
17 Abstract

18 This study investigates the acute impact of high-intensity activity on perceptual decision-
making, using computational modeling to assess changes during and after physical activity.
19

20 Participants performed a two-alternative forced choice perceptual decision-making task at rest
21 (pre- and post-exercise) and during six of eight 5-minute cycling bouts (totaling 47 minutes)
22 under dual-task condition, while maintaining an average intensity of 86 ± 7% of their maximum
23 heart rate. Drift diffusion modeling was applied to accuracy and reaction time data to estimate
24 changes in evidence accumulation (drift rate), decision threshold (boundary separation), and non-
25 decision processes ($t_{er}$). Results revealed improved post-exercise performance, characterized by
26 shorter non-decision time, potentially reflecting a transient improvement in motor or perceptual
27 efficiency. During ongoing physical activity, results indicate that exercise is associated with a
28 decrease in non-decision time and an increase in the efficiency of evidence accumulation, while
29 response caution remains stable. These findings provide novel insights into how sustained high-
30 intensity exercise modulates perceptual decision-making dynamics under physiological stress.

31 Keywords: High-intensity exercise, Perceptual decision making, Drift diffusion model, Dual-task
32 performance, Non-decision processes.
33 **Introduction**

34 April 15, 2019. Notre-Dame is ablaze. After a strenuous climb, during which a firefighter 35 analyzes and interprets the situation, she finally reaches the top of the tower. Now, she must 36 decide which side of the raging fire to attack first. In such extreme conditions, is her decision- 37 making affected by sustained physical and mental demand? And if so, which specific processes 38 are altered during and after such intense exercise? These questions motivate our investigation into 39 how high-intensity exercise influences perceptual decision-making.

40 It is well established that acute moderate-intensity physical activity can enhance cognitive 41 performance across various cognitive tasks, including memory, vigilance, and conflict resolution 42 (e.g., McMorris, 2021; Pontifex et al., 2019). However, the effects of high-intensity aerobic 43 activity remain less clear. While some studies report impairments, others suggest null effects or 44 even improvements (McMorris, 2021; Moreau & Chou, 2019). Recent reviews (Sudo, Costello, 45 McMorris & Ando, 2022; Zheng et al., 2021; Pontifex et al., 2019) emphasize the need for more 46 research into this intensity range, as most existing findings focus on moderate (58% of the 47 literature) intensities of physical activity. This emphasis, often driven by methodological 48 concerns, has left a gap in our understanding of how demanding physical exertion affects 49 cognition.

50 High-intensity activity, typically defined as physiological demand exceeding 80%
51 maximal oxygen uptake or HRmax (McMorris et al., 2016), triggers more pronounced
52 physiological responses than conventional aerobic activity (Herold et al., 2019). These responses
53 include changes in circulating lactate, blood glucose, circulating cortisol levels, or neurotrophic
factors (Hashimoto et al., 2021; Singh & Staines, 2015), which may differentially modulate
54
cognitive function. Such changes can affect various neural processes, potentially enhancing
55
sensory encoding, increasing arousal, and modulating motor readiness.
56

Because cognitive processes cannot be directly observed, we used a computational
57
modeling approach to infer them from behavioral data. Specifically, we applied the Drift
58
Diffusion Model (DDM, Ratcliff, 1978) a well-validated model for analyzing two-alternative
59
forced-choice tasks (Ratcliff et al., 2016). The DDM assumes that decisions result from the
60
accumulation of noisy evidence until a response threshold is reached (Bogacz et al., 2006). The
61
basic DDM contains four key parameters, each related to a different cognitive process: (1) the
62
drift rate (v), reflecting the efficiency of evidence accumulation; (2) the decision threshold (a),
63
representing response caution; (3) the starting point (z), indicating decision bias; and (4) the non-
64
decision time ($t_{er}$), encompassing stimulus encoding and motor execution. Participants completed
65
a random dot motion task before, during, and after engaging in 5-minute cycling bouts at >80%
66
HRmax. Behavioral data (accuracy and reaction times, RT) were used to estimate model
67
parameters and track changes in cognitive processes across conditions.
68

## 69 **Materials and methods**

70 **Participants**

Nineteen young adults (one woman, age range: 18–34; mean age: 25 years) voluntarily
71
72 participated in the experiment. All participants were healthy and practiced regular physical
73
activities (peak oxygen uptake: 55 ± 8 mL kg⁻¹ min⁻¹; maximal heart rate: 184 ± 9 bpm). All
74
participants were fully informed about the study and signed a written informed consent before
inclusion. The study was approved by the Ethics Committee for Research in Science and
Technology of Physical Sports Activities (IRB00012476-2022-21-01-148).

Random dot motion task (RDK)

We used a perceptual decision-making task, the random dot motion task (RDK), for
which the DDM has consistently provided excellent accounts of behavioral data. The experiment
was programmed in Python, utilizing components of the PsychoPy toolbox (Peirce et al., 2019),
and was executed on a PC computer operating natively on Windows 10 with a 19.5-inch screen.
Participant responses were recorded using two buttons (one for each hand).

The task requires participants to determine the global direction of a random dot
kinematogram (RDK) featuring a proportion of dots moving coherently in the left or right signal
direction. Each trial started with the presentation of the random dot motion stimulus, which
remained on the screen until the participant responded. A trial is correct if the participant report
the direction of coherently moving dots. A response time deadline was set to 5 s. The interval
between the response to the stimulus and the next trial was 1.5 s. The coherence parameter
(proportion of dots moving in the same direction) was set for each participant at the beginning of
the experiment according to a 2 up and 1 down staircase method lasting 30 trials, which
corresponds to an accuracy close to 70%. This participant-specific adjustment of task difficulty
helps eliminate potential confounding factors arising from inter-individual differences in
task performance, thereby making it easier to interpret the model parameters—and, in
turn, the effects of the time window on these parameters. White dots were presented within a
virtual 12.6° circular aperture centered on a 24.8° 3 13.9° black field. Each dot was a 4 x 4 pixel
(0.05° square), moving at a speed of 8°/s. Dot density was fixed at 16.7 dots/deg2/s. Random dot motion was controlled by a white noise algorithm (Pilly & Seitz, 2009).

## Procedure

Participants made three separate visits to the laboratory, each spaced at least 48 hours apart: a characterization session, a familiarization session, and an experimental session (figure 1).

Participants were positioned on an upright cycle ergometer (Lode Excalibur Sport, Groningen, The Netherlands), equipped with two thumb response buttons on the right and left handle grips. A computer screen was positioned at eye level, approximately 1 meter in front of the participant.

<figure><img src="image_2.png" /></figure>

104

105 **Figure 1.** Overview of the experimental design and task structure. The study included three

106 laboratory visits: (1) a characterization session to assess individual physiological parameters

107 and calibrate the difficulty of the cognitive task, (2) a familiarization session, identical in all
108 respects to the experimental session, aimed at stabilizing performance, and (3) an experimental
109 session combining 5-minute cycling bouts at >80% HRmax with (in grey) or without (in white)
110 cognitive testing. Cognitive performance was assessed at rest for 5 minutes before (PRE) and
111 immediately after exercise (POST), and during exercise at three-time windows: Beginning
112 (minutes 1–5, B1), Middle (minutes 19–30, B4–B5), and End (minutes 37–48, B7–B8) of the
113 physical exercise. A schematic representation of the random dot motion task (RDK) is shown in
114 the top right corner of the figure.

115 The preliminary session was conducted to individually adjust the difficulty of the
116 cognitive task, assess VO2max, and determine ventilatory thresholds (Wasserman, 2012) (see
117 table 1 for details). Participants were seated on the cycle ergometer, fitted with a facemask to
118 measure gas exchange data (Innocor CO, Cosmed, Italia) and a heart rate monitor. The
119 incremental cycling activity to exhaustion test started at an initial power level defined according
120 to the participant's level (100 watts for non-cyclists and 150 watts for cyclists) and increased by
121 30 watts every 2 minutes until exhaustion. The end of the test was determined by volitional
122 cessation of exercise or failure to maintain pedal cadence above 60 rpm despite strong verbal
123 encouragement. During the first step of this maximal test, 30 trials of the RDK task were
124 performed to set up the consistency parameter according to a staircase procedure.
<table><caption>Mean ± standard deviation</caption><thead><tr><th>Variables</th><th>All</th></tr></thead><tbody><tr><td>Sample size</td><td>19</td></tr><tr><td>Age (years)</td><td>25±5</td></tr><tr><td>Height (cm)</td><td>177±7</td></tr><tr><td>Weight (kg)</td><td>67±9</td></tr><tr><td>Fat mass (%)</td><td>15±4</td></tr><tr><td>VO<sub>2</sub>max (ml.kg<sup>-1</sup>.min<sup>-1</sup>)</td><td>55±8</td></tr><tr><td>Heart rate max (bpm)</td><td>184±9</td></tr><tr><td>Maximal aerobic power (W)</td><td>295±58</td></tr><tr><td>P<sub>SV1</sub> (W)</td><td>208±55</td></tr><tr><td>P<sub>SV2</sub> (W)</td><td>254±52</td></tr><tr><td>Heart rate HIE (% max)</td><td>86±6</td></tr><tr><td>Central temperature HIE (°C)</td><td>38,2±0,4</td></tr></tbody></table>

125

126

127

128

129

130

131

**Table 1.** Anthropometric and physiological characteristics of participants. Note. P<sub>SV</sub>: Power corresponding to the first SV1 and second SV2 ventilatory thresholds, HIE: high-intensity exercise, VO<sub>2</sub>max: maximal oxygen consumption.

132

133

134

135

Before the experimental session, a familiarization session (about 1,250 trials), identical in all respects to the experimental session, was conducted to stabilize performance and control for potential learning effects, as well as to assess the physiological load of the dual task performed while cycling. The data of the familiarization session were not included in the analyses.

136

137

138

139

140

141

142

143

The experimental protocol involved high-intensity interval activity, a discontinuous mode of endurance exercise characterized by short bouts of high-intensity workloads interspersed with periods of rest. Participants completed a perceptual decision-making task at rest both before and after eight 5-minute bouts of cycling. Each 5-minute cycling bout was followed by a 1-minute rest period, resulting in a total session duration of 47 minutes. During six of the eight cycling bouts, participants performed the perceptual decision-making task while cycling at a workload fixed at 90% of SV1—an intensity determined during the incremental test of the preliminary session—placing them in a dual-task situation involving both cognitive and physical activity.
Cardiorespiratory parameters were continuously recorded, and capillary blood samples from the
fingertip were collected before and immediately after each of the 5-minute cycling bouts for the
measurement of lactate and glucose levels. The skin was prepared by cleaning with alcohol,
allowed to dry, and then punctured with an automated lancet. Blood samples were analyzed using
a Biosen blood analyzer (EKF Diagnostics, UK).

To examine the time-dependent effects of exercise on cognitive processes, the dual-task
period was segmented into three time windows: the Beginning phase (B1), corresponding to the
first 5 minutes of the physical activity; the Middle phase, spanning minutes 19 to 30 (B4-B5); and
the End phase, encompassing minutes 37 to 48 (B7-B8). This segmentation was based on the
expected temporal dynamics of physiological responses during sustained intermittent cycling.

The Beginning of exercise (B1) was chosen as a baseline, as it corresponds to the early phase of
exertion when physiological changes (e.g., blood lactate accumulation, cardiovascular strain)
have not yet fully developed, allowing us to capture the initial dual-task condition under minimal
metabolic load. The Middle window reflects a phase where physiological responses typically
plateau (e.g., elevated but stabilized lactate and heart rate), while the End window captures
potential cumulative effects of prolonged exertion and fatigue. This time window approach
enables us to better track the evolving impact of physical exercise on decision-making processes
over time.

## Data Analyses

The experiment was designed to evaluate the exercise-induced effects on cognitive
processes throughout high-intensity activity and immediately afterward exercise bout. Due to the
165 interplay between physical demands and cognitive performance in the dual-task condition,
166 cognitive data recorded at rest (pre- and post-exercise, 4916 trials) and during the dual-task
167 condition (blocks while cycling, 12402 trials) were analyzed separately. During the physical
168 activity, we anticipated that the impacts on decision-making processes may vary according to
169 time spent on task. To capture the time-specific effects of exercise on cognitive performance, we
170 conducted analyses across three specific time windows.

171 Anticipations (RTs < 200 ms; 0.13 %) were discarded from all analyses. Five subjects did
172 not perform the perceptual task appropriately (i.e., exhibiting a percentage of accuracy below
173 50% or employing a stereotyped response strategy). They were excluded from the initial sample
174 of 24 participants. Regarding physiological measures, we analyzed glucose and lactate levels
175 within linear mixed-effects models. These models included the ‘sample’ (PRE and B1 to B8)
176 expressed in mmol/L as a fixed effect, with ‘subject’ as random effect.

177 The parameters of the Drift Diffusion Model (DDM) were estimated using a hierarchical
178 Bayesian approach implemented with the HSSM toolbox in Python (Fengler et al., 2025). Two
179 separate sets of model fits were conducted: one comparing the PRE and POST time windows,
180 and another comparing the Beginning, Middle, and End phases of exercise. In both cases, the
181 fitting procedure was identical. We evaluated eight versions of a DDM without inter-trial
182 variability. The first was a "basic" model, in which all parameters—starting point (z), drift rate
183 (v), threshold (a), and non-decision time (ter)—were held constant across blocks. The “full”
184 model allows all parameters, except the starting point, to vary across time windows. The
185 remaining six models each allowed different combinations of these parameters to vary across
186 time windows. For example, the "vt" model permits both the drift rate (v) and non-decision time

187 ($t_{er}$) to vary across time windows, and is formally defined in HSSM as:

188 $v \sim 1 + \text{time\_window} + (1 + \text{time\_window} | \text{subject})$

189 $z \sim 1 + (1 | \text{subject})$

190 $a \sim 1 + (1 | \text{subject})$

191 $t_{er} \sim 1 + \text{time\_window} + (1 + \text{time\_window} | \text{subject})$

192 For all models, we used the default priors provided by HSSM. We then assessed
193 convergence by visually inspecting the Markov Chain Monte Carlo (MCMC) traces and by
194 examining the R-hat statistic from Brooks and Gelman (1998). Model comparison was carried
195 out using the Bayesian Information Criterion (BIC). Subsequently, we evaluated model fits by
196 visually comparing the best-fitting model to the basic model and analyzed the parameter
197 estimates of the best model. This procedure provided two complementary approaches to assess
198 the influence of experimental blocks on the parameters: (i) identifying which model structure best
199 captures the data and (ii) examining the extent to which parameter estimates depend on time
200 windows.

201 **Results**

202 *Physiological measures*

203 **Heart rate.** The linear mixed model revealed a significant effect of block on heart rate (F(7, 133) = 16.83, p < .001). Post-hoc comparisons showed that heart rate at block B1 was significantly
lower than at all subsequent blocks (B3 to B8; all p < 0.001), while the difference between B1 and B2 was not significant (p = 0.44). Heart rate at B2 was also significantly lower than at blocks B4 to B8 (p < 0.05), but not significantly different from B3 (p = 0.21; see table 2 for means and standard deviation). No significant differences were found between any other block pairs from B3 to B8, suggesting that heart rate stabilized after the second or third block (see appendix S1 for individual trajectories across the protocol).

**Blood Biomarkers.** Linear mixed-effects models revealed a significant effect of exercise on both blood glucose (F(8, 127) = 5.76, p < 0.001) and blood lactate levels (F(8, 127) = 5.76, p < 0.001), measured at the end of each 5-minute cycling bout (table 2 and Supplementary Figures S1–S3). Post-hoc comparisons showed that glucose concentrations were significantly higher at the pre-exercise baseline compared to all subsequent time points (B1 to B8; all p < 0.01), after which values remained relatively stable. In contrast, lactate concentrations were significantly lower at the pre-exercise baseline than at all subsequent time points (B1 to B8; all p < 0.01), indicating a sharp increase after exercise onset followed by a plateau.

<table><thead><tr><th></th><th>PRE</th><th>B1</th><th>B2</th><th>B3</th><th>B4</th><th>B5</th><th>B6</th><th>B7</th><th>B8</th></tr></thead><tbody><tr><td>Lactate (mmol/L)</td><td>1.63 (0.53)</td><td>5.92 (2.24)</td><td>5.51 (2.64)</td><td>4.92 (2.61)</td><td>4.56 (2.55)</td><td>4.20 (2.32)</td><td>4.28 (2.56)</td><td>4.17 (2.68)</td><td>3.92 (2.81)</td></tr><tr><td>Glucose (mmol/L)</td><td>4.79 (0.75)</td><td>4.04 (0.51)</td><td>3.89 (0.45)</td><td>4.14 (0.82)</td><td>3.94 (0.80)</td><td>4.06 (0.67)</td><td>4.21 (0.58)</td><td>4.17 (0.52)</td><td>4.18 (0.90)</td></tr><tr><td>HR (bpm)</td><td></td><td>151.07 (16.11)</td><td>153.54 (16.09)</td><td>156.28 (15.89)</td><td>156.79 (15.94)</td><td>157.41 (15.94)</td><td>159.34 (16.23)</td><td>158.54 (16.53)</td><td>159.36 (17.03)</td></tr><tr><td>%HRmax</td><td></td><td>81.82 (8.06)</td><td>83.23 (7.99)</td><td>84.51 (7.86)</td><td>85.04 (7.95)</td><td>85.61 (8.24)</td><td>86.79 (8.26)</td><td>86.47 (8.60)</td><td>86.94 (8.83)</td></tr></tbody></table>
**Table 2.** Mean values of glucose, lactate, heart rate (HR), and percentage of maximum heart rate (%HRmax) across bouts of high-intensity exercise. Note. Values represent the mean (standard deviation) for each bout.

## Computational modeling results

**Pre- and Post-exercise Effects.** BIC model comparison shows that models that allow non-decision time ($t_{er}$) to vary between PRE and POST time windows clearly dominate those who don't (table 3). There are, however, little differences between the models that allow $t_{er}$ to vary across time windows, which suggest that threshold and drift variations do not contribute to improve fits.

<table><thead><tr><th>Model</th><th>BIC</th></tr></thead><tbody><tr><td>at</td><td>3585</td></tr><tr><td>vt</td><td>3590</td></tr><tr><td>full</td><td>3593</td></tr><tr><td>t<sub>er</sub></td><td>3602</td></tr><tr><td>a</td><td>3730</td></tr><tr><td>av</td><td>3733</td></tr><tr><td>v</td><td>3932</td></tr><tr><td>basic</td><td>4044</td></tr></tbody></table>

**Table 3.** Bayesian Information Criterion (BIC) for each model. In the “basic” model, all parameters—starting point (z), drift rate (v), threshold (a), and non-decision time ($t_{er}$)—were held constant across PRE and POST time windows. The “full” model allows all parameters, except the starting point, to vary across time windows. The remaining six models each allowed different combinations of these parameters to vary across time windows. For example, the "vt" model permits both the drift rate (v) and non-decision time ($t_{er}$) to vary across time windows.
However, quantile probability plots suggest that letting the threshold *a* and the non-decision time *t*<sub>er</sub> vary across time windows does not dramatically improve the fit quality compared to the basic model (Figure 2 and Supplementary Figure S6).

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

**Figure 2.** Quantile probability functions averaged across subjects for before (PRE, in blue) and after (POST, in purple) exercise, constructed by plotting RT quantiles (.1, .3, .5, .7, .9; y-axis) of the distributions of correct and incorrect responses against the corresponding response type proportion (x-axis). Dots represent observed data, “+” represent predictions of the basic model, and “x” represent predictions of the at model (using in both cases best fitting parameters).

Dotted ellipses around the crosses represent 95% confidence intervals, reflecting the expected variability arising from model stochasticity and parameter estimation uncertainty.

The results of the fit of the best model (*at* model) confirm this assessment (figure 3; see Table S2 in appendix for a full summary of the results). We found that 82% of the posterior for the boundary parameter (*a*) difference between POST and PRE time windows was below 0, with a very small mean effect (M = -0.033, 94% HDI: [-0.099, 0.42]) compared to the average value
249 across time windows (M = 0.979, 94% HDI: [0.839, 1.114]). While the effect on the non-decision time (t<sub>er</sub>) is more significant, with 94% of the posterior between POST and PRE time windows 250 below 0, its amplitude is small (M = -0.032, 94% HDI: [-0.069, 0.008]) compared to the average 251 value across time windows (M = 0.50, 94% HDI: [0.43, 0.58]). 252

<figure><img src="image_4.png" /></figure>

253

<caption>Figure 3. Posteriors for the boundary (a, left panel) and non-decision time (t<sub>er</sub>, right panel) parameters difference between POST and PRE time windows.</caption>

255

256 **During-effort Effects.** BIC model comparison shows that the model that allow drift rate (v) and 257 non-decision time (t<sub>er</sub>) to vary across time windows is clearly the better model among those we 258 compared (table 4).

<table><thead><tr><td>Model</td><td>BIC</td></tr></thead><tbody><tr><td>vt</td><td>9655</td></tr><tr><td>full</td><td>9682</td></tr><tr><td>at</td><td>9710</td></tr><tr><td>t<sub>er</sub></td><td>9721</td></tr><tr><td>av</td><td>9824</td></tr><tr><td>a</td><td>9851</td></tr><tr><td>v</td><td>9970</td></tr><tr><td>basic</td><td>10182</td></tr></tbody></table>
**Table 4.** Bayesian Information Criterion (BIC) for each model. In the “basic” model, all parameters—starting point (z), drift rate (v), threshold (a), and non-decision time ($t_{er}$)—were held constant across time windows. The “full” model allows all parameters, except the starting point, to vary across time windows. The remaining six models each allowed different combinations of these parameters to vary across time windows. For example, the "vt" model permits both the drift rate (v) and non-decision time ($t_{er}$) to vary across time windows.

Visual inspection of quantile probability plots confirm that letting the non-decision time ($t_{er}$) and drift rate (v) vary across time windows improve the fit quality (figure 4 and Supplementary Figure S7).

<figure><img src="image_5.png" /></figure>

**Figure 4.** Quantile probability functions averaged across subjects at the Beginning (in orange), Middle (in green) and End (in red) of exercise, constructed by plotting RT quantiles (.1, .3, .5, .7, .9; y-axis) of the distributions of correct and incorrect responses against the corresponding response type proportion (x-axis). Dots represent observed data, “+” represent predictions of the basic model, and “x” represent predictions of the vt model (using in both cases best fitting
parameters). Dotted ellipses around the crosses represent 95% confidence intervals, reflecting
the expected variability arising from model stochasticity and parameter estimation uncertainty.

The results of the fit of the best model (vt model) confirm this assessment (figure 5; see
Table S3 in appendix for a full summary of the results). We found that the drift rate (v) likely
increases from the Beginning to the Middle time windows (94% of the posterior for the
difference above 0; figure 5, left-top), with a size effect about 7% (M = 0.097, 94% HDI :
[-0.017, 0.215]) compared to the average value across time windows (M = 1.427, 94% HDI :
[1.037, 1.861]). There is, however, no evidence for an effect between the Middle and End time
windows (61% of the posterior for the difference above 0; figure 5, left-middle). The non-
decision time (t<sub>er</sub>) likely decreases between the Beginning and the End of exercise (97.5% of the
posterior for the difference below 0; figure 5, right-bottom), with a size effect about 8% (M = -
0.041, 94% HDI : [-0.070, -0.004]) compared to the average value across time windows (M =
0.492, 94% HDI : [0.421, 0.563]). This result suggests an acceleration of perceptual-motor
processes or motor facilitation induced by physical exertion. No clear difference was observed
between the beginning and the Middle of exercise (81% of the posterior for the difference below
0; figure 5, right-top), nor between the Middle and end of exercise (73% of the posterior for the
difference below 0; figure 5, right-middle), suggesting a stabilization of this effect.
<figure><img src="image_6.png" /><figcaption>**Figure 5.** Posteriors for the drift rate ($v$, left panel) and non-decision time ($t_{er}$, right panel) parameters difference across time windows.</figcaption></figure>

## Discussion

### Summary

The present study aimed to investigate the impact of high-intensity activity on cognitive processes involved in perceptual decision-making. To this end, participants completed a perceptual decision task at rest (pre- and post-exercise), as well as six times during exercise in a
301 dual-task condition involving both cognitive and physical activity. Cognitive processes were
302 assessed by fitting a Drift Diffusion Model (DDM) to behavioral data collected across these time
303 points. The transition from pre- to post-exercise conditions was associated with a possible
304 reduction in non-decision time ($t_{er}$), potentially reflecting a transient improvement in motor or
305 perceptual efficiency. During ongoing physical activity, results indicate a decrease in non-
306 decision time ($t_{er}$) and an increase in the efficiency of evidence accumulation (drift rate, $v$), while
307 response caution (boundary separation) remains stable.

308 *Post-effort modulation of cognitive processes.*

309 Findings indicate that transitioning from the Pre- to Post-exercise condition does not induce
310 robust changes in decision-making parameters modeled by the DDM. However, a possible
311 reduction in non-decision time ($t_{er}$) was observed, which may reflect transient enhancements in
312 sensory or motor efficiency. Bayesian model comparisons support a potential effect of exercise
313 on non-decision components, with no consistent change core decision parameters. This suggests
314 that exercise does not significantly impact perceptual discrimination processes, as measured by
315 drift rate ($v$) or boundary separation ($a$).

316 This interpretation is consistent with prior evidence that cognitive benefits can persist
317 briefly after the cessation of intense exercise (Tsukamoto et al., 2016a, 2016b; Moreau & Chou,
318 2019; McMorris et al., 2016), potentially due to sustained neurochemical or metabolic effects.
319 Notably, elevated blood lactate has been shown to enhance motor system reactivity and sensory
320 perception (Coco et al., 2009; 2016), possibly contributing to reduced $t_{er}$. Increased cerebral
321 blood flow and oxygenation following exercise may also support cognitive benefits (Rooks et al.,
322 2010). Furthermore, catecholamine metabolism and the neuromodulatory role of lactate—
323 produced even under fully aerobic conditions—warrant further exploration as potential mediators
324 of the post-effort cognitive modulation.

325 *Ongoing physical activity impact on cognitive processes.*

326 Bayesian model comparisons suggest that sustained exercise is associated with a decrease
327 in non-decision time ($t_{er}$) and an increase in the efficiency of evidence accumulation (drift rate,
328 $v$), while response caution (boundary separation, a) remains stable.

329 Drift rate analyses revealed a progressive increase in evidence accumulation from the
330 Beginning to the Middle phase of exercise (minutes 19 to 30), followed by a plateau during the
331 End phase (minutes 37 to 48), indicating changes in perceptual discrimination across exercise
332 duration. In parallel, $t_{er}$ decreased from the beginning to the end of exercise, particularly between
333 the early and middle phases, indicating a facilitation of perceptual-motor processes. The initial
334 elevation in $t_{er}$ may reflect the time required for physiological adaptations, while the later
335 stabilization could result from improved lactate clearance and a metabolic shift from
336 carbohydrate to fat oxidation (Herold et al., 2022; Hashimoto et al., 2018; Tsukamoto et al.,
337 2016b).

338 These results partially align with previous research demonstrating the influence of
339 physiological states on cognitive functioning. For instance, Ratcliff & Van Dongen (2011),
340 showed that reduced vigilance state induced by sleep deprivation accumulation via a reduction in
341 $v$, while Du Rietz et al. (2019) reported a positive association between high-intensity activity and
342 brain indices associated with arousal and attentional allocation. In contrast, the current findings
suggest that exercise-induced changes are more nuanced and primarily reflected in non-decision processes.

This interpretation is consistent with prior evidence that physical exercise modulates both early (sensory) and late (motor) peripheral processing stages. Early studies have shown that moderate to intense exercise enhances sensory sensitivity and motor output speed, likely through increased excitability of the central nervous system (Davranche et al., 2005, 2006; Audiffren et al., 2008; Beyer et al., 2017). Exercise has been shown to increase cortical arousal and improve the signal-to-noise ratio in sensory processing, possibly due to the transient release of catecholamines and other neuromodulators (Davranche & Audiffren, 2004; Rodriguez et al., 2017; Moxon et al., 2007). At the motor level, evidence from single-pulse TMS studies suggests that exercise increases corticospinal excitability by reducing intracortical inhibition (Davranche et al., 2015), which could support more efficient motor command and response execution. These neurophysiological adaptations may underlie the observed improvements in $t_{er}$ and $v$, by rendering the brain more receptive to sensory input and more effective in translating decisions into action. Although the exact mechanisms remain to be fully elucidated, transient factors such as the release of catecholamines, serotonin, cortisol, or lactate production during exercise are likely contributors to these adaptive changes (Singh & Staines, 2015). Supporting this view, Coco et al. (2010) demonstrated that excitability of the primary motor cortex (M1), assessed via TMS over the hand area, was significantly enhanced at the end of a maximal cycling test, and that this enhancement strongly correlated with the increase in blood lactate levels. Interestingly, a similar effect on M1 excitability was also observed when lactate was increased via intravenous infusion in resting participants, suggesting a direct modulatory role of lactate on cortical excitability.
365 *Limitations and Perspectives*

366 Several limitations warrant consideration. First, the sample was predominantly male
367 (~95%), which may limit the generalization of the findings to female populations. Nonetheless,
368 previous studies involving gender-balanced cohorts have not reported sex-related differences in
369 cognitive responses to elevated lactate levels (Perciavalle et al., 2015; Coco et al., 2016).

370 Second, there was substantial inter-individual variability in physiological responses to
371 exercise at 90% of the first ventilatory threshold. Heart rates values ranged from 71% to 99% of
372 HRmax, indicating variability in the relative contributions of aerobic and anaerobic metabolism.
373 Post-hoc examination of individual lactate levels confirmed this variability, as participants were
374 nearly evenly split between those above and below the 4 mmol/L threshold, commonly associated
375 with the onset of blood lactate accumulation (OBLA). Since OBLA marks a key transition in
376 metabolic pathways, this variability in lactate response could account for the absence of pre- vs
377 post-exercise effect on v and may also obscure the effects during the different phases of the
378 exercise. Supporting this interpretation, Coco et al. (2009) and Perciavalle et al (2014) reported
379 that a large increase in blood lactate was associated with a significant worsening of attentional
380 processes, suggesting that beyond a certain threshold—possibly near OBLA—rising lactate
381 concentrations may no longer facilitate, but rather impair, cognitive functioning. This limit
382 underscores the need for careful consideration of individual physiological profiles in future
383 studies.

384 Additionally, differences in aerobic fitness likely modulate cognitive responses to intense
385 exercise. Individuals with higher fitness levels exhibit more efficient lactate clearance, which
386 may influence the duration and magnitude of post-exercise cognitive effects. Variability in
387 tolerance to elevated lactate concentrations may also modulate cognitive outcomes, underscoring
388 the importance of incorporating fitness assessments and lactate sensitivity measures in future
389 research.

390 **Conclusion**

391 By applying DDM to a perceptual decision-making task, this study provides new insights
392 into the cognitive mechanisms underlying high-intensity exercise-induced changes. Our findings
393 suggest that sustained physical activity is associated with changes in decision efficiency,
394 primarily through a decrease in non-decision time and an increase in evidence accumulation. As a
395 time-efficient and ecologically valid intervention, high-intensity exercise may represent a
396 promising tool to transiently enhance cognitive function, warranting further investigation.

397 **Acknowledgements:** We would like to thank the Filière Fastspor’in for its support in
398 carrying out this study, and Anita Vergnaud et Laurine Stefanuto for their assistance in data
399 collection.

400 **Declarations:**

401 **Funding:** Not applicable.

402 **Conflicts of interest/Competing interests:** Not applicable.

403 **Ethics approval:** Ethics Committee for Research in Science and Technology of Physical

404 Sports Activities (IRB00012476-2022-21-01-148).
405 **Consent to participate**: All participants were fully informed about the study and signed a
406 written informed consent before inclusion.

407 **Consent for publication**: All authors have reviewed and approved the final manuscript for
408 publication.

409 **Availability of data and materials**: All data and scripts used in the present study are
410 available at the following link: https://zenodo.org/records/17447883

411 **Code availability**: Not applicable.

412 **Authors' contributions**: KD and TG contributed to conceptualization, methodology, data
413 curation, formal analysis, and writing – original draft, review & editing. DG contributed to
414 investigation. AH contributed to resources and investigation.

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537
Appendices

538

539 **Figure S1.** Heart rate (HR) across exercise blocks. Group-level mean (MD) ± standard deviation

540 (SD).

<figure><img src="image_7.png" /></figure>

541

542 **Figure S2.** Blood lactate concentration across exercise blocks. Group-level mean (MD) ±

543 standard deviation (SD).

<figure><img src="image_8.png" /></figure>

544
545 **Figure S3.** Blood glucose concentration across exercise blocks. Group-level mean (MD) ±

546 standard deviation (SD).

<figure><img src="image_9.png" /></figure>

547
<table><caption>548 Table S2. Full summary of the results of the fit of the best model (at model)</caption><thead><tr><th></th><th>mean</th><th>sd</th><th>hdi_3%</th><th>hdi_97%</th><th>essBulk</th><th>essTail</th><th>rHat</th></tr></thead><tbody><tr><td>t<sub>er</sub>C(condition)[POST]</td><td>-0.032</td><td>0.020</td><td>-0.069</td><td>0.008</td><td>2001.0</td><td>2991.0</td><td>1.0</td></tr><tr><td>z_Intercept</td><td>0.513</td><td>0.017</td><td>0.481</td><td>0.546</td><td>3476.0</td><td>4558.0</td><td>1.0</td></tr><tr><td>a_Intercept</td><td>0.979</td><td>0.073</td><td>0.839</td><td>1.114</td><td>1415.0</td><td>2595.0</td><td>1.0</td></tr><tr><td>a_C(condition)[POST]</td><td>-0.033</td><td>0.037</td><td>-0.099</td><td>0.042</td><td>3445.0</td><td>4855.0</td><td>1.0</td></tr><tr><td>t_Intercept</td><td>0.502</td><td>0.040</td><td>0.430</td><td>0.580</td><td>1419.0</td><td>2594.0</td><td>1.0</td></tr><tr><td>v_Intercept</td><td>1.500</td><td>0.214</td><td>1.108</td><td>1.903</td><td>1318.0</td><td>2601.0</td><td>1.0</td></tr></tbody></table>

549

<figure><img src="image_10.png" /><figcaption>550 Figure S3. Model *at* traces</figcaption></figure>
553

<caption>Table S4. Full summary of the results of the fit of the best model (vt model)</caption>

554

<table><thead><tr><th></th><th>mean</th><th>sd</th><th>hdi_3%</th><th>hdi_97%</th><th>essBulk</th><th>essTail</th><th>rHat</th></tr></thead><tbody><tr><td>t_C(condition)[Middle]</td><td>-0.022</td><td>0.027</td><td>-0.077</td><td>0.026</td><td>1087.0</td><td>1535.0</td><td>1.01</td></tr><tr><td>t_C(condition)[End]</td><td>-0.041</td><td>0.020</td><td>-0.079</td><td>-0.004</td><td>1251.0</td><td>1813.0</td><td>1.00</td></tr><tr><td>v_C(condition)[Middle]</td><td>0.097</td><td>0.062</td><td>-0.017</td><td>0.215</td><td>3451.0</td><td>3114.0</td><td>1.00</td></tr><tr><td>v_C(condition)[End]</td><td>0.122</td><td>0.085</td><td>-0.035</td><td>0.280</td><td>2300.0</td><td>2320.0</td><td>1.00</td></tr><tr><td>zIntercept</td><td>0.495</td><td>0.018</td><td>0.461</td><td>0.529</td><td>1663.0</td><td>2143.0</td><td>1.00</td></tr><tr><td>aIntercept</td><td>0.951</td><td>0.070</td><td>0.831</td><td>1.092</td><td>977.0</td><td>1639.0</td><td>1.01</td></tr><tr><td>tIntercept</td><td>0.492</td><td>0.038</td><td>0.421</td><td>0.563</td><td>813.0</td><td>1126.0</td><td>1.00</td></tr><tr><td>vIntercept</td><td>1.427</td><td>0.220</td><td>1.037</td><td>1.861</td><td>1121.0</td><td>1654.0</td><td>1.00</td></tr></tbody></table>

555

556

<figure><img src="image_11.png" /><figcaption>Figure S5. Model vt traces</figcaption></figure>

557
558
559

**Figure S6.** Posterior predictive check of reaction times for PRE and POST exercise.

Posterior Predictive Check: Model basic

<figure><img src="image_12.png" /></figure>

Posterior Predictive Check: Model at

<figure><img src="image_13.png" /></figure>

560

561
562

**Figure S7.** Posterior predictive check (PPC) of reaction times across exercise blocks: Beginning, Middle, and End.

563

### Posterior Predictive Check: Model basic

<figure><img src="image_14.png" /></figure>

564

### Posterior Predictive Check: Model vt

<figure><img src="image_15.png" /></figure>

565