# Examining the impact of physiological stress on time perception: A systematic review and meta-analysis

Philippe Vignaud, Jerôme Brunelin, Perrine Galia, Simon Grondin, André Morin, Nathalie Prieto, Emmanuel Poulet, William Vallet

## ► To cite this version:

Philippe Vignaud, Jerôme Brunelin, Perrine Galia, Simon Grondin, André Morin, et al.. Examining the impact of physiological stress on time perception: A systematic review and meta-analysis. Neuroscience and Biobehavioral Reviews, 2025, 178, pp.106382. 10.1016/j.neubiorev.2025.106382. hal-05297517

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Submitted on 29 Oct 2025

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# Examining the impact of physiological stress on time perception: A systematic review and meta-analysis

Philippe Vignaud<sup>a,b,c</sup>, Jerôme Brunelin<sup>c,d</sup>, Perrine Galia<sup>b</sup>, Simon Grondin<sup>e</sup>, André Morin<sup>e</sup>, Nathalie Prieto<sup>a,b</sup>, Emmanuel Poulet<sup>c,d,f</sup>, William Vallet<sup>c,d,*</sup>

## ARTICLE INFO

### Keywords:

Time perception

Stress

## ABSTRACT

Stress is a ubiquitous experience that can significantly impact various aspects of human cognition and behavior, including time perception. Time perception, which refers to the ability to estimate and experience time intervals, plays a crucial role in everyday functioning and decision-making. However, whether stress affects how individuals perceive time remains unclear in literature. While some studies report that time perception can be underestimated (i.e., larger time production and shorter estimate) under certain stress conditions, others observe the opposite effect, with an overestimation of perceived time (i.e., shorter time production and larger estimate). To clarify the inconsistencies in the literature, we conducted a systematic review and meta-analysis of studies examining the effects of acute stress on time perception. The model, based on 437 participants exposed to stress and 434 control participants, demonstrated a significant effect favoring overestimated time under stress conditions (Cohen's d = -0.40; 95 % CI: -0.7037 to -0.1016). No significant effects of task type and socio-demographic factors were observed.

## 1. Introduction

Stress is a universal aspect of human experience. In response to an acute stressor, the body and mind undergo a complex array of physiological and cognitive reactions, all aimed at preparing the body for action and supporting adaptive behavior. The stress response is mediated by the activation of several pathways, including the autonomic nervous system, specifically one of its anatomical divisions: the sympathetic nervous system (SNS). This activation leads to an increase in heart rate, elevated blood pressure, heightened hormonal secretion, and adjustments in sweat regulation (Goldstein, 2021). While stress response is a normal and adaptive mechanism in daily life, chronic or excessive stress can overwhelm the system, leading to a compromised or maladaptive response (Mc Ewen, 1998). Such a maladaptive response can, in turn, lead to cognitive deficits and trigger the onset of various symptoms observed in multiple psychiatric conditions, including major depressive

disorder, substance-use related disorder, anxiety disorders, and psychosis (Tafet and Nemeroff, 2016; Wingenfeld and Wolf, 2015; Zorn et al., 2017).

At the cognitive level, among other cognitive functions involved in the “fight or flight” response, stress can influence the perception of time, altering how its duration is evaluated (Gilbert et al., 1977; Hancock and Weaver, 2005; Langer et al., 1961). However, accurate and sensitive evaluation of interval duration is central to facilitating behavioral adaptation (Grondin, 2010; van Hedger et al., 2017; Zakay, 1993) and adjusting behavior, particularly in stressful and challenging situations. To illustrate, in a critical situation, one may need to precisely calculate the estimated time to collision with an oncoming vehicle, based on current speeds. In these circumstances, the decision of whether and when to slow down or swerve becomes a critical choice (Eisen, 2009; Hancock and Weaver, 2005).

In order to experimentally assess time perception in Humans,

<footnote><sup>a</sup> Cellule d'urgences médico-psychologiques, hôpital Edouard Herriot, Lyon F-69437 France</footnote>

<footnote><sup>b</sup> Centre régional du psychotraumatisme, hôpital Edouard Herriot, Lyon F-69437, France</footnote>

<footnote><sup>c</sup> Université Claude Bernard Lyon 1, Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale, Centre de Recherche en Neurosciences de Lyon U1028 UMR5292, PSYR2, F-69500, Bron, France</footnote>

<footnote><sup>d</sup> Le Vinatier, Psychiatrie Universitaire Lyon Métropole, F-69500, Bron, France</footnote>

<footnote><sup>e</sup> Ecole de psychologie, Université Laval, Laboratoire recherche en psychologie de la perception, Québec, Canada</footnote>

<footnote><sup>f</sup> Service d'urgences psychiatriques; hôpital Edouard Herriot, Lyon F-69437, France</footnote>

<footnote>* Correspondence to: CH Le Vinatier, 95 Bd Pinel, PSYR2 team, Bâtiment 416, 1st floor, Bron 69500, France.
E-mail address: william.vallet@inserm.fr (W. Vallet).</footnote>
psychophysics traditionally uses a variety of behavioral tasks involving explicit timing conditions. Explicit timing is defined as a domain of time perception in which individuals are required to provide overt and precise estimates of elapsed time. Common tasks employed to assess time judgment include interval temporal generalization, temporal bisection, time production, and time reproduction. In these tasks, participants may be told in advance that they will have to estimate the duration of an interval, thereby giving them the opportunity to pay attention to time (prospective timing) or they may informed that they have estimate the duration of an interval only once it has ended, i.e., without paying special attention to time (retrospective timing (Grondin et al., 2015)). In explicit timing, time perception is primarily influenced by attention (Brown, 2008) and the level of physiological activation also referred to as arousal, ranging from calm to excitement (Cui et al., 2023), factors that are central to clock models of time perception(Church, 1984; Grondin, 2010; Zakay and Block, 1997).

Pioneering computational models of time perception, such as the Scalar Expectancy Theory (SET (Gibbon et al., 1984)), initially overlooked the role of attentional control. These models posited that a pacemaker or oscillator emitted neural pulses, which were captured by an accumulator modulated by a switching mechanism. The pulse count provided the basis for estimating time, with a higher count corresponding to a longer perceived duration. This pulse count was subsequently stored in memory for future comparisons.

Subsequent advancements incorporated attentional mechanisms into the internal clock framework, as exemplified by the Attentional-Gate Model (AGM (Zakay and Block, 1995)). The AGM proposes that the pulse count is regulated by a gate mechanism linked to attentional allocation, whereby increased attention directed toward the temporal aspect of a stimulus allows more pulses to pass through the gate, resulting in a longer perceived duration (Macar et al., 1994). In this model, the current perception of time is stored in working memory and compared against previous temporal experiences stored in long-term memory to estimate durations. In explicit timing tasks, stored temporal experiences can be used to produce a duration or estimate the current time as longer or shorter compared to the reference stored in memory.

The human brain has a remarkable ability to accurately estimate the passage of time, maintaining a high sensitivity even for short durations ranging from milliseconds to seconds (Mioni and Grondin, 2024; Van Wassenhove et al., 2019). However, the perception of time is not immune to distortion (Grondin, 2020). The experience of time can be compressed, based on an acceleration of the emission of pulses stored in the pacemaker. This time compression leads to an overestimation of elapsed time and the production of shorter durations. Conversely, the experience of time can be dilated, based on a deceleration of the emission of pulses stored in the pacemaker. So, under these conditions the brain tends to underestimate durations and to produce longer durations.

Stress play a significant role in distorting time perception, a phenomenon that is increasingly highlighted in the growing body of research (Kingma et al., 2021; Sarigiannidis et al., 2020; van Hedger et al., 2017; Zlomuzica et al., 2022) examining how stress affects our sense of time. As previously mentioned, exposure to physiological stressors triggers coordinated stress responses involving multiple homeostatic systems, with SNS playing a central role (Goldstein, 2021). The degree of activation of the SNS, referred to as "SNS arousal" and ranging on a continuum from activation to deactivation, has been proposed as a primary mechanism in the modulation of time perception under stress. For example, increased activation of the sympathetic nervous system (SNS) can accelerate the internal pacemaker, resulting in temporal distortions (Ogden et al., 2019, 2022). More specifically, increases in physiological arousal tend to compress time perception (leading to overestimation of durations) by accelerating the pacemaker output rate at the clock stage, whereas decreases in physiological arousal would tend to expand time perception by slowing the pacemaker (leading to underestimation of durations (Cui et al., 2023; Droit-Volet and Meck, 2007; Gibbon et al., 1984; Grondin et al., 2014; Grondin

et al., 2015; Lake et al., 2016; Penton-Voak et al., 1996)).

The effect of SNS arousal on time perception has been well documented in numerous studies using various stress-inducing methods. Among the most used methods are extreme temperatures, aversive noise, electric shocks and social stressors. Nonetheless, experimental stressors have yielded inconsistent results regarding their effects on time perception. While some studies report a compression of time perception in stressed healthy participants (Tamm et al., 2014; van Maanen et al., 2019) others find a dilatation of time perception following exposure to stressors (Sarigiannidis et al., 2020).

Considering these inconsistent results, we conducted a meta-analysis of studies investigating the effects of acute stress on time perception. The aims were: (i) to determine whether acute stress exposure modulates time perception; (ii) to identify the direction of this modulating effect, that is whether it leads to compression or expansion of perceived time; (iii) to quantify the mean size of the effect; and (iv) to explore potential moderators that influence the relationship between acute stress exposure and time perception. Based on the established links between the internal clock pacemaker, arousal, the SNS and stress, we hypothesized that acute physiological stress induces an accelerated pacemaker, leading to a compressed perception of objective time. In addition to this rationale, the hypothesis seems to be consistent with an evolutionary perspective. In order to adapt one's behavior to dangerous situations, one needs more time to gather and process information. With time compression, subjective time gets longer, which may facilitate the decision-making process in the present or the encoding memory process in case of a future exposure to a similar danger (Stetson et al., 2007)

## 2. Materials and methods

The systematic review was conducted in accordance with the recommendations of the Cochrane Collaboration (Chandler et al., 2013) and the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA (Page et al., 2021)). The current meta-analysis was registered in PROSPERO (CRD42023476198).

### 2.1. Eligibility

Inclusion criteria were as follows: i) full-length original articles published in English in peer-reviewed journals; ii) controlled trials (parallel arms or cross-over) or studies with a within-subject design in which time perception was assessed both before and after stress exposure; iii) the stress task had to be time-limited and involve acute exposure to a physiological challenge (e.g., extreme temperatures, unpleasant noises, or social stressors) known to result in a robust and sustained activation of the HPA axis; iv) use of a standardized time perception task; and v) studies conducted in healthy adult human participants.

Studies were excluded according to the following criteria: i) commentaries, indices, protocol designs, open-label studies, case reports, reviews, meta-analyses or letters to the editor; ii) participants under the age of 18 years; iii) participants exposed to non-acute stressors (such as the COVID-19 pandemic and repeated confinement procedures); and iv) stress tasks based on emotional challenges using brief presentation of pictures (in other words, the tasks where the main goal explicitly consists in inducing a specific negative emotion such as sadness, anger, etc.)

### 2.2. Research strategy

We conducted a systematic search in the following databases: PubMed, Embase, Web of science and the first 200 references of google scholar. The search was conducted in these different databases until October 2024 using the following MESH words with no limitation of date: i) PubMed: ("Time Perception"[MeSH Terms] OR "Time Perception"[All Fields] OR "temporal perception"[All Fields] OR "time
duration"[All Fields] OR "rhythm perception"[All Fields]) AND "stress"*[All Fields]; ii) Embase: ('time perception'/exp OR 'time perception' OR 'temporal perception' OR 'time duration' OR 'rhythm perception') AND ('stress'/exp OR stress); iii) Web of Science: (TS=("time perception") OR TS= ("temporal perception") OR TS= ("time duration") OR TS= ("rhythm perception")) AND TS= (stress); iv) Google scholar: ("Time perception" OR "temporal perception" OR "time duration" OR "rhythm perception") AND "stress". We also manually examined the citation lists of the identified publications for additional studies and used the related articles function of the PubMed database for other relevant sources of data. Two investigators (PV and PG) independently screened the title, abstract and key words of each reference identified by the search and applied the inclusion and exclusion criteria. For each potentially eligible reference, the same procedure was applied to the full-text article. Discrepancies between the reviewers were resolved via discussion with a third investigator (WV).

## 2.3. Quality assessment

The quality of each study was assessed using the QualSyst checklist for assessing the quality of quantitative studies. The QualSyst checklist assesses the quality of studies by rating a wide range of 14 methodological criteria on a 3-point scale (2 = the study fully met the criterion, 1 = the study partially met the criterion, 0 = the study did not meet the criterion) and has been shown to have high reliability (Kmet et al., 2004). Two reviewers (PV and PG) independently assessed each included study and calculated a QualSyst score. The scores of both reviewers were then averaged. In case of discrepancies (i.e., if the difference between the QualSyst scores of the two reviewers was greater than 10 % of the maximum score), these were resolved with the intervention of a third reviewer (WV). A Qualsyst score > 0.80 was interpreted as high quality, 0.65-0.79 as good quality, 0.55-0.64 as fair quality and < 0.55 as poor methodological quality (Kmet et al., 2004). Notably, the quality rating was not an inclusion criterion but was reported to inform the interpretation of the results.

## 2.4. Data extraction

Two investigators (PV and PG) independently extracted the following data: i) demographic data (number of participants, age, sex); ii) experimental design (cross-over or parallel arms or within-subject procedure); iii) characteristics of the stress task (type of stressor, duration when appropriate); iv) type of time perception task used in the study; v) the outcomes used to assess the level of the induced stress.

## 2.5. Methods of meta-analysis model

Quantitative analyses were performed using the R project with the Metafor package, version 3.2.1 (Viechtbauer, 2010). First, we calculated a random effects model to determine the effect size of time perception under stress. This model provided a pooled Cohen's d effect size across studies, based on the following metrics: time production, time estimation, and bisection point (BP) value. To calculate Cohen's d, we used statistical data comparing time perception under stress versus no stress conditions. The F-values from one-way ANOVA, correlation coefficients (r) and sample statistics (means and standard deviations) were mostly available in the studies and were used for this calculation. The statistical values were input into the effect size calculator (Lipsey and Wilson, 2001) to compute the effect size and the variance of d (Vd).

Regarding the polarity of Cohen's effect size, a negative value indicates an acceleration of pulse emission, resulting in a compressed perception of time. This compression is evidenced in time perception tasks by larger time estimates, shorter time productions and shorter bisection point (BP) values. Conversely, a positive Cohen's d effect indicates an expanded perception of time, characterized by shorter time estimates, longer time productions and larger BP values.

After data extraction, we calculated a random effect model based on Cohen's d with maximum likelihood estimation of the effect size of the primary outcome. We used a random effect model to account for between-study variability and thus provide a more conservative estimate of the composite effect size. Results of the model were reported according to I² for residual heterogeneity or unexplained variability, relative risk [95 % CI], Q-test for residual heterogeneity, and p-value for significance. Finally, Egger's test (weighted regression models with multiplicative dispersion) was used to test for funnel plot asymmetry and potential publication bias.

For exploring the influence of some potential causal factors (type of stress task, sex, age...) we used mixed effect model. The models reported the results according to I² for residual heterogeneity or unaccounted variability, relative risk [95 % CI], QE-test for residual heterogeneity, QM-test for the omnibus test of coefficients, and p value for significance. Egger's test was also performed to test funnel plot asymmetry and potential publication bias.

# 3. Results

## 3.1. Systematic review

The primary search returned 1388 results. The Fig. 1 shows the flow chart of the systematic search. After the exclusion of records, 29 references remained to be assessed for eligibility. A supplementary manual search of the reference lists of the included studies completed the systematic search and added 2 further articles that were also assessed for inclusion. At this stage, 16 additional references were excluded (reasons given in Fig. 1). Finally, 15 articles corresponding to 16 datasets that met all our inclusion criteria were eligible for quality assessment and then included in the meta-analysis (Fig. 1).

## 3.2. Quality assessment

The methodological quality of the 15 selected articles ranged from 0.61 to 0.92, as assessed by the QualSyst tool. The mean score was 0.76, with a median of 0.77. Among the studies included, only two were classified as "fair" or "poor" quality, indicating a QualSyst score below the acceptable threshold. 0.65 (Table S1).

## 3.3. Preliminary qualitative analyses of the studies included in the meta-analysis

All included studies used either a parallel arm or crossover design, except for two. The quantitative analysis included 437 participants in the experimental group and 434 in the control group. The mean age of the participants was 23.59 years (SD = 2.45). Gender was reported in all but two studies, with a female to male ratio of 0.57. Time perception tasks were relatively consistent across studies. Four studies used a time bisection task (Cellini et al., 2023; Droit-Volet et al., 2010; Sarigiannidis et al., 2020; Yao et al., 2016), two datasets were included from the study by Sarigiannidis and colleagues. Five used a time estimation task (S. Bagley et al., 2021; Cocenas-Silva et al., 2012; Piovesan et al., 2019; van Hedger et al., 2017; Zlomuzica et al., 2022). Six used a time production task (Brocas et al., 2018; Hancock, 1993; Kingma et al., 2020; Ozel et al., 2004; Tamm et al., 2014; van Maanen et al., 2019). Physiological stress induction methods varied, with 12 studies using physical stress tasks and three studies using psychosocial stress tasks (see Table 1 and 2).

## 3.4. Pooled effect size of stress on time perception

The meta-analysis revealed a moderate time compression effect following exposure to stress (Cohen's d = -0.40; 95 % CI: -0.7037 to -0.1016; p < 0.01; I² = 77 %; Fig. 2). The Q-test indicated significant heterogeneity (Q(df = 15) = 64.96, p < 0.01). Additionally, Egger's test did not show any significant asymmetry, suggesting a symmetrical
<figure><img src="image_4.png" /><figcaption>Fig. 1. PRISMA flowchart of the study selection process.</figcaption></figure>

funnel plot and no notable publication bias (t = -0.68, df = 15, p = 0.5047; Fig. 3).

### 3.5. Investigation of different potential moderators in the meta-analysis model

First, we examined whether the type of stress task served as a moderator of the observed time compression effect. The meta-analysis showed significant heterogeneity (QE(df = 11) = 57.5590, p < 0.01) and the impact of this moderator was not significant (QM(df = 4) = 0.86, p = 0.92). The analysis identified four subtypes of stress tasks: three physiological stress tasks—exposure to heat (p > 0.05), noise (p > 0.05), and electric shocks (p > 0.05)—and one psychosocial task, the Trier Social Stress Test (p > 0.05). Egger's test indicated no significant publication bias (t = -0.2810, p > 0.05).

Second, we assessed the moderating effect of the type of time perception task. The meta-analysis again displayed significant heterogeneity (QE(df = 14) = 64.89, p < 0.01) and the time perception task was not a significant moderator (QM(df = 1) = 0.0152, p = 0.9018). The Egger's test showed no significant publication bias (t = -0.22, p > 0.05).

Third, we explored age as a potential moderator. The analysis revealed significant heterogeneity (QE(df = 11) = 43.88, p < 0.01), but

age was not a significant moderator (QM(df = 1) = 1.09, p = 0.29). No publication bias was detected (t = 0.76, p > 0.05).

Finally, we tested the moderating effect of participant sex. The meta-analysis remained heterogeneous (QE(df = 12) = 59.91, p < 0.01) and non-significant (QM(df = 1) = 0.20, p = 0.65), with no evidence of publication bias (t = -0.28, p = 0.78).

## 4. Discussion

The present meta-analysis aimed to investigate the effect of physiological stress on time perception. The quality assessment of the studies included in the meta-analytic model was generally high, with only two studies classified as poor. Based on the pooled effect size extracted from each study, the primary finding suggests that stress leads to time compression, resulting in an overestimation of duration and a decrease in time production, regardless of the experimental paradigm used. According to Cohen's d interpretation guidelines, the effect was large (Cohen, 2008). Considering the observed discrepancies in the extant literature, the findings of this study substantiate the human capacity to modulate time perception in stressful circumstances, thereby adapting behavior and responses to potentially hazardous environments. This phenomenon finds application in ecological contexts, wherein the allocation of additional time for threat assessment might prove
<table><caption>**Table 1**
General features of the included studies.</caption><thead><tr><th>First Author</th><th>Experimental design; randomization (yes/no/nr)</th><th>Type of time perception task used in the study</th><th>Time intervals tested in the study (in seconds)</th><th>Mean age (SD)</th><th>Number of participants (Male/Female)</th><th>Was time perception the main outcome of the study? (Yes/No)</th></tr></thead><tbody><tr><td>Droit-Volet, 2010</td><td>Cross-over; nr</td><td>Bisection task</td><td>0.2–0.8; 0.8–1.6</td><td>nr</td><td>Experiment 1: 30 (nr)<br>Experiment 2: 34 (nr)</td><td>Yes</td></tr><tr><td>Yao, 2016</td><td>Parallel arms; yes</td><td>Bisection task</td><td>0.4–1.6</td><td>21,22<br>(1,28)</td><td>57</td><td>Yes</td></tr><tr><td>Sarigiannidis, 2020</td><td>Within-subject design; no</td><td>Bisection task</td><td>1.4–2.6</td><td>24,14<br>(nr)</td><td>130 (45/85)</td><td>Yes</td></tr><tr><td>Cellini, 2023</td><td>Parallel arms; yes</td><td>Bisection task</td><td>0.3–0.9 s</td><td>22,89<br>(2,48)</td><td>38 (10/28)</td><td>Yes</td></tr><tr><td>Cocenas-Silva, 2012</td><td>Parallel arms; yes</td><td>Time estimation task</td><td>1.0–7.0</td><td></td><td>120</td><td>No</td></tr><tr><td>Bagley, 2021</td><td>Parallel arms; yes</td><td>Time estimation task</td><td>47 and 72</td><td>19,09<br>(0,87)</td><td>34 (34/0)</td><td>Yes</td></tr><tr><td>Zlomuzica, 2022*</td><td>Parallel arms; yes</td><td>Time estimation task</td><td>From 0.0–510.0</td><td>25,5<br>(2,5)</td><td>87 (52/35)</td><td>No</td></tr><tr><td>van Hedger, 2017</td><td>Within-subject design; no</td><td>Time estimation task</td><td>0.4; 1.650; 2.9; 4.150</td><td>19,95</td><td>42 (26/16)</td><td>Yes</td></tr><tr><td>Piosesan, 2018</td><td>Cross-over; yes</td><td>Time estimation task</td><td>0.242; 0.455; 0.767;<br>1.058; 1.296</td><td>26,2<br>(3,91)</td><td>40 (10/30)</td><td>No</td></tr><tr><td>Tamm, 2014</td><td>Cross-over; yes</td><td>Time production task</td><td>0.5; 0.75; 1.0; 2.0; 3.0;<br>5.0; 10.0</td><td>25,3<br>(3,9)</td><td>24 (24/0)</td><td>Yes</td></tr><tr><td>Van Maanen, 2019</td><td>Cross-over; yes</td><td>Time production task<br>(interval timing task)</td><td>1.0</td><td>23 (nr)</td><td>29 (29/0)</td><td>No</td></tr><tr><td>Kingma, 2021</td><td>Cross-over; yes</td><td>Time production task<br>(interval timing task)</td><td>1.0</td><td>23 (4)</td><td>27 (27/0)</td><td>Yes</td></tr><tr><td>Ozel, 2004</td><td>Cross-over; yes</td><td>Time production task</td><td>10.0</td><td>26,8<br>(3,43)</td><td>17 (9/8)</td><td>Yes</td></tr><tr><td>Brocas, 2018</td><td>Parallel arms; yes</td><td>Time production task</td><td>31.0–41.0</td><td>nr</td><td>170 (79/91)</td><td>No</td></tr><tr><td>Hancock, (1993)</td><td>Cross-over; yes</td><td>Time production task</td><td>1.0; 11.0; 41.0</td><td>26.6<br>(10.4)</td><td>12 (12/0)</td><td>Yes</td></tr></tbody></table>

Nr, not reported. * A retrospective time perception paradigm was used in the study.

advantageous. When confronted with a stressful stimulus, a compressed perception of time (i.e., slower subjective time) may enhance the ability to accumulate a larger amount of information, enabling a more adaptive response. Furthermore, the confirmation of time compression aligns with the pacemaker- accumulator model (AGM), which suggests that sympathetic nervous system (SNS) arousal accelerates pulse emission, leading to an overestimation of duration during exposure to stress (Cui et al., 2023).

## 4.1. How does SNS arousal modulate time perception ?

Stress in mammals is primarily associated with two main physiological systems: the hypothalamic-pituitary-adrenal (HPA) axis, and a sub part of the SNS: the sympatho-adrenomedullary (SAM) system. Under conditions of stress, the SAM system activates the adrenal medulla, resulting in the release of catecholamines that facilitate short-term responses, such as the "fight or flight" reaction (Ulrich-Lai and Herman, 2009). Simultaneously, the central nervous system (CNS) activates the HPA axis, leading to the secretion of the pituitary hormone adrenocorticotropic hormone (ACTH). This physiological arousal induced by stress is believed to affect time perception through the activation of the left anterior insular cortex (AIC), which is involved in both time perception and modulation of the activity of the SNS (Ogden et al., 2019). The proposed explanation is that the dual function of the AIC, in both time perception and homeostatic regulation, leads to time distortion (Craig, 2009; Wittmann, 2009). However, a straightforward causal relationship between time perception and the ANS should be interpreted with caution. Findings across studies have been inconsistent, suggesting that the interaction between physiological arousal and time perception is likely more complex (Ogden et al., 2019; Piovesan et al., 2019). This rationale guided our decision to limit the selection of articles in this study to those examining physiological stress while excluding studies on induced cognitive stress with brief presentations of emotional content. Although the induction of negative emotions can elicit a stress response

and potentially activate the hypothalamic-pituitary-adrenal (HPA) axis—resulting in increased cortisol secretion—the strength of this association remains relatively weak (Joseph et al., 2021). Moreover, cortisol secretion in response to emotional stress is significantly influenced by individual, contextual, and situational factors, such as daily physical activity and social support (Doane and Zeiders, 2014; Pauly et al., 2019). Given these complexities, we considered the effects of emotionally induced stress on time perception to be a distinct area of research and therefore excluded it from our meta-analysis. Instead, we included only studies employing validated stress tasks known to robustly activate the HPA axis, leading to a substantial and sustained cortisol release.

In the current meta-analysis, the sex ratio moderator showed no statistically significant effect on the relationship between physiological arousal and time perception. However, research consistently indicates that the HPA axis response to physiological arousal is greater in males than in females. In response to a stressor, females may exhibit a more pronounced physiological reaction, as gonadal hormones have been shown to inhibit the negative feedback of cortisol on the HPA axis. The role of gonadal hormones in this sex-based disparity is well documented, with evidence suggesting that these hormones contribute to the establishment, activation, and regulation of the HPA axis (Heck and Handa, 2019). More specifically, estrogen secretion may influence HPA axis activity by enhancing the function of N-methyl-D-aspartate (NMDA) receptors, which are widely distributed in the hippocampus (Foy et al., 1999). This mechanism may underlie the observed tendency for females to overestimate duration (Block et al., 2000) and to produce shorter duration (Glicksohn and Hadad, 2012) as compared to males.

Furthermore, due to the fluctuations associated with the menstrual cycle, the influence of gonadal hormones on HPA axis regulation varies over time (for a detailed review, see Oyola and Handa, 2017). Conversely, it is plausible to hypothesize that females exhibit a heightened sensitivity to stress exposure, which could result in a more pronounced impact of stress on time perception. However, potential
<table><caption>**Table 2**
Advanced features of the included studies and main results.</caption><thead><tr><th>First Author, Year</th><th>Stress task</th><th>Control condition</th><th>Was the induced stress assessed? If yes, outcome to evaluate the level of stress</th><th>Main results</th><th>Direction of the difference</th></tr></thead><tbody><tr><td colspan="6"><strong>Studies completed with a time bisection task</strong></td></tr><tr><td>Droit-Volet, 2010</td><td>Experiment 1: aversive noise (50ms burst of a 95dB white noise)<br>Experiment 2: same</td><td>Experiment 1: neutral noise; no noise<br>Experiment 2: the aversive noise was presented either before or after the time perception trial</td><td>SCR<br>SAM</td><td>In the stress condition (exposure to the aversive noise), the BP was shorter than in the no noise group. This was the case for both tested task duration.<br>The effect size in the aversive condition was greater when the expectation was longer (namely when the aversive sound was presented after the time estimation trial)<br>Data included in the meta-analysis : raw data table 1</td><td>Brain time was compressed by stress</td></tr><tr><td>Yao, 2016</td><td>Stress task combining a variant of the TSSF and a mental calculation task</td><td>Mental calculation task, without any time or psychosocial pressure</td><td>Cortisol reactivity (saliva sample)<br>Heart rate, PANAS</td><td>Both BP and WR did not display any significant main effect of Test period or Group, or significant interaction effect between Test period and Group.<br>Data included in the meta-analysis : Raw data table 2 page 10</td><td>Brain time was not affected by stress</td></tr><tr><td>Sarigiannidis, 2020</td><td>Groups 1; 2; 3) Threat of shock; the participants were informed that electric shocks would occur but did not know when and how many. Groups 4 ; 5 Fear manipulation : electric shock were delivered to the participants without any previous information</td><td>Groups 1; 2; 3: anxiety was expected; Groups 4; 5: fear was expected. For each group there was a control condition without any intervention</td><td>Visual analogue scale assessing the subjective level of induced anxiety or fear</td><td>In the threat condition, BP was significantly larger during the threat period (M=246.75, SD=186.19).<br>In the fear condition, BP was shorter during the fear (M=2,004.77, SD=194.98) as compared to the baseline (M=2,083.58, SD=153.06) condition but the effect was no significant (t(24)=1.99, p=0.058, d=0.40).<br>Data included in the meta-analysis<br>Experiment 3: threat t test (t(19)=2.66, p=.015; p=.001 page 8 ;<br>Experiment 4: shock ANOVA F(1, 24)=3.78, p=.063 page 9</td><td>Brain time was diluted by the treat condition<br>Brain time was not affected by the fear condition</td></tr><tr><td colspan="6"><strong>PASAT: a mental calculation task</strong></td></tr><tr><td></td><td></td><td>PANRAT</td><td>Physiological outcomes: HR, electrodermal activity, SCR.<br>Psychological outcomes: SAM<br>Scale (SAM, a combination of 2 Visual Analogue Scales assessing the subjective valence and the arousal induced by the task)</td><td>The CE was significantly lower in the stress group than in the control group.<br>Data included in the meta-analysis<br>CE : ANOVA F(1, 36) = 5.05, p = 0.031 page 2235</td><td>Brain time was compressed by stress</td></tr><tr><td colspan="6"><strong>Studies completed with a time estimation task</strong></td></tr><tr><td>Cocenas-Silva, 2012</td><td>aversive noise (50ms burst of a 95dB white noise)</td><td>Yes</td><td>No</td><td>At the delayed test, the peak time was longer in the control condition as compared to the aversive and non-aversive conditions. But when considering more precisely the control condition, the peak time did not differ significantly between the immediate and the delayed test.<br>Data included in the meta-analysis : Raw data figure 2 (mean + standard errors) page 164</td><td>Brain time was not affected by stress</td></tr><tr><td>Bagley, 2021</td><td>SCPT</td><td>Yes (Warm Water Task)</td><td>Cortisol reactivity (saliva sample), PANAS</td><td>In the SECT group, time was estimated as larger than in the control. Water task grouping was the only statistically significant variability factor.<br>Data included in the meta-analysis : Raw data (stress group mean 12,13 control group=9.73) page 293</td><td>Brain time was compressed by stress</td></tr><tr><td>Zlomuzica, 2022</td><td>The threat of scream paradigm. This was an updated and online version of the threat of shock paradigm</td><td>4 groups: Safety-Safety / Safety-Threat / Threat-Safety / Threat-Threat</td><td>Visual analogue scale assessing the subjective level of induced distress</td><td>no significant difference in the time estimation task was reported (session 1: z2[3] = 2.596; p=0.4582; session 2z2[3] =0.5733; p=0.9025)<br>Data included in the meta-analysis : raw data transferred by the authors</td><td>Brain time was not affected by stress</td></tr><tr><td>Van Hedger, 2017</td><td>TSST</td><td>baseline</td><td>HR, PEP, HF-HRV, STAI-state</td><td>Time estimations were significantly larger for negative pictures at both the 400-ms and 4,150-ms durations.<br>Data included in the meta-analysis : t test, t(41)=3.55, p=.001 page 709</td><td>Brain time was compressed by stress</td></tr><tr><td>Piovesan et al. 2018</td><td>Repeated painful cutaneous electro-stimulation</td><td>Yes</td><td>HF-HRV, SCR</td><td>Times estimations were significantly larger in the high pain than in the low pain and no pain conditions.<br>Data included in the meta-analysis : raw data : table 2 ; page 6</td><td>Brain time was compressed by stress</td></tr><tr><td colspan="6"><strong>Studies completed with a time production task</strong></td></tr><tr><td>Tamm, 2014</td><td>Condition 1: temperate (22°C) or hot (40°C) environment. Condition 2: no exercise or walking at 60% of VO2max until subjective exertion Hne and Nne at temperature for 90min</td><td>Yes</td><td>Ratings of perceived fatigue (CR-10); RPE</td><td>The participants in the group Hot temperature + Exercise (HEx) produced shorter time intervals. The difference between the 3 other groups were not significant. In the Hex group, time distortion was correlated with core temperature and the subjective level of fatigue.<br>Data included in the meta-analysis : correlation r(156) = -.24, p&lt;0.01</td><td>Brain time was compressed by stress</td></tr><tr><td>Van Maanen, 2019</td><td>Warm bath at 38°C for 60 min</td><td>Bath at 36°C for 60 min</td><td>No</td><td>The participants in the stress condition provided shorter time productions.<br>Data included in the meta-analysis : regression coefficient β = -0.41 (0.17) page 3</td><td>Brain time was compressed by stress</td></tr><tr><td>Kingma, 2021</td><td>Warm bath at 38,5°C for 60 min</td><td>Bath at 36°C for 60 min</td><td>Thermal sensation was assessed on a 7-point paper scale; thermal comfort was assessed using a 4-point paper scale; RPE from water immersion is assessed using a paper Borg scale</td><td>Time production was shorter in the warm bath condition (F(1,26) = 6.48, p=0.02), including an interaction effect with time (F(1,26) = 11.7, p&lt;0.001). Time was contracted only at the 60min assessment.<br>Data included in the meta-analysis : ANOVA (F(1,26) = 6.48, p &lt; 0.02) page 59</td><td>Brain time was compressed by stress</td></tr><tr><td>Ozel, 2004</td><td>Listening to a 20 min long audio record composed of noises commonly considered as very stressful ("sub-machine-gun and fighting"; "motocross-noises")</td><td>Yes</td><td>No</td><td>Time production was significantly shorter the noise condition as compared with the no-noise condition.<br>Data included in the meta-analysis : Raw data : "Times observed in the noise condition (M = 7.66, s, SE = 0.40) were significantly lower. F(1, 12) = 53.95, p &lt; 0.001 than the times in the no-noise condition (M = 8.87, s, SE = 0.20)" page 200</td><td>Brain time was compressed by stress</td></tr><tr><td>Brocas, 2018</td><td>SCPT</td><td>Yes</td><td>Salivary cortisol, pupil diameter, electrodermal activity, HRV</td><td>No significant difference between the group exposed to stress and the control group regarding time production.<br>Data included in the meta-analysis : p-value p=0.67 page 13</td><td>Brain time was not affected by stress</td></tr><tr><td>Hancock, 1993</td><td>Heating helmet (heat 1 : +0.75°C ; heat 2 : +1.5°C)</td><td>Yes</td><td>Head temperature measured at the site of the deep auditory meatus</td><td>In the heat 2 condition, time production was significantly shorter than in the other conditions (p&lt;0.05). in the heat 1 condition, time production did not differ from the placebo and control conditions.<br>Data included in the meta-analysis : raw data table 2 page 203</td><td>Brain time was compressed by the severe heating<br>Brain time was not affected by the mild heating</td></tr></tbody></table>
BP, Bisection Point; BMIS, Brief Mood Introspective Scale; CE, Constant Error; HR, Heart Rate; HRV, Heart rate Variability; HF-HRV, High Frequency Heart rate Variability; IAPS, International Affective Picture System; PANRAT, Paced Auditory Number Reading Task; PASAT, Paced Auditory Serial Addition Task; PANAS, Positive and Negative Affective Scale; PEP, PreEjection Period; RPE, Rate of Perceived Exertion; SAM, Self-Assessment Manikin; SCR, Skin Conductance Response; SCPT, Socially Evaluated Cold Pressure Task; SUD, Subjective Unit of Distress; TSST, Trier Social Stress Test; WR, Weber Ratio.

<figure><img src="image_5.png" /><figcaption>Fig. 2. Forest plot for the meta-analysis on Stress and Time Perception. RE: Random effect.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 3. Funnel plot for the meta-analysis. Point represents the observed effect sizes against standard error. In current meta-analysis, majority of points falling on the pseudo-confidence interval region and Egger's test for funnel plot asymmetry reported no publication bias.</figcaption></figure>

confounding factors related to the menstrual cycle, such as the partici-
pant's cycle phase at the time of inclusion in the study or the use of
hormonal contraception, were not controlled for. Nevertheless, the ef-
fect size of the sex ratio on time perception was not statistically signif-
icant in the current meta-analysis. While the included studies exhibited a
marginally higher proportion of female participants (sex ratio = 0.57),
this potential moderating factor may not have been sufficiently robust to
yield a detectable effect in our analysis.

4.2. Dysregulated response to stress and time perception ?

The primary finding of the current meta-analysis, which demon-
strates time compression in individuals experiencing acute stress situa-
tion, aligns with clinical reports from individuals exposed to traumatic
events—considered extreme stressors. Impaired time perception is a
well-documented dissociative symptom (Vancappel et al., 2023), and
from a clinical perspective, changes in the sense of time (e.g., "It felt as if
my sexual assault lasted an eternity") are generally reported by the
victims. Impaired time perception is also reported in several
stress-related psychopathologies, including post-traumatic stress disorder (PTSD (Ahmadi et al., 2019; Vicario and Felmingham, 2018)), borderline personality disorder (BPD (Berlin and Rolls, 2004)), and disorders within the dissociative spectrum (Lanius et al., 2012). In a controlled study, participants with PTSD were compared to healthy controls, and time perception was assessed. The authors of the study noted that individuals with PTSD demonstrated impaired time perception, with time estimations that were significantly larger than those of the control group. These findings are consistent with the results of our own analysis (Ahmadi et al., 2019).

As noted above, impaired time perception is closely associated with dissociative symptoms observed in both acute stress disorder (ASD) and PTSD. Time perception offers valuable potential for advancing research and clinical applications in this area. First, in both conditions, phase-oriented approaches are commonly used with the aim of stabilizing trauma symptoms prior to exposure-based trauma-focused treatment (Willis et al., 2023). The stabilization phase often relies on psycho-education and emotional stabilization tools to help patients control episodes of emotional distress. Although there are many such tools, they are based on three main mechanisms: light physical activity, activation of the senses (sight, hearing, smell...), saturation of the mind with a pleasant and relaxing imagery exercise. As the adherence to the emotional stabilization tools is a recurrent challenge in the stabilization phase, such tools cultivating a better temporal awareness could improve the adherence thanks to their entertaining character. Moreover, strengthening the temporal awareness might serve to mitigate chronic time dilation, potentially leading to a reduction in PTSD symptoms (Treitel and Levy-Gigi, 2024). Secondly, in the last decade there has been increasing research exploring the clinical potential of neurofeed-back in PTSD. This is a physiological intervention that measures the patient's ongoing cerebral activity (the input), which is then presented in real time using a simple stimulus (the output: usually a visual or auditory cue) as feedback. Through operant conditioning, the patient learns to produce the desired brain activity. The results of neurofeed-back studies in the treatment of PTSD have been encouraging (Askovic et al., 2023). However, published studies of neurofeedback in PTSD have often been designed with either an electrophysiological input (such as modulation of alpha rhythm (Du Bois et al., 2021; Leem et al., 2020) or an input reflecting connectivity within the default mode network (DMN) and salience network (SN) (Bell et al., 2019), which cannot be easily controlled by patients. Time perception may offer interesting perspectives on this topic. Indeed, as discussed above, stress may affect time perception by activating the left anterior insular cortex (AIC), which is also involved in modulating the activity of the SNS. Furthermore, neurocircuit models suggest that PTSD involves hyperreactivity of brain regions that mediate fear expression and emotional responsiveness, such as AIC (Etkin and Wager, 2007; Milad et al., 2007; Rauch et al., 2017), and the severity of PTSD symptoms would correlate with the activity of the AIC (Park et al., 2022). Thus, the activity of the AIC could be a relevant input for a protocol involving neurofeedback in a PTSD sample, and time perception appears to be a promising output to modulate it. In addition, an input based on time perception would certainly be more ergonomic and would then facilitate patient adherence to neurofeedback.

### 4.3. Limits to experiment time perception in the lab

Among the fifteen studies included in this meta-analysis, nine reported a compression effect of stress, six reported either an opposite effect or no effect of stress on time perception. This discrepancy may be attributable to several factors. Firstly, in the majority of studies, experimental designs incorporated a quantitative assessment of induced stress levels, which represents a methodological strength of this meta-analysis. However, it is important to note that biological responses to acute stress exhibit considerable variability (Zänkert et al., 2019). In certain studies, or among specific participants, the level of induced stress

may not have been sufficiently high to influence time perception. Secondly, while physiological arousal and ANS activation play a crucial role in time perception, cognitive functions such as memory and attention also contribute to temporal processing (Matthews and Meck, 2016). In this meta-analysis, stress tasks primarily involved either physical stressors or standardized stress paradigms. In a substantial number of studies where no overestimation of duration was observed, it is reasonable to hypothesize that the stress task did not sufficiently challenge participants (Cocenas-Silva et al., 2012; Hancock, 1993; Sarigiannidis et al., 2020). Conversely, tasks such as the Trier Social Stress Test (TSST) likely engaged these cognitive resources to a greater extent.

Another methodological factor should be considered. The time intervals tested in the included studies ranged from 0.0 s to 10.0 s in 11 studies; 2 studies tested time intervals longer than 60 s (Zlomuzica et al., 2022). Notably, 6 trials tested sub-second time intervals. Thus, there were some discrepancies in the time intervals tested between studies, and this may have affected the influence of stress, as the mechanisms involved in time perception appear to differ depending on the time interval being assessed. Notably, in most studies examining longer time intervals (≥10 s), the effect of stress on time perception was especially consistent with the results of our meta-analysis (Hancock, 1993; Ozel et al., 2004; Tamm et al., 2014). Some studies have reported that there may be two different temporal mechanisms involved in processing below and above 1-s time intervals (Rioux et al., 2023). Indeed, two neural circuits appear to be involved in processing below and above 1-s time intervals (Gooch et al., 2011; Murai and Yotsumoto, 2016; Thibault et al., 2025, 2023). Subsecond time intervals activate the motor system, primary motor area and primary somatosensory area, whereas suprasecond time intervals activate the prefrontal cortex, posterior parietal cortex and basal ganglia (Lewis and Miall, 2006). In addition, similar differences have been reported between the processing of subminute and supraminute time intervals. The processing of short durations may rely on attention to a greater degree, whereas the processing of long durations would rely more on memory-based mechanism (especially when the time perception task is retrospective (Droit-Volet et al., 2019). In addition, all but one of the studies included in our review employed a prospective time perception task, making it impossible to analyze or compare the effects of stress between prospective and retrospective tasks. Regarding sociodemographic factors, particularly age and sex, our findings diverge from those of previous research. Specifically, we did not observe an age-related effect of stress on time perception. It is well established that the HPA axis is strongly impacted by age at both central and peripheral levels (Veldhuis et al., 2013). One potential consequence of this phenomenon is that older individuals tend to underestimate time (Droit-Volet, 2019; Lamotte and Droit-Volet, 2017; Mioni et al., 2020; Wittmann and Lehnhoff, 2005). This phenomenon may be attributed not only to a slowdown in the internal pacemaker rate but also to changes in attentional resources and working memory capacity (Mioni et al., 2020). However, in the present meta-analysis, the participant sample was relatively young and homogeneous in age (mean = 23.59 years, SD = 2.45), resulting in limited inter-individual variability. This homogeneity likely reduced the statistical power needed to detect any moderating effect of age.

Notably, the type of perceptual task was not a moderator in the results of our meta-analysis. On the one hand, this would imply that, at a quantitative level, the changes in time perception under stress tap into cognitive or neurological mechanisms deeply involved in human timing, influencing outcomes in various experimental tasks, and across a wide range of target durations, opening up interesting research perspectives. On the other hand, one might have expected that the studies designed with a time bisection task would have moderated the results of our analysis. Indeed, there is still no consensus on the cognitive functions most relied upon by this task. Some authors suggest that the effects observed in this type of task depend on how the standard durations (or anchors) are remembered, while others suggest that this would rather depend on how participants experience of the comparison directions
(Vatakis et al., 2018).

As previously mentioned, the present study exclusively included research that induced stress through physical stressors (e.g., noise, heat, pain, cold) or a validated stress paradigm, such as the TSST. While this approach resulted in the exclusion of numerous relevant studies demonstrating the effects of negative emotions on time perception (Anderson et al., 2007; Dirnberger et al., 2012; Vallet et al., 2019; Watts and Sharrock, 1984), it helped to maintain methodological consistency. The incorporation in our meta-analysis of a substantial number of studies encompassing diverse cognitive tasks would have resulted in a considerable augmentation of variability across studies and in the introduction of additional confounding biases.

## Role of the funding source

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

## Disclosure statement

No potential competing interest was reported by the authors.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.neubiorev.2025.106382.

## Data availability

Data will be made available on request.

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