# Movie editing influences spectators' time perception

Klara Kovarski, Joanna dos Reis, Claire Chevais, Anaïs Hamel, Dominique

Makowski, Marco Sperduti

► To cite this version:

Klara Kovarski, Joanna dos Reis, Claire Chevais, Anaïs Hamel, Dominique Makowski, et al.. Movie editing influences spectators' time perception. Scientific Reports, 2022, 12, 10.1038/s41598-022-23992-2. [https://doi.org/10.1038/s41598-022-23992-2](https://doi.org/10.1038/s41598-022-23992-2)

HAL Id: hal-04004294

https://u-paris.hal.science/hal-04004294

Submitted on 24 Feb 2023

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

Public Domain
<figure><img src="image_1.png" /></figure>

Check for updates

OPEN

# Movie editing influences spectators' time perception

Klara Kovarski<fnref n="1" />,²,³,⁴✉, Joanna Dos Reis⁵, Claire Chevais⁵, Anaïs Hamel⁶,⁷, Dominique Makowski⁸ & Marco Sperduti⁵✉

Filmmakers use different techniques (e.g., camera movements, editing) to shape viewers' experience. In particular, editing can be used to handle the temporal unfolding of events represented in a movie. Nevertheless, little is known about how different editing types impact viewers' time perception. In an exploratory on-line study (90 participants) and a pre-registered conceptual replication study (60 participants), we asked participants to judge (Study 1) or reproduce (Study 2) the duration of 45 excerpts of the movie "Le Ballon Rouge" containing either continuous editing, action discontinuity editing or no editing. Each excerpt was formatted in three durations (2000, 2500 or 3000 ms). In both studies, we reported that scenes containing continuous editing were perceived as longer than the other two scene types. Moreover, scenes containing action discontinuity editing were perceived as longer than scenes with no editing. This study contributes to the emerging field of psycho-cinematics which could ultimately develop the dialog between arts and science.

The dialogue between arts and psychology has gained attention, by enhancing the understanding of how artistic objects are processed and perceived and how artists influence and create specific perceptual phenomena<fnref n="1" />. Concurrently, using artistic objects as stimuli also allows better apprehending of human behavior and brain functioning. Among others, this dual interest in this new interdisciplinary field has been flourishing in developing the relation between cinema and cognitive science. Films can be used by researchers as complex stimuli to study several cognitive processes such as natural vision² or memory formation for complex material³. In particular, studying spectators' cinematic experience could lead to crucial understanding on perceptual processes, since film-makers have often developed a theory of perception through their intuition and a true experimental approach, a paradigmatic example of which is the Koulechov effect. This editing effect consists in attributing an emotional or a motivational content to a neutral face followed by another photograph. This effect is surprisingly close in psychology to priming, and has recently been exploited and reproduced in neuroscientific studies, suggesting that editing can be used to investigate several perceptual processes⁴.

More than other forms of art, cinema deals with time and duration perception⁵. Among other cinema language rules, editing rules have been established to assure syntactic and semantic continuity, but also to deal with rhythm (i.e. pacing). The most current editing type is the cut⁶, consisting in the juxtaposition of shots (i.e. montage), leading to perceptual discontinuity and the possible detection of an event. Continuity editing allows maintaining a continuous and narrative action⁶. To provide this effect, shots are matched across boundaries, along perceptual features, spatiotemporal relations, and actions.

Interestingly, empirical studies have shown that cuts following continuity editing rules pass unnoticed between a quarter and a third of the time, a phenomenon named "edit blindness"⁷. This effect seems to be driven by the modulation of the attentional dynamic induced⁸,⁹ by cuts toward the narrative, and away from low-level perceptual information¹⁰. In addition to editing rules, different styles, varying in the density of cuts and length of shots, are observed in cinema language. We will use the terms of editing or cut types to refer to the rules used for juxtaposing shots (e.g. continuous editing), and editing styles for rules concerning the organization of the cuts (or their absence) in the movie (e.g. action movies, continuous take). Editing techniques are specifically

<footnote n="1">¹Hôpital Fondation Rothschild, Paris, France. ²Integrative Neuroscience and Cognition Center-CNRS, Université Paris Cité, Paris, France. ³Sorbonne Université, INSPE, Paris, France. ⁴LaPsyDÉ, Université Paris Cité, CNRS, Paris, France. ⁵Laboratoire Mémoire, Cerveau and Cognition, (LMC2 UPR 7536), Institut de Psychologie, Université Paris Cité, 92100 Boulogne-Billancourt, France. ⁶Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Neuropresage Team, Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France. ⁷UR2NF-Neuropsychology and Functional Neuroimaging Research Unit at CRCN-Center for Research in Cognition and Neurosciences and UNI - ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Brussels, Belgium. ⁸Clinical Brain Lab, Nanyang Technological University, Singapore, Singapore. ⁹email: klara.kovarski@u-paris.fr; marco.sperduti@u-paris.fr</footnote>
employed to handle the discrepancy between the temporal unfolding inside the narrative (diegetic time) and the real duration of the narrative¹¹.

Surprisingly, to our knowledge, there is only one published study investigating how editing style modulates spectators' time perception. Balzarotti, et al.¹², asked participants to judge the duration of different video clips representing goal-directed routine actions. The authors manipulated the editing density (number of cuts) of video clips to obtain three different versions: a master shot (without cuts), a slow-paced editing (5 cuts), and a fast-paced (10–12 cuts). They showed that the duration of fast-paced video was overestimated compared to the master shot. It is important to note that both slow- and fast-paced video were created respecting continuity editing rules. Nevertheless, different kinds of cuts could differently affect cognitive processes and consequently time perception.

Magliano and Zacks¹³ investigated how different degrees of continuity/discontinuity at editing points modulate how individuals perceive and segment events while watching a film (Le Ballon Rouge by Lamorisse, 1956). In particular, they identified three types of editing: edits that are continuous in space, time, and action; edits that are discontinuous in space or time but continuous in action; and edits that are discontinuous in space and time as well as in action. Cuts associated with action discontinuity more robustly predicted the probability of detecting an event boundary. Event Segmentation Theory¹⁴,¹⁵ predicts that an event boundary is detected when perceptual predictions based on the actual active event model (a working memory representation of what is occurring in a given situation) are no longer accurate. At this point, a gating mechanism transiently increases the bottom-up influence of sensory information over the event model to up-date it. Thus, event segmentation implements an attentional mechanism that is meant to efficiently allocate cognitive resources over time to bias perceptual processing of pertinent information¹⁵.

Crucially, attention is a central feature of classical cognitive models of time perception¹⁶⁻¹⁸. Briefly, these models put forward the existence of an internal clock composed by a pacemaker, an accumulator, and a gating system signaling to the accumulator that a relevant event is taking place. Arousal is supposed to accelerate the production of pulses of the pacemaker, so that higher arousal corresponds to longer perceived durations, while attention is postulated to drive the gating mechanism¹⁹. When attention is focused on temporal information, the gate closes and more pulses are stored in the accumulator. As a result, perceived duration is longer. This is particularly true when participants are informed in advance that they have to perform a timing task (prospective timing), since they can allocate more attentional resources in processing temporal information. On the contrary, in retrospective timing, participants incidentally encode temporal information since they are only informed of the timing task once the stimulus has ended. In this situation, time perception mostly relies on memory processes²⁰⁻²². As we have seen before, editing has been shown to modulate attentional allocation, and different kinds of cuts are likely to modulate attentional processes in various manners. Thus, it is likely that cuts signaling a transition between scenes (action discontinuity), and cuts maintaining continuity would have a differential impact on time perception. In particular, cuts signaling an event boundary, like action discontinuity, could transiently increases attention toward visual sensory information and likely divert attention from time processing. In this case, we should predict an underestimation of duration, since temporal information is lost. Nevertheless, there are studies reporting that scenes containing more events are perceived as longer²³,²⁴. As we have seen, action discontinuities are robust indicators of events boundaries, so we should predict that scenes containing discontinuities should be judged as longer. To our knowledge, no previous study has explored the effect of different types of editing on time perception.

We investigated this question in two studies and employed the same material and editing coding as Magliano and Zacks¹³. We used prospective timing tasks in both studies. Participants were asked to watch short sequences (2500, 3000 and 3500 ms) that either contain no cut, a cut maintaining continuity or a cut introducing discontinuity in time, space and action. Duration was only manipulated to operationalize the timing task. Indeed, always presenting the same duration would most likely have biased participants' responses. Moreover, this manipulation allowed us to check participant accuracy in timing (this was particularly important in the first study conducted online). We did not have any specific hypothesis on the interaction between the two factors (duration and cut type). Participants had to estimate (Study 1) the duration of the scene in an exploratory study conducted online (during the Covid lockdown) or reproduce (Study 2) these durations. They were also asked to judge, after each scene, their level of arousal. Indeed, arousal has been previously proposed to modulate the pulse rate of the pacemaker, and therefore to influence time perception²⁵. Finally, as a proxy measure of attentional engagement while watching the excerpts, we introduced a surprise recognition test of snapshots taken from the videos. In Study 1, these two measures (arousal and memory for the snapshots) were only taken as control measures. In particular, given that Study 1 was performed online, the memory measure was added to ensure that participants paid attention, and were able to recognize snapshots extracted from the clips. Thus, no difference between cut conditions was expected on these measures. Moreover, given the contrasting predictions that can be made, particularly on cuts introducing action discontinuities, we did not have any directional hypothesis concerning time estimation in Study 1. In the second pre-registered conceptual replication study conducted in a laboratory setting, we formulated three directional hypotheses based on the results of the Study 1: i) Continuous clips will be associated with longer perceived duration; ii) Continuous clips will be associated with higher levels of self-reported arousal, compared to clips with discontinuities or no editing; 3) Images from continuous clips will be associated with more correct recognitions, followed by images from discontinuous clips, which will be better recognized than images from unedited clips.
## Results

In the following, we report results found with repeated measures ANOVAs from Study 1 and 2 in different sections. In a separate final section, complementary analyses issued from mixed models from both studies are reported.

**Study 1. Data analysis.** We computed the ‘time estimation error’ by dividing the difference between the time estimation and the real duration by the real duration in milliseconds. Positive value reflects overestimation, while negative values reflect underestimation of the real duration.

We performed repeated-measures ANOVAs on the raw estimation, the time estimation error, and the arousal level with Duration (2500, 3000, and 3500 ms) and Editing type (continuous editing, action discontinuity, and no cut) as factors. A repeated measures ANOVA was also performed for the ratio of correctly recognized images in the recognition task, with Editing type as a factor.

The metric used for effect size is partial eta-squared ($\eta_p^2$) for ANOVA, and Cohen's *d* for post-hoc. Post-hoc analyses were conducted using Holm's correction.

Data preprocessing was carried out with R²⁶ running in R Studio²⁷. Inferential statistics were conducted using JASP²⁸.

*Raw estimation.* There was a significant main effect of Duration, $F(2, 178) = 230.62, p < 0.001, \eta_p^2 = 0.72$. Post-hoc analysis showed that all differences were significant at $p < 0.001$, in particular scenes lasting 2500 ms (marginal mean 2388.31, 95% CI: 2333.81, 2442.81 were judged shorter than scenes lasting 3000 ms (marginal mean 2612.02, 95% CI: 2557.51, 2666.52, Cohen's *d* = 1.54) and 3500 ms (marginal mean 2822.89, 95% CI: 2768.39, 2877.39, Cohen's *d* = 1.81). Scenes lasting 3000 ms were judged shorter than those lasting 3500 ms (Cohen's *d* = -1.18).

There was also a significant main effect of Editing type, $F(2, 178) = 15.83, p < 0.001, \eta_p^2 = 0.15$. Post-hoc analysis showed that scenes containing continuous editing (marginal mean 2647.74, 95% CI: 2595.46, 2700.02) were judged longer than both scenes containing action discontinuity (marginal mean 2611.11, 95% CI: 2558.83, 2663.39, *p* = 0.008, Cohen's *d* = 0.29) and no cut (marginal mean 2564.36, 95% CI: 2512.09, 2616.64, *p* < 0.001, Cohen's *d* = 0.57). Scenes containing action discontinuity were judged longer than scenes containing no cut (*p* = 0.008, Cohen's *d* = -0.31).

The interaction between the two factors was not significant, $F(4, 356) = 1.06, p = 0.37, \eta_p^2 = 0.01$.

*Time estimation error.* There was a significant main effect of Duration, $F(2, 178) = 296.29, p < 0.001, \eta_p^2 = 0.77$. Post-hoc analysis showed that all differences were significant at $p < 0.001$. Scenes lasting 2500 ms (marginal mean -0.04, 95% CI: -0.06, -0.03) were associated with a smaller estimation error than scenes lasting 3000 ms (marginal mean -0.13, 95% CI: -0.15, -0.11, Cohen's *d* = 1.71) and 3500 ms (marginal mean -0.19, 95% CI: -0.21, -0.18, Cohen's *d* = 2.10). Scenes lasting 3000 ms were associated with a smaller estimation error than those lasting 3500 ms (Cohen's *d* = 1.24).

There was also a significant main effect of Editing type, $F(2, 178) = 16.39, p < 0.001, \eta_p^2 = 0.16$. Post-hoc analysis showed that scenes containing continuous editing (marginal mean -0.11, 95% CI: -0.13, -0.09) were associated with a smaller estimation error than scenes containing action discontinuity (marginal mean -0.12, 95% CI: -0.14, -0.10, *p* = 0.005, Cohen's *d* = 0.33) and no cut (marginal mean -0.14, 95% CI: -0.15, -0.12, *p* < 0.001, Cohen's *d* = 0.58). Scenes containing action discontinuity were associated with a smaller estimation error than those containing no cut (*p* = 0.006, Cohen's *d* = -0.29).

The interaction between the two factors was not significant, $F(4, 356) = 1.39, p = 0.24, \eta_p^2 = 0.01$.

*Arousal.* There was a significant main effect of Duration, $F(2, 178) = 5.39, p = 0.005, \eta_p^2 = 0.06$. Post-hoc analysis showed that scenes lasting 2500 ms (marginal mean 3.19, 95% CI: 2.86, 3.51) were associated with lower reported arousal than scenes lasting 3000 ms (marginal mean 3.31, 95% CI: 2.99, 3.64, *p* = 0.010, Cohen's *d* = -0.32) and 3500 ms (marginal mean 3.33, 95% CI: 3.36, *p* = 0.02, Cohen's *d* = -0.28). The difference between scenes lasting 3000 and 3500 ms was not significant (*p* = 0.68, Cohen's *d* = -0.04).

There was also a significant main effect of Editing type, $F(2, 178) = 18.16, p < 0.001, \eta_p^2 = 0.17$. Post-hoc analysis showed that scenes containing continuous editing (marginal mean 3.42, 95% CI: 3.10, 3.75) were associated with higher reported arousal than scenes containing action discontinuity (marginal mean 3.20, 95% CI: 2.88, 3.52, *p* < 0.001, Cohen's *d* = 0.48) and no cut (marginal mean 3.21, 95% CI: 2.88, 3.53, *p* < 0.001, Cohen's *d* = 0.58). Scenes containing action discontinuity and no cut did not differ (*p* = 0.87, Cohen's *d* = 0.02).

The interaction between the two factors was not significant, $F(4, 356) = 1.21, p = 0.3, \eta_p^2 = 0.01$.

*Recognition.* There was a significant main effect of Editing type, $F(2, 178) = 22.92, p < 0.001, \eta_p^2 = 0.2$. Post-hoc analysis showed that images extracted from scenes containing no cut (mean = 0.74, SD = 0.16) were better recognized than both images extracted from scenes containing continuous editing (mean = 0.68, SD = 0.18, *p* < 0.001, Cohen's *d* = -0.4) and action discontinuity (mean = 0.62, SD = 0.17, *p* < 0.001, Cohen's *d* = 0.7). Images extracted from scenes containing continuous editing were better recognized than those presenting action discontinuity (*p* = 0.003, Cohen's *d* = 0.32).

**Study 2. Data analysis.** We computed the ‘time reproduction error’ by dividing the difference between the time reproduction and the real duration by the real duration in milliseconds. Positive value reflects overestimation.
tion, while negative values reflect underestimation of the real duration. The analysis carried out for this study was identical to Study 1.

**Raw reproduction.** There was a significant main effect of Duration, $F(2, 118) = 126.07, p < 0.001, \eta_p^2 = 0.68$. Post-hoc analysis showed that all differences were significant at $p < 0.001$. In particular, scenes lasting 2500 ms (marginal mean 2587.78, 95% CI: 2366.80, 2808.75) were reproduced shorter than scenes lasting 3000 ms (marginal mean 2910.70, 95% CI: 2689.72, 3131.67, Cohen's $d = -1.07$) and 3500 ms (marginal mean 3294.86, 95% CI: 3073.88, 3515.83, Cohen's $d = -1.98$). Scenes lasting 3000 ms were reproduced shorter than those lasting 3500 ms (Cohen's $d = -1.03$).

There was also a significant main effect of Editing type, $F(2, 118) = 15.04, p < 0.001, \eta_p^2 = 0.2$. Post-hoc analysis showed that scenes containing continuous editing (marginal mean 3030.49, 95% CI: 2811.05, 3249.94) were reproduced longer than both scenes containing action discontinuity (marginal mean 2939.93, 95% CI: 2720.48, 3159.37, $p = 0.011$, Cohen's $d = 0.31$) and no cut (marginal mean 2822.91, 95% CI: 2603.46, 3042.35, $p < 0.001$, Cohen's $d = 0.76$). Scenes containing action discontinuity were judged longer than scenes containing no cut ($p = 0.011$, Cohen's $d = -0.37$).

The interaction between the two factors was not significant, $F(4, 236) = 0.38, p = 0.82, \eta_p^2 = 0.006$.

**Time reproduction error.** There was a significant main effect of Duration, $F(2, 118) = 21.59, p < 0.001, \eta_p^2 = 0.27$. Post-hoc analysis showed that scenes lasting 2500 ms (marginal mean $-0.03$, 95% CI: $-0.04, 0.11$) were associated with a positive reproduction error that differed significantly from scenes lasting 3000 ms (marginal mean $-0.03$, 95% CI: $-0.10, 0.04, p < 0.001$, Cohen's $d = 0.56$) and 3500 ms (marginal mean $-0.06$, 95% CI: $-0.13, 0.02, p < 0.001$, Cohen's $d = 0.81$). Scenes lasting 3000 ms were associated with a smaller estimation error than those lasting 3500 ms ($p = 0.44$, Cohen's $d = 0.26$).

There was also a significant main effect of Editing type, $F(2, 118) = 16.52, p < 0.001, \eta_p^2 = 0.22$. Post-hoc analysis showed that scenes containing continuous editing (marginal mean 0.02, 95% CI: $-0.06, 0.09$) were associated with a more positive reproduction error than scenes containing action discontinuity (marginal mean $-0.02$, 95% CI: $-0.09, 0.06, p = 0.011$, Cohen's $d = 0.34$) and no cut (marginal mean $-0.05, 0.95$% CI: $-0.13, 0.02, p < 0.001$, Cohen's $d = 0.79$). Scenes containing action discontinuity were associated with a smaller negative reproduction error than those containing no cut ($p = 0.010$, Cohen's $d = -0.38$).

The interaction between the two factors was not significant, $F(4, 236) = 0.72, p = 0.58, \eta_p^2 = 0.01$.

**Arousal.** There was a significant main effect of Editing type, $F(2, 118) = 12.28, p < 0.001, \eta_p^2 = 0.17$. Post-hoc analysis showed that scenes containing continuous editing (marginal mean 3.08, 95% CI: 2.70, 3.455) were associated with higher reported arousal than scenes containing action discontinuity (marginal mean 2.93, 95% CI: 2.55, 3.30, $p = 0.005$, Cohen's $d = 0.41$) and no cut (marginal mean 2.88, 95% CI: 2.51, 3.25, $p < 0.001$, Cohen's $d = 0.61$). Scenes containing action discontinuity and no cut did not differ ($p = 0.17$, Cohen's $d = -0.18$).

The main effect of Duration, $F(2, 118) = 0.84, p = 0.43, \eta_p^2 = 0.01$, and the interaction between the two factors were not significant, $F(4, 236) = 0.74, p = 0.57, \eta_p^2 = 0.01$.

**Recognition.** There was a significant main effect of Editing type, $F(2, 118) = 3.17, p = 0.046, \eta_p^2 = 0.05$. Post-hoc analysis do not reveal any significant differences. Images extracted from scenes containing no cut (mean = 0.74, SD = 0.15) were not better recognized than both images extracted from scenes containing continuous editing (mean = 0.71, SD = 0.13, $p = 0.29$, Cohen's $d = -0.19$) and action discontinuity (mean = 0.69, SD = 0.17, $p = 0.06$, Cohen's $d = 0.31$). Images extracted from scenes containing continuous editing were not better recognized than those presenting action discontinuity ($p = 0.29$, Cohen's $d = 0.14$).

**Complementary analyses: mixed models.** Analysis was carried out using R 4.2<sup>29</sup>, using the glmmTMB (Brooks and et al., 2017) and easystats packages<sup>30-32</sup>. We report here only the main results on time estimation error (judgement and reproduction, respectively for Study 1 and 2; see Fig. 1), arousal, and recognition. Complete results are available here: https://osf.io/eb6r9/files/osfstorage.

**Time estimation error.** Time estimation error was modelled using linear mixed regressions with participants and items set as random factors, and Editing type (continuous editing, action discontinuity, and no cut) as fixed factor. In Study 1, marginal contrast analysis revealed that continuous editing was related to longer reported durations as compared to the control condition (marginal difference = $-0.03$, 95% CI: $-0.06, 0.00, p = 0.049$). The difference between action discontinuity and continuous editing was not significant (marginal difference = 0.02, 95% CI: $-0.01, 0.04, p = 0.298$).

In Study 2, marginal contrast analysis revealed that continuous editing was related to longer reported durations compared to the control condition (marginal difference = $-0.07$, 95% CI: $-0.10, -0.04, p < 0.001$), and that the difference between action discontinuity and continuous editing was also significant (marginal difference = 0.04, 95% CI: 0.01, 0.08, $p = 0.01$).

**Arousal.** In Study 1, adding the subjective arousal as predictor confirmed that continuous editing was related to longer reported durations as compared to the control condition (marginal difference = 0.04, 95% CI: 0.05, 0.07, $p = 0.023$). Moreover, this model revealed a positive and significant effect on the time estimation error in the control condition (beta = 0.01, 95% CI: 0.00, 0.02, $p < 0.001$), The interaction with continuous editing condi-
<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /><figcaption>**Figure 1.** Time estimation error for the three editing types. The picture represents the marginal means (estimated by mixed models) with data distributions and data points. Error bars represents 95% CI. The confidence intervals of marginal differences between conditions are reported between the conditions of interest.
\*p*<0.05, \*\*\*p*<0.001.</figcaption></figure>

tion (beta = -0.004, 95% CI: -0.009, 0.002, *p* = 0.161), and action discontinuity condition (beta = -0.004, 95% CI: -0.009, 0.002, *p* = 0.007) was not significant, showing a similar effect of arousal in all conditions.

In Study 2, adding the subjective arousal as predictor confirmed that continuous editing was related to longer reproduced durations as compared to the control condition (marginal difference = 0.07, 95% CI: 0.02, 0.12, *p* = 0.004). Moreover, the model showed that there was no effect of arousal in the control condition (beta = -0.005, 95% CI: -0.02, 0.00, *p* = 0.351), nor an interaction with the continuous editing (beta = -0.001, 95% CI: -0.01, 0.01, *p* = 0.884) or the action discontinuity condition (beta = 0.005, 95% CI: -0.003, 0.02, *p* = 0.451), showing no link between arousal and time reproduction.

*Recognition.* Logistic mixed models suggested that items presented in the action discontinuity condition were less recognized compared to the control condition in Study 1 (marginal difference = -0.60, 95% CI: -0.01, -1.20, *p* = 0.046). No other effects were significant. No significant differences emerged in Study 2.

Discussion

The main goal of the two present studies was to test whether different types of cuts, in particular cuts representing
action continuity within a scene and cuts leading to discontinuity, differentially modulate spectators' time percep-
tion. The results of the two studies were coherent, with results of Study 1 replicated by Study 2 (pre-registered).
Overall, the results showed that video excerpts containing both types of editing were perceived as longer than
those containing no cuts. Moreover, scenes containing continuous cuts were also perceived as longer than those
presenting discontinuity. This finding was also replicated by more stringent complementary analysis using mixed
models, thus confirming our first preregistered hypothesis. We also reported that videos with continuous editing
were judged as more arousing than the other two kinds of scenes, and that snapshots extracted from unedited
scenes were in general better recognized than those taken after cuts in both continuous and discontinuous edit-
ing, even if these last results were less robust across the two studies (no differences were reported in Study 2).

The finding that scenes containing editing were perceived as longer that those without cuts are partially in line
with the results of Balzarotti et al.¹² showing that the density of editing (number of cuts) was associated with an
overestimation of video duration compared with unedited video. Nevertheless, they only reported a significant
difference between fast-paced editing (10–12 cuts) and unedited scenes, but the difference between the latter and
slow-paced (5 cuts) editing was not significant. On the contrary, here we reported significant differences between
scenes containing only one cut and those with no cut. Of note is that in the study by Balzarotti et al.¹², the differ-
ence between slow-paced and unedited scenes went in the same direction and approached significance (*p* = 0.056),
suggesting that the null results could be due to low statistical power. In any case, the comparison between the
two studies is not straightforward due to methodological differences. For example, in Balzarotti et al.¹² durations
were in the order of tenth of seconds (11–13.5 s), while we used durations between 2500–3500 ms. Our results are coherent with recent proposals that tracking changes in low-level perceptual processing provides a basis for human time perception³³,³⁴, in particular with results showing that naturalistic videos with greater perceptual change were estimated as longer in duration³³. Indeed, even if we did not directly measure perceptual change, a cut introduces an abrupt change in the visual information flow by definition.

If perceptual changes would solely predict time perception, we should expect perceived longer duration for discontinuous scenes. According to Magliano and Zacks¹³, a stimulus-driven increase of processing and an attention-driven modulation mechanism operate at points of discontinuity corresponding to cuts. Continuous editing would be expected to produce larger stimulus-driven increases in processing that may contribute to bridging the perceptual discontinuities to maintain continuity. Crucially, this process would be superfluous for discontinuity cuts as they signal a major scene change. In this case, top-down attentional down-regulation would suppress the additional processing driven by the presentation of new visual information. This kind of pattern of activity, hyper-activation for continuous and de-activation for discontinuous cut, was reported in a set of brain regions encompassing the non-primary visual cortex, inferotemporal cortex, and parietal cortex¹³. We speculatively argue that this additional neural activity could explain the results we reported here. Further neuroimaging studies are needed to confirm this hypothesis.

In the present study continuous editing was associated with higher self-reported arousal, compared to the other two conditions, confirming our second preregistered hypothesis. This finding resembles the pattern of results for the perceived duration, and is coherent with the literature showing that higher arousal is associated with longer perceived duration³⁵. Nevertheless, we believe that this result should be taken with caution. Indeed, even if scenes with continuous editing were judged to be more arousing, their overall level of arousal is quite low in both studies (on average 3.42 and 3.08 in Study 1 and 2 respectively on a scale ranging from 1 to 9). Even if complementary analysis (mixed models) showed that higher arousal predicted longer perceived durations, this was true in all conditions. Additionally, the difference in perceived time between continuous cut and the control condition remained significant in this model, suggesting that the effect of cut types was not explained by arousal. Moreover, the link between arousal and perceived duration was only significant in Study 1. Taken together, these results are coherent with recent findings questioning the role of arousal in time perception, above all for complex visual stimuli. Indeed, Suárez-Pinilla, et al.³³ did not find any relationship between autonomic response (heart rate) and duration estimation when participants observed naturalistic video stimuli.

Finally, qualitatively we reported a similar pattern of results in both studies concerning memory performance that we meant as a proxy measure of attention while watching videos. Snapshots extracted from unedited scenes were better recognized than those extracted after cuts, even if this effect failed to reach significance in the second study. Mixed models confirmed that the probability of recognizing a snapshot was significantly lower in the discontinuity editing compared to the control condition only in the first study. No difference emerged in the second study. For the first study, the results are likely due to the choice of the material and did not reflect the general attentional engagement during viewing. Indeed, we chose to take snapshots for the recognition task just after the cut (1 frame in the Study 1). Shimamura, et al.¹⁰ showed that detection accuracy of targets (asterisk) presented during a video clip decreased when the target was presented just after an edit, compared with a target presented before the edit or in the middle of a shot. This result suggests that cuts could temporarily capture attention and disrupt processing and subsequent encoding of information presented in close succession. This explanation is corroborated by the absence of differences in Study 2, in which we choose, for the recognition task, snapshot that were more shifted in time from the cut (5–10 frames). Future studies should combine more direct behavioral and neurophysiological measures of attention to investigate the interplay between editing, attentional regulation and time perception.

To resume, editing modulates time perception, leading to longer perceived duration compared to unedited videos. In particular, cuts allowing narrative continuity, despite perceptual discontinuity, showed the strongest effect on time perception. Editing is a series of techniques that impose a structure on a visual flow of information to allow narrative coherence and facilitate comprehension. This seems to be exactly what our perceptual system does in order to deal with the continuous stream of information. Indeed, following the Event Segmentation Theory, the perceptual system automatically parses in time the flow of information in discrete events¹⁴,¹⁵. This facilitates the perceptual and cognitive organization of information, allowing the system to efficiently and transiently allocate attentional resources to pertinent information, to structure it and make sense of it, resulting in a mnemonic advantage for information that has been properly segmented. Despite the numerous studies investigating the impact of event segmentation on attentional and memory processes, a surprising few investigate the impact of segmentation on time perception²³,²⁴,³⁶. In an initial study, Bangert, et al.²³ asked participants to reproduce a previously encoded duration while watching naturalistic videos containing many (eventful), few (uneventful) event boundaries or a blank screen. The authors showed that reproduction was shorter for eventful videos compared to the other two conditions, and for uneventful videos compared to the blank condition. Similar results were found by Fenerci, et al.²⁴ reporting that videos containing an single event boundary (spatial shift) were reproduced as shorter than target durations, compared to videos not containing boundaries (steady-cam shot), and that the duration of the same videos was retrospectively judged as longer (see also³⁷). Taken together, these results suggest that the number of events (spatial shifts or cuts) makes participants perceive that more time has passed. Even if these findings are generally coherent with our main results that scenes containing cuts are perceived as longer than scenes without editing, one would expect that clips containing discontinuous editing would be perceived as longer than those containing continuous cuts. Indeed, as shown by Magliano and Zacks¹³, discontinuous editing, contrary to continuous cuts, is a robust predictor of event perception. Nevertheless, it has to be noted that in Bangert, et al.²³ eventfulness was established on participants' behavioral segmentation of scenes of everyday activity not containing cuts. In the same vein, in Fenerci, et al.²⁴ events were defined as a shift in spatial context (e.g., characters moving through doorways). This last situation resembles continuous
editing since temporal and action continuity are maintained. Neither of the two studies contained a condition similar to discontinuous editing. Moreover, making a direct comparison between these studies and our work is not straightforward due to important methodological differences concerning the material, the range of the estimated duration, and the timing task. On the contrary, in a subsequent study, Bangert, et al.³⁶ reported that the duration of test intervals presented across a single event boundary while watching videos was more frequently judged as shorter than a target duration, compared to test intervals presented within an event (the target and the test intervals had always the same duration of 5 s). Discrepancies in the results are likely due to methodological differences including material used, durations, as well as instructions provided to the participants (e.g. reproduction, estimation). Overall, our contribution adds information on how different types of events (visual continuous or discontinuous cut) modulate time perception and gives important insight on how studying movie editing could be pertinent for our understanding of time perception in real life under the Event Segmentation framework. Moreover, the present findings are of interest to better apprehend how film editing, and in general cinema formalism (camera movements etc.), influences time perception by providing valuable clues as to how time perception is impacted by natural phenomena producing similar visual consequences (e.g. eye movements, blinks)³⁸. Finally, this study contributes to the emerging interdisciplinary field of psycho-cinematics which could ultimately develop the dialog between arts and science¹, by questioning how artists' intuitions could be considered in experimental psychology.

## Methods

**Study 1. Participants.** One hundred and thirty-three participants were recruited during the first Covid-19 lockdown (autumn-spring 2020) among the undergraduate students at the Institute of Psychology at Université Paris Cité using an announcement on the university portal. Participants received class credit for their participation. Participants were not familiar with the film. Forty-three participants were excluded according to the following criteria: reporting a history of neurological or psychiatric disorders (9 participants); having used alcohol or drugs before the experiments (6 participants); having been interrupted during the protocol (16 participants); having already seen the movie (1 participants); having completed the protocol with a duration longer than 1.96 SD (6 participants), having an 'A' on the recognition task equal or inferior to 0.5 (2 participants); being over 40 years old (3 participants). 'A' is a non-parametric index of discriminability of the signal detection theory (SDT). This index is preferred to the parametric index 'd' since it is not dependent on assumptions about the distribution of signal and noise, and is not sensitive to extreme values of Hit and False alarms rates³⁹. The final sample was composed of 90 participants (77.78% females, mean age 20.31 ± 1.85 years).

All participants were informed of the academic nature of the study and accepted that their responses would be processed anonymously. Then, all participants gave written informed consent before carrying out the study. The protocol was carried out in accordance with local ethical standards.

**Material.** We selected excerpts from the movie *Le Ballon Rouge* (Lamorisse, 1956), and extracted 45 unique sequences, 15 in the continuous editing condition, 15 in the action discontinuity editing condition and 15 with no cut, according to the editing coding of Magliano and Zacks¹³. Each sequence was formatted in 3 durations: 2500 ms, 3000 ms, and 3500 ms. For edited clips, the different durations were obtained by manipulating the time window around the time of the cut (e.g. for 2500 ms, we took 1250 ms before and after the cut). For unedited clips, we used the same procedure, but the time window was centered around a reference frame that was hold constant for the three durations (see Fig. 2).

For the recognition task we extracted the image corresponding to the frame following the cut for the sequences containing editing, and the central frame for the sequences without editing. We also extracted 30 images from other sequences of the movie to be used as lures.

The Metacognitive Questionnaire on Time was also employed as an interference task between the time estimation and the recognition task⁴⁰. This task created a retention interval before the recognition task. This questionnaire is composed of 12 items rated on a 5-point Likert scale. In the present study, the scores from this questionnaire were not used further in the analyses.

**Time estimation task.** The task began with a brief synopsis of the movie, followed by instructions. Participants were instructed to try to estimate the duration of each sequence as accurately as possible without using external devices. They were also notified that, after each time estimation, they would be asked to judge their emotional reaction to each sequence.

Each trial started with the presentation of a sequence that was immediately followed by the appearance of a scale ranging from 2000 ms and 4000 ms with intervals of 100 ms. The participants had to use their mouse to select the estimated duration. We used an interval longer than the actual presented durations, since it has been shown that participants tend to avoid using the extremes of a scale, and that this modification can counteract this bias, leading to more precise estimation⁴¹. The participants were asked not to use any temporal support to perform the task, nor to count. Then, participant had to estimate their arousal on a 9-point Likert scale (1-relaxed, 9-excited). Afterwards a new trial began.

The 45 sequences were presented in a randomized order. The association between the editing condition and the duration was counterbalanced across participants so that each sequence was presented in all three durations in the whole sample, but each participant saw a specific sequence once.

**Recognition task.** Participants were instructed that they would see a list of pictures belonging to the sequences presented in the previous task, and other pictures extracted from the same movie, but that were not presented before. They had to indicate if they had already seen the scene represented in the picture or not.
<figure><img src="image_4.png" /><figcaption>**Figure 2.** (A) Example of time course for Study 2. Stimuli were presented after a fixation cross (500 ms) for one of the three durations (2500, 3000 or 3500 ms). Then, an empty screen (500 ms) followed by a question mark (500 ms) were shown before the appearance of a black square at the center of the screen. Participants had to press the spacebar when they estimated that the duration of presentation of the square was equivalent to the duration of the preceding video clip. After the time reproduction, an arousal scale was presented. (B) Examples of edits for each condition. In the continuous condition, two edited sequences show the same action although a change of perspective (a child looking out of the window), while in the action discontinuity condition the edited sequences present two separate actions. In the control condition the were no edits.</figcaption></figure>

Each trial began with the presentation of one picture, and participants had to click on a 'yes' or 'no' button with their mouse. The presentation was self-paced, so that after participants' response, a new trial began. The 45 target pictures and the 30 lures were presented in a randomized order.

*Procedure.* Due to the Covid-19 pandemic the study was conducted completely on-line. The protocol was programmed on Psytoolkit<sup>42,43</sup>. The link to access the original (French) version of the Time Estimation Task is the following: https://osf.io/eb6r9/files/osfstorage.

Participants were requested to pass the experience in a quiet place to avoid distraction. They were informed they would watch different video sequences and they would be asked to estimate their duration, and that they would have to answer some questionnaires.

After having read the protocol description and after accepting to take part in the study, they responded to socio-demographic questions (age, sex) and completed the PHQ-4 questionnaire<sup>44</sup>. Participants completed the Time estimation task, followed by the Metacognitive Questionnaire on Time<sup>40</sup>, and the Recognition task. The protocol ended with questions assessing the participants' habits toward films (e.g., the frequency of movie watching, preferred movie style), if they knew the film from which the sequences were extracted, and their editing experience.

The total duration of the protocol was 16.73 ± 3.8 min.

**Study 2. Participants.** We used the software program G*Power to conduct a power analysis. Our goal was to obtain 0.8 power to detect an effect size of 0.43, based on the effect size obtained in our first exploratory study concerning the effect of Editing on the Time estimation error, at the standard 0.05 alpha error probability. Our subsequent target sample size was 56 participants. We recruited 70 participants among the undergraduate students at the Institute of Psychology at Université Paris Cité using an announcement on the university portal. Participants received class credit for their participation. Participants were excluded according to the following criteria: reporting a history of neurological or psychiatric disorders (8 participants); incomplete data due to technical reasons (2 participants). None of the participants were excluded for having an A' inferior to 0.5. The final sample was composed of 60 participants (95% females, mean age 19.78 ± 3.12 years), and no participant watched the film before the task.

All participants were informed of the academic nature of the study and accepted that their responses would be processed anonymously. After the nature of the procedure had been fully explained, all participants gave written informed consent before carrying out the study. The protocol was approved by the local ethical committee (Université Paris Cité, IRB number: 0012022-5).

*Material.* The material was identical to Study 1, except the images employed for the recognition task. Indeed, in this study the images from the edited sequences (continuous editing and action discontinuity) were captured 5 frames (167 ms) after the cut. For two sequences only, the images were taken 10 frames (333 ms) after the cut, in order to have a salient element in the image.
**Time reproduction task.** The task began with a brief synopsis of the movie, followed by the instructions. Participants were instructed that they would have to reproduce the duration of a movie sequences, as accurately as possible without using external devices, by ending the presentation of a visual stimulus when they estimate it is comparable to that of the previous sequence. They were also told that, after the time estimation, they would be asked to judge their emotional reaction to each sequence.

Following the time reproduction procedure in Damsma, et al.⁴¹, each trail started with a fixation cross lasting 500 ms that was followed by the presentation of a sequence. At the end of the sequence an empty screen was presented for 500 ms, followed by a question mark at the center of the screen for 500 ms that served to indicate the reproduction task. This was followed by a black square (216×216 pixel) presented at the center of the screen. Participants had to click on the spacebar to stop the presentation of the square when they estimated that the duration of presentation was equivalent to that of the preceding sequence. Then, participant had to estimate on a 9-point Likert scale their arousal (1-relaxed, 9-excited). A new trial began afterwards.

The 45 sequences were presented in a randomized order. The association between the editing condition and the duration was counterbalanced across participants so that each sequence was presented in all three durations in the whole sample, but each participant saw a specific sequence once.

The main task was preceded by a familiarization phase of 10 trials that followed the procedure of the experimental task, excepting the stimuli whose duration had to be reproduced and the duration employed. In the familiarization phase, the stimuli consisted in 30 randomly moving dots on a rectangular background that were presented during 2000 ms, 2500 ms, 3000 ms, 3500 ms, and 4000 ms (each duration was presented twice).

**Recognition task.** Study 2 followed the same procedure as the Recognition task in Study 1, except for the stimuli employed (see Fig. 2).

**Procedure.** The experimental sessions took place in an experimental room at the Institute of Psychology of Université Paris Cité. The protocol was programmed with PsychoPy 3¹⁵, and was delivered on a computer screen Iyama G-master GE2488HS (568 × 409 × 217.5 mm, 1920 × 1080 pixel resolution, 75 Hz). Participants were seated at about 60 cm from the screen.

After the protocol description had been explained, all participants signed the informed consent, and answered socio-demographic question (age, sex). Participants completed the Time reproduction task, followed by the Metacognitive Questionnaire on Time⁴⁰, and the Recognition task. The protocol ended with questions assessing the participants' habits toward movies (e.g., the frequency of movie watching, preferred movie style), if they knew the movie from which the sequences were extracted, and their editing experience. As for Study 1, only participants that did not know the film were included in the analyses.

The study was publicly preregistered on the Open Science Framework repository (https://osf.io/vhxa9).

**Ethics approval.** This research was conducted in accordance with the local ethical standards and the Declaration of Helsinki. The conceptual replication study (Study 2) has been publicly preregistered on the Open Science Framework repository (https://osf.io/vhxa9) and received local ethical approval by Université Paris Cité (IRB number: 0012022-5).

**Data availability**

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Received: 29 July 2022; Accepted: 8 November 2022

Published online: 22 November 2022

**References**

1. Cavanagh, P. The artist as neuroscientist. *Nature* **434**, 301–307. https://doi.org/10.1038/434301a (2005).

2. Hasson, U., Nir, Y., Levy, I., Fuhrmann, G. & Malach, R. Intersubject synchronization of cortical activity during natural vision. *Science* **303**, 1634–1640. https://doi.org/10.1126/science.1089506 (2004).

3. Hasson, U., Furman, O., Clark, D., Dudai, Y. & Davachi, L. Enhanced intersubject correlations during movie viewing correlate with successful episodic encoding. *Neuron* **57**, 452–462. https://doi.org/10.1016/j.neuron.2007.12.009 (2008).

4. Calbi, M. et al. How context influences the interpretation of facial expressions: A source localization high-density EEG study on the “Kuleshov effect”. *Sci. Rep.* **9**(1), 1–16 (2019).

5. Tarkovsky, A. Sculpting in time. (University of Texas Press 986).

6. Cutting, J. E. Narrative theory and the dynamics of popular movies. *Psychon. Bull Rev.* **23**, 1713–1743. https://doi.org/10.3758/s13423-016-1051-4 (2016).

7. Smith, T. J. & Henderson, J. M. Edit Blindness: The relationship between attention and global change blindness in dynamic scenes. *J. Eye Mov. Res.* **2**, 2 (2008).

8. Andreu-Sanchez, C., Martin-Pascual, M. A., Gruart, A. & Delgado-Garcia, J. M. Chaotic and fast audiovisuals increase attentional scope but decrease conscious processing. *Neuroscience* **394**, 83–97. https://doi.org/10.1016/j.neuroscience.2018.10.025 (2018).

9. Andreu-Sanchez, C., Martin-Pascual, M. A., Gruart, A. & Delgado-Garcia, J. M. Eyeblink rate watching classical Hollywood and post-classical MTV editing styles, in media and non-media professionals. *Sci. Rep.* **7**, 43267. https://doi.org/10.1038/srep43267 (2017).

10. Shimamura, A. P., Cohn-Sheehy, B. I., Pogue, B. L. & Shimamura, T. A. How attention is driven by film edits: A multimodal experience. *Psychol. aesthet. Creat. Arts* **9**, 417–422. https://doi.org/10.1037/aca0000025 (2015).

11. Bordwell, D., Thompson, K. & Smith, J. *Film art: An introduction*. 12th Edition edn, (McGraw Hill Education, 2019).

12. Balzarotti, S. et al. The editing density of moving images influences viewers' time perception: The mediating role of eye movements. *Cogn. Sci.* **45**, e12969. https://doi.org/10.1111/cogs.12969 (2021).
13. Magliano, J. P. & Zacks, J. M. The impact of continuity editing in narrative film on event segmentation. *Cogn. Sci.* **35**, 1489–1517.
https://doi.org/10.1111/j.1551-6709.2011.01202.x (2011).

14. Zacks, J. M. & Swallow, K. M. Event segmentation. *Curr. Dir. Psychol. Sci.* **16**, 80–84. https://doi.org/10.1111/j.1467-8721.2007.00480.x (2007).

15. Zacks, J. M., Speer, N. K., Swallow, K. M., Braver, T. S. & Reynolds, J. R. Event perception: A mind-brain perspective. *Psychol. Bull.* **133**, 273–293. https://doi.org/10.1037/0033-2909.133.2.273 (2007).

16. Gibbon, J. Scalar expectancy theory and Weber's law in animal timing. *Psychol. Rev.* **84**, 279–325. https://doi.org/10.1037/0033-295X.84.3.279 (1977).

17. Treisman, M. Temporal discrimination and the indifference interval. Implications for a model of the “internal clock”. *Psychol. Monogr.* **77**, 1–31. https://doi.org/10.1037/h0093864 (1963).

18. Zakay, D. & Block, R. A. In *Advances in Psychology* Vol. 115 (edn María A. Pastor & Julio Artieda) 143–164 (North-Holland, 1996).

19. Meck, W. H. & Benson, A. M. Dissecting the brain's internal clock: How frontal-striatal circuitry keeps time and shifts attention. *Brain Cogn.* **48**, 195–211. https://doi.org/10.1006/brcg.2001.1313 (2002).

20. Block, R. A. & Zakay, D. Prospective and retrospective duration judgments: A meta-analytic review. *Psychon. Bull. Rev.* **4**, 184–197.
https://doi.org/10.3758/BF03209393 (1997).

21. Zakay, D. & Block, R. A. Temporal Cognition. *Curr. Dir. Psychol. Sci.* **6**, 12–16. https://doi.org/10.1111/1467-8721.ep11512604 (1997).

22. Zakay, D. & Block, R. A. Prospective and retrospective duration judgments: An executive-control perspective. *Acta Neurobiol. Exp. (Wars.)* **64**, 319–328 (2004).

23. Bangert, A. S., Kurby, C. A. & Zacks, J. M. The influence of everyday events on prospective timing “in the moment”. *Psychon Bull Rev* **26**, 677–684. https://doi.org/10.3758/s13423-018-1526-6 (2019).

24. Fenerci, C., da Silva Castanheira, K., LoParco, M. & Sheldon, S. Changes in the experience of time: The impact of spatial information on the perception and memory of duration. *Q. J. Exp. Psychol. (Hove.)* **74**, 471–482. https://doi.org/10.1177/1747021820968492 (2021).

25. Droit-Volet, S. & Meck, W. H. How emotions colour our perception of time. *Trends Cogn. Sci.* **11**, 504–513. https://doi.org/10.1016/j.tics.2007.09.008 (2007).

26. R Core Team. *R: A language and environment for statistical computing*, <https://www.R-project.org/> (2020).

27. R Studio Team. *R Studio: Integrated development for R*, <http://www.rstudio.com/> (2019).

28. JASP Team. JASP (Version 0.16.2) [Computer software], <https://jasp-stats.org/> (2022).

29. R Core Team. *R: A language and environment for statistical computing*. (2022).

30. Lüdecke, D., Ben-Shachar, M., Patil, I., Waggoner, P. & Makowski, D. Performance: An R package for assessment, comparison and testing of statistical models. *J. Open Sour. Softw.* **6**, 3139. https://doi.org/10.21105/joss.03139 (2021).

31. Lüdecke, D., Ben-Shachar, M. S., Patil, I. & Makowski, D. Extracting, computing and exploring the parameters of statistical models using R. *J. Open Sour. Softw.* **5**, 2445. https://doi.org/10.21105/joss.02445 (2020).

32. Lüdecke, D., Waggoner, P. & Makowski, D. Insight: A unified interface to access information from model objects in R. *J. Open Sour. Softw.* **4**, 1412. https://doi.org/10.21105/joss.01412 (2019).

33. Suárez-Pinilla, M., Nikiforou, K., Fountas, Z., Seth, A. K. & Roseboom, W. Perceptual content, not physiological signals, determines perceived duration when viewing dynamic, natural scenes. *Collabra Psychol.* https://doi.org/10.1525/collabra.234 (2019).

34. Roseboom, W. et al. Activity in perceptual classification networks as a basis for human subjective time perception. *Nat. Commun.* **10**, 267. https://doi.org/10.1038/s41467-018-08194-7 (2019).

35. Gil, S. & Droit-Volet, S. Emotional time distortions: The fundamental role of arousal. *Cogn. Emot.* **26**, 847–862. https://doi.org/10.1080/02699931.2011.625401 (2012).

36. Bangert, A. S., Kurby, C. A., Hughes, A. S. & Carrasco, O. Crossing event boundaries changes prospective perceptions of temporal length and proximity. *Atten. Percept. Psychophys.* **82**, 1459–1472. https://doi.org/10.3758/s13414-019-01829-x (2020).

37. Faber, M. & Gennari, S. P. Effects of learned episodic event structure on prospective duration judgments. *J. Exp. Psychol. Learn. Mem. Cogn.* **43**, 1203–1214. https://doi.org/10.1037/xlm0000378 (2017).

38. Duyck, M., Collins, T. & Wexler, M. Visual continuity during blinks and alterations in time perception. *J. Exp. Psychol. Hum. Percept. Perform.* **47**, 1–12. https://doi.org/10.1037/xhp0000864 (2021).

39. Stanislaw, H. & Todorov, N. Calculation of signal detection theory measures. *Behav. Res. Methods Instrum. Comput.* **31**, 137–149.
https://doi.org/10.3758/bf03207704 (1999).

40. Lamotte, M., Chakroun, N., Droit-Volet, S. & Izaute, M. Metacognitive questionnaire on time: Feeling of the passage of time. *Timing Time Percept.* **2**, 339–359. https://doi.org/10.1163/22134468-00002031 (2014).

41. Damsma, A., Schlichting, N., van Rijn, H. & Roseboom, W. Estimating time: Comparing the accuracy of estimation methods for interval timing. *Collabra Psychol.* **7**(1), 2 (2021).

42. Stoet, G. *Psy Toolkit: A software package for programming psychological experiments* using Linux. *Behav. Res. Method* **42**, 1096–1104. https://doi.org/10.3758/BRM.42.4.1096 (2010).

43. Stoet, G. *Psy Toolkit: A novel web-based method for running online questionnaires and reaction-time experiments*. *Teach. Psychol.* **44**, 24–31. https://doi.org/10.1177/0098628316677643 (2017).

44. Kroenke, K., Spitzer, R. L., Williams, J. B. & Lowe, B. An ultra-brief screening scale for anxiety and depression: The PHQ-4. *Psychosomatics* **50**, 613–621. https://doi.org/10.1176/appi.psy.50.6.613 (2009).

45. Peirce, J. et al. PsychoPy2: Experiments in behavior made easy. *Behav. Res. Method* **51**, 195–203. https://doi.org/10.3758/s13428-018-01193-y (2019).

## Acknowledgements

This study was funded by the SublimAE project (ANR-18-CE27-0023). We thank Catherine Bouton and Nicolas Polion for their help for Study 1. We thank Lucie Rose for proof reading the revised version of the manuscript.

## Author contributions

K.K. and M.S. wrote the main manuscript text. K.K. and M.S. conceptualized the study. J.D.R., C.C. collected and analyzed the data and contributed to designing the protocol. A.H. programmed the experimental task of Study 1. D.M. contributed in analyzing the data. All authors reviewed the manuscript.

## Competing interests

The authors declare no competing interests.

## Additional information

Correspondence and requests for materials should be addressed to K.K. or M.S.
**Reprints and permissions information** is available at www.nature.com/reprints.

**Publisher's note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

© The Author(s) 2022