Cognitive load, fatigue and aversive simulator symptoms but not manipulated zeitgebers affect duration perception in virtual reality

Duration judgments

The sun was a constant component of the environment and was designed to move with either its natural speed on the horizon or to not move at all. Participants completed 8 trials in total, 6 minutes each, and were not informed of the fact that trials were of equal length, nor of the number of trials they would complete. Moreover, throughout the experiment participants listened to ambient sounds of sea waves and wind through headphones.

We experimentally manipulated the level of immersion by asking participants to complete the task in both a non-immersive and an immersive environment, in front of the LCD monitor or with the head mounted device (HMD) respectively. In order to probe the effect of cognitive load, as in the original study, in half of the trials participants were asked to complete the classical n-back task30. These experimental conditions amount to 2 (immersion) × 2 (sun movement) × 2 (cognitive load) design. At the end of each trial, participants were asked to estimate the duration of the time of the passed trial in seconds, following Schatzschneider et al. 2016 design. At the beginning and the end of the experimental session participants were also asked to complete a Simulator Sickness Questionnaire (SSQ), the change of this score is used as Simulator Sickness Score. To test the effects of each experimental condition we run a 2 × 2 × 2 Repeated measures ANOVA, with the Simulator Sickness Score as a covariate in a separate run. Results were adjusted for multiple comparison with a Bonferroni correction where needed.


First, we tested the effect of the manipulation of the sun. In the original study authors found that when no task was executed, participants estimated the time to be longer when the sun was still than when it moved at its natural speed. We found no significant effect of the manipulation of the speed of the movement of the sun on time estimation (F(1,36) = 0.103, p = 0.750, Fig. 1A) even when we look only at the condition with no-task (two-tailed you(36) = − 0.191, p = 1, adjusted for multiple comparisons, Fig. 2). To make sure that the effect was not inhibited by the unpleasant simulator effect we included the score as a covariate to the ANOVA analysis. Although adding the SSQ score improves fit of the model, (residuals(full + SSQ) < residuals (full), where residual = data − fit), it has no significant effect on the sun, immersion, or interaction between sun and immersion.

Figure 1
figure 1

Mean duration estimation for each experimental condition. Error bars indicate SEM. We found no significant effect of sun (A) nor immersion (C). We found a significant effect of cognitive load (B.). Post-hoc tests indicated that subjects estimated time to be shorter in the condition with an n-back task compared to the condition with no task (you(36) = 8.307, p= 6.876and−10,d= 0. 59).

Figure 2
figure 2

Mean estimation as an interaction between the sun and cognitive load. We did not replicate the effect from the original study.

Cognitive load

We replicated the classical cognitive load resulteleven and found a significant effect of the task on trial duration estimation (F(1,36) = 69.013; p = 6.874e-10, ηptwo= 0.657). Post-hoc test revealed duration estimation to be shorter when participants performed a task than when they were passively present in the environment (you(36) = 8.307, p = 6.876and−10,d= 0.59, Fig. 1B).


Schatzschneider et al.28 reported shorter duration estimation in non-immersive (LCD) than in immersive (HMD) environments. We did not replicate this result and no effect of the environment was found on duration estimation (F(1,36) = 0.588; p= 0.448, Fig. 1C), however, we found a significant interaction between cognitive load and immersion (F(1,36) = 6.854; p= 0. 013, (? P ^ {2 } = 0.16))—when no task was assigned participants estimated time to be longer in an immersive environment, but when participants performed a task, they estimated the duration to be longer in a non-immersive environment (Fig. 3A). We initially thought that this is again a cognitive load effect—in immersive environment participants were exposed to fewer distractors and could focus on the task more, and engage more cognitive resources. However, performance was slightly higher in the LCD condition (paired you(73) = 2.7156, p= 0. 0083, Fig. 3A insert). In light of these results, we hypothesized that HMD was somehow a more challenging environment. In the non-immersive environment, in the condition with no task, participants were exposed to the rest of the experimental room, which resulted in a more complex environment as opposed to the HMD condition where only the virtual island could be observed.

Figure 3
figure 3

(A) Mean estimation as interaction between immersion and cognitive load. insert Performance (sensitivity index A’) during the n-back task for the two immersion levels. (B.) Average time estimation over the course of the experiment, regardless of the experimental condition. In gray label shuffled permutation, shaded area represents standard deviation. (C) Scatter plot of the mean duration estimation during HMD trials and the SSQ score. Each dot represents one participant.

Simulator Sickness symptoms—physiological arousal

Although negative simulator symptoms did not specifically affect the effect of the sun on time estimation, we tested whether it had any effect on average time estimation. To assess the statistical significance of this relationship we regressed the SSQ score on duration estimation measurements. The SSQ score explained a significant amount of the variance of the duration estimation (F(1,294) = 9. 590, p= 0.002,R = 0. 178,Rtwo = 0.032). The regression coefficient (B.= 3.765,95%,IC= [1.372,6.156]) indicated that an increase in SSQ score by 1 increased, on average, the duration estimation by 4 seconds (Fig. 3C). We hypothesize that this effect of aversive simulator sickness symptoms, like nausea or vertigo, is mediated by the link between physiological arousal and time perception. It has previously been shown that various types of arousal affect interval timing4,5.6especially unpleasant stimuli dilate time perception31. Although Schatzschneider et al.28 also collected SSQ responses, they did not provide analysis beyond score change.


As stated above, we did not replicate the effect of immersion on time duration estimation. Although the effect reported in the original paper was not significant, it did appear as a strong trend, at least for conditions with a task. In the original paradigm, participants had always started with a non-immersive block and continued onto an immersive block. Such a linear design is confused by fatigue. At the beginning of the experiment, participants may estimate the duration to be shorter just because they are less tired than at the end, so we hypothesize that this is the reason we did not replicate the immersion effect reported by Schatzschneider et al.28. In our design, we controlled for this effect by randomizing the immersion blocks. Whatever the condition with which participants have started or finished the experiment, they estimated the time to be shorter at the beginning of the experiment than at the end (Fig. 3B) with a transient constricting effect of the environment change mid-experiment.

Bayesian analysis

Non-significant results of frequentist tests do not discriminate between “absence of evidence” and “evidence of absence”. To test our ability to present evidence in favor of the null hypothesis (no effects of sun movement on time perception) we went beyond the frequentist approach, turned to Bayesian Inference, and conducted Bayesian Repeated ANOVA and separate Bayesian Paired-Sample t-tests for each condition. We first conducted a Bayesian Repeated Measures ANOVA on the data with the experimental conditions. [immersion (2) × task (2) sun (2)] as within-subjects factors. We used the default prior options for the effects (ie, r= 0.5 for the fixed effects). To assess the robustness of the result, we also repeat the analysis for two different prior specifications (details in the ”Methods” section). Analysis of effects indicates moderate to strong evidence for exclusion of immersion (BFexcl = 4.803, where BFexcl is the change from prior to posterior exclusion odds for model-averaged results, our notation follows JASP manual), sun (BFexcl = 16.815), and all interactions between the two. In fact, only the model with a single cognitive load term had BFexcl smaller than 1 (BFexcl = 3.724and−13). Post-hoc tests indicated robustness of these findings to prior width (Figs. 4, S2). We can therefore conclude that in our data we can observe moderately strong evidence for no effect of the manipulation of sun or immersion on duration estimation.

Figure 4
figure 4

Results of the post-hoc Bayesian paired sample t-test between the two levels of sun manipulation. On the left, the effect size as a function of the prior and posterior density. in the middle BF10 as a function of tested prior. On the right, accumulation of evidence towards H0 as a function of the number of samples (participants).

Leave a Reply

Your email address will not be published.