# The Effect of Task Complexity on Time Estimation in the Virtual Reality Environment: An EEG Study
## 4 Discussion and Conclusions
Throughout the experiment, we purposefully avoided providing any feedback to the participants on their estimation results. The participants were thus unable to adjust their estimations based on their previous results. Task repetition decreases the attentional resources assigned to the given task [70, 71] and leaves more attentional resources available for time estimation, thereby contributing to more accurate time estimation results [70]. In addition to considering relative beta-band power, we examined changes in relative theta-band power in the frontal and parietal regions. We did not find significant results when we tested the relative theta-band power as the dependent variable. Given that the previous studies have reported increased theta-band power in enhanced task complexity [35, 72, 73], beta-band power is more sensitive in the VRE to detect changes in task complexity compared to the theta-band. The significant correlation between the relative beta-band power at Cz and time estimation error indicates the connection between beta-band activities in the midline central region and time estimation, whether inside or outside of the VRE. In a previous study conducted in a non-VRE setting, [PERSON] and colleagues [74] observed a suppression in beta-band activities in the midline central region during time estimation. [PERSON] et al. [75] also reported a significant change in beta-band power at Cz when participants estimated elapsed time. As previous researchers have suggested, the beta-band plays an essential role in time estimation mechanisms [74, 75, 76], and our findings further support the theory in the VRE. Additionally, our correlation results between the absolute time estimation error and the NASA-TLX score corroborate previous findings indicating that time estimation is a measure of mental workload in surgery training [49] and in 2D video games [77]. Our results also validate time estimation usage in the VRE. When the environment limits the use of the NASA-TLX questionnaire, time estimation can serve as a valid and quick mental workload evaluation tool. Given that the age distribution of the participants was not diverse in that most of the participants were college-aged young adults, we did not originally consider age to be the main factor in this study. However, we acknowledge that age plays a significant role in time estimation tasks [56, 78]. Previous studies found noticeable differences between age groups such that the elderly group made larger time estimation errors than the younger groups did [56; 78]. To test the effect of age on our dependent variables, we divided age into two levels (i.e., older than or equal to 24 and younger than 24) using a median split. However, the ANOVA result showed that age did not have a significant effect on absolute estimation error (\(F\) (1, 27) = 0.16, \(p\) = 0.70), relative beta-band power at Cz (\(F\) (1,20) = 0.08, \(p\) = 0.78), Fz (\(F\) (1,20) = 0.04, \(p\) = 0.85), Pz (\(F\) (1,20) = 2.05, \(p\) = 0.17), or NASA-TLX score (\(F\) (1, 27) = 2.94, \(p\) = 0.10). To increase the generalizability of the results, future studies might aim to recruit participants from different age groups. Our study has several limitations that merit future research. First, this study included factors that may have imperceptibly influenced the quality of EEG signals, such as the vibration of the VR controller and the electrical interference between the VR HMD and the EEG headset. To eliminate noise, future research might perform further data filtering by comparing the EEG signals at the baseline and during the experiment. Second, the statistical power of the analysis was 0.6, indicating a 40% probability of having a Type II error in the results, which could be attributed to the small sample size. Third, only three EEG electrodes were used in the experiment. In the future, brain signals from multiple electrodes might be analyzed synchronously to further investigate brain connectivity and its relationship with time estimation in the VRE.