# The Effect of Task Complexity on Time Estimation in the Virtual Reality Environment: An EEG Study
## 1 Introduction  
Virtual reality (VR) technology provides an immersive computer-generalized environment in which individuals are able to interact with multisensory input [1; 2]. As VR technology has become increasingly portable and inexpensive in recent decades, VR applications have expanded into diverse education and training areas, where they are used to reproduce scenarios that may be expensive or hard to mimic in real life [3]. However, because the head-mounted display (HMD) that is part of VR technology blocks real-world external stimuli, it is challenging for VR users to estimate the amount of time they have spent inside the virtual reality environment (VRE) [1]. Time estimation refers to the process by which an individual subjectively judges duration. Two categories of time estimation have been defined. In the prospective time estimation paradigm, participants are informed that they are required to estimate duration prior to starting a task; in contrast, in the retrospective time estimation paradigm, participants are not informed that they are required to estimate duration prior to starting a task. Both paradigms ask participants about duration after the task [4; 5]. Due to the limitations of the experimental settings in which participants surmise that they may be asked to judge duration, prospective time estimation has been used more widely in studies than retrospective time estimation [5]. Prospective time estimation is explained by the attention allocation model that fewer resources are available for processing time estimation when other cognitive tasks require more attentional resources [6; 7; 8]. Multiple studies have underlined the importance of time estimation in indicating task performance and users' satisfaction in diverse task settings [9; 10]. Accurate time estimation is known to be an indicator of enhanced user experience, high decision-making quality, and high academic performance. For example, users were likely to overestimate the time they spent on webpage loading and downloading tasks, and their user experience ratings decreased correspondingly [11]. In addition, inaccurate time estimation has been shown toresult in customers' dissatisfaction with waiting in line [12; 13]. Previous studies have also paid attention to how time estimation influences decision-making. [PERSON]'s study [14] indicated that when individuals made larger time estimation errors, they tended to make impulsive decisions without evaluating the value of the options. Another study, too, found that individuals made impulsive decisions, choosing instant rewards over delayed rewards with greater value [15], when they overestimated the time. In the academic arena, [PERSON] [16] indicated that students who underestimated the amount of time they had spent reading textbooks and answering exercise questions were more likely to delay starting coursework and to have lower academic performance. [PERSON] and [PERSON] [17] observed that students who made accurate time estimations when reading a psychology manuscript tended to have higher grade-point averages (GPAs). Though researchers have realized the importance of time estimation, few studies have investigated time estimation in the VRE; of those that have, there have been mixed results. One stream of research showed that during chemotherapy, most individuals estimated the time duration in the VRE to be shorter than the actual time elapsed [1; 18]. In other studies, participants estimated that the time elapsed in the VRE was longer than the actual time they had spent playing a music game and a shooting game [19], and walking according to the direction of a virtual marker [20]. One of the main reasons for such inconsistency is investigating without taking task complexity into account to time estimation in the VRE. In Sections 1.1 and 1.2, we provide a literature review (the first and second paragraphs). Then, we discuss the limits of the existing studies in terms of their experimental designs (the third paragraph of Sections 1.1 and 1.2) and assessment tools (Section 1.3).