Virtual Reality’s Effect on Time Estimation is Inconsistent and Depends on Environment Size
Bibliographic record
Abstract
Abstract Despite anecdotal reports that time flies in virtual reality (VR), only a few studies have found that participants underestimate time in VR in comparison with a matched non-VR control condition. Across three experiments, we attempt to replicate one of these studies (Mullen and Davidenko, 2021) and to identify factors that may mediate the effect of VR on time estimation. Participants were assigned to play a simple video game for a specified duration (five or 10 minutes) in one of two display conditions (VR or conventional monitor), and we recorded the actual durations they produced. Experiments 1 and 2 both failed to replicate a VR-induced underestimation effect, suggesting that the previously reported effect is not reliable. However, the VR group in Experiment 2 produced significantly longer intervals than the VR group in Experiment 1. This difference may be related to changes in virtual camera size, which inversely determines the simulated scale of the environment in VR. Experiment 3 tested this possibility by assigning participants to estimate time in VR conditions that used a small, medium, or large virtual camera. Participants tended to underestimate time in smaller-camera (i.e., larger environment) conditions relative to larger-camera (smaller environment) conditions. Collectively, these results suggest that controlled experiments may fail to detect VR-induced time compression because the virtual environments that they use as stimuli (specifically, those that can be viewed from a fixed perspective in a non-VR control condition) lack the immersive scale of commercial VR experiences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".