Ahead of its Time: An exploration of virtual environment effects on time estimation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Virtual reality (VR) is an increasingly popular technology, yet little is known about the cognitive effects it produces. For example, no research has been done investigating time perception in virtual environments. The present work proposed and tested a model of time estimation accuracy in virtual environments. A VR flight simulator was used to engage participants in a virtual environment, where they were required to make time estimations. Video game experience, cognitive load, and VR immersiveness factors were considered potential predictors. Video game experience, presence, interactivity, and immersion -fluency were significant predictors of time estimation accuracy. Having prior video gaming experience, higher levels of presence and interactivity in the virtual environment led to more accurate time estimates. In contrast, higher levels of immersion -fluency reduced time estimation accuracy. These results inform stakeholders of VR technology and highlight the importance of understanding how these factors influence time perception in VR.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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 it