Effects of Constant and Time-Varying Display Lag on DVP and Cybersickness When Making Head-Movements in Virtual Reality
Bibliographic record
Abstract
When HMD users move their heads in virtual reality (VR), display lag creates differences between their virtual and physical head pose (DVP). This study examined whether objective estimates of DVP could predict experiences of cybersickness during simulations with three different types of added lag: (1) Constant lag (where the display was always delayed by 250 ms); (2) Predictable time-varying lag (where delays alternated between 0 and 250 ms every 5 s); and (3) Random time-varying lag (where delays alternated between 0 and a randomly determined value, up to 250 ms, every 1–5 s). Constant, Predictable, and Random added lag were found to generate similar levels of cybersickness—with all three conditions producing more severe sickness than the Baseline lag control. Consistent with our DVP hypothesis, the spatial magnitude and temporal dynamics of our participants’ DVP were both found to be reliable predictors of their cybersickness in all display lag conditions tested.
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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 | 0.001 | 0.014 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".