DVP predicts the probability of becoming sick and dropout times during head mounted display based virtual reality
Bibliographic record
Abstract
Abstract When head-mounted display (HMD) users move their heads during virtual reality (VR), display lag will generate differences between their virtual and physical head pose (DVP). Previously, we have shown that objective estimates of DVP can be used to predict the severity of user experiences of cybersickness. Here we examined whether DVP also predicts: (1) the probability of them becoming sick during VR; and (2) the time of their first sickness symptoms. Our participants made continuous nodding head movements while viewing a virtual room under different levels of experimentally imposed display lag (ranging from 0 to 250 ms on top of the baseline system lag). While each trial could last up to 3 min, they were instructed to drop out as soon as they felt any sickness. We found that: (1) the self-similarity of the participant’s DVP in the first 30 s of the trial predicted whether they would become sick (or remain well) later on; and (2) their dropout times were predicted by the spatial magnitudes of their DVP. Consistent with past findings, the severity of their cybersickness was again shown to depend on both the spatial magnitudes and the temporal dynamics of their DVP. In the future, it might therefore be possible to apply these DVP findings to warn HMD users about the likely imminent onset of cybersickness during normal (i.e., non-experimental) VR exposures.
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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 | 0.000 | 0.007 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".