Testing the ‘differences in virtual and physical head pose’ and ‘subjective vertical conflict’ accounts of cybersickness
Bibliographic record
Abstract
Abstract When we move our head while in virtual reality, display lag will generate differences in our virtual and physical head pose (known as DVP). While DVP are a major trigger for cybersickness, theories differ as to exactly how they constitute a provocative sensory conflict. Here, we test two competing theories: the subjective vertical conflict theory and the DVP hypothesis. Thirty-two HMD users made continuous, oscillatory head rotations in either pitch or yaw while viewing a large virtual room. Additional display lag was applied selectively to the simulation about the same, or an orthogonal, axis to the instructed head rotation (generating Yaw-Lag + Yaw-Move, Yaw-Lag + Pitch-Move, Pitch-Lag + Yaw-Move, and Pitch-Lag + Pitch-Move conditions). At the end of each trial: (1) participants rated their sickness severity and scene instability; and (2) their head tracking data were used to estimate DVP throughout the trial. Consistent with our DVP hypothesis, but contrary to subjective vertical conflict theory, Yaw-Lag + Yaw-Move conditions induced significant cybersickness, which was similar in magnitude to that in the Pitch-Lag + Pitch-Move conditions. When extra lag was added along the same axis as the instructed head movement, DVP was found to predict 73–76% of the variance in sickness severity (with measures of the spatial magnitude and the temporal dynamics of the DVP both contributing significantly). Ratings of scene instability were also found to predict sickness severity. Taken together, these findings suggest that: (1) cybersickness can be predicted from objective estimates of the DVP; and (2) provocative stimuli for this sickness can be identified from subjective reports of scene instability.
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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 | 0.002 | 0.018 |
| 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 | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".