Influence of Sensory Conflict on Perceived Timing of Passive Rotation in Virtual Reality
Bibliographic record
Abstract
Integration of incoming sensory signals from multiple modalities is central in the determination of self-motion perception. With the emergence of consumer virtual reality (VR), it is becoming increasingly common to experience a mismatch in sensory feedback regarding motion when using immersive displays. In this study, we explored whether introducing various discrepancies between the vestibular and visual motion would influence the perceived timing of self-motion. Participants performed a series of temporal-order judgements between an auditory tone and a passive whole-body rotation on a motion platform accompanied by visual feedback using a virtual environment generated through a head-mounted display. Sensory conflict was induced by altering the speed and direction by which the movement of the visual scene updated relative to the observer's physical rotation. There were no differences in perceived timing of the rotation without vision, with congruent visual feedback and when the speed of the updating of the visual motion was slower. However, the perceived timing was significantly further from zero when the direction of the visual motion was incongruent with the rotation. These findings demonstrate the potential interaction between visual and vestibular signals in the temporal perception of self-motion. Additionally, we recorded cybersickness ratings and found that sickness severity was significantly greater when visual motion was present and incongruent with the physical motion. This supports previous research regarding cybersickness and the sensory conflict theory, where a mismatch between the visual and vestibular signals may lead to a greater likelihood for the occurrence of sickness symptoms.
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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 | 0.009 |
| 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 | 0.001 |
| 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".