Can the Perceived Timing of Multisensory Events Predict Cybersickness?
Bibliographic record
Abstract
Humans are constantly presented with rich sensory information that the central nervous system (CNS) must process to form a coherent perception of the self and its relation to its surroundings. While the CNS is efficient in processing multisensory information in natural environments, virtual reality (VR) poses challenges of temporal discrepancies that the CNS must solve. These temporal discrepancies between information from different sensory modalities leads to inconsistencies in perception of the virtual environment which often causes cybersickness. Here, we investigate whether individual differences in the perceived relative timing of sensory events, specifically parameters of temporal-order judgement (TOJ), can predict cybersickness. Study 1 examined audiovisual (AV) TOJs while Study 2 examined audio-active head movement (AAHM) TOJs. We deduced metrics of the temporal binding window (TBW) and point of subjective simultaneity (PSS) for a total of 50 participants. Cybersickness was quantified using the Simulator Sickness Questionnaire (SSQ). Study 1 results (correlations and multiple regression) show that the oculomotor SSQ shares a significant yet positive correlation with AV PSS and TBW. While there is a positive correlation between the total SSQ scores and the TBW and PSS, these correlations are not significant. Therefore, although these results are promising, we did not find the same effect for AAHM TBW and PSS. We conclude that AV TOJ may serve as a potential tool to predict cybersickness in VR. Such findings will generate a better understanding of cybersickness which can be used for development of VR to help mitigate discomfort and maximize adoption.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".