Can the Perceived Timing of Multisensory Events Predict Cybersickness?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.001 |
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 it