Sensory Reweighting: A Common Mechanism for Subjective Visual Vertical and Cybersickness Susceptibility
Bibliographic record
Abstract
Abstract The malaise symptoms of cybersickness are thought to be related to the sensory conflict present in the exposure to virtual reality (VR) content. When there is a sensory mismatch in the process of sensory perception, the perceptual estimate has been shown to change based on a reweighting mechanism between the relative contributions of the individual sensory signals involved. In this study, the reweighting of vestibular and body signals was assessed before and after exposure to different typical VR experiences and sickness severity was measured to investigate the relationship between susceptibility to cybersickness and sensory reweighting. Participants reported whether a visually presented line was rotated clockwise or counterclockwise from vertical while laying on their side in a subjective visual vertical (SVV) task. Task performance was recorded prior to VR exposure and after a low and high intensity VR game. The results show that the SVV was significantly shifted away from the body representation of upright and towards the vestibular signal after exposure to the high intensity VR game. Cybersickness measured using the fast motion sickness (FMS) scale found that sickness severity ratings were higher in the high intensity compared to the low intensity experience. The change in SVV from baseline after each VR exposure modelled using a simple 3-parameter gaussian regression fit was found to explain 49.5% of the variance in the FMS ratings. These results highlight the aftereffects of VR for sensory perception and suggests a potential relationship between the susceptibility to cybersickness and sensory reweighting.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".