Reduction of cybersickness during and immediately following noisy galvanic vestibular stimulation
Bibliographic record
Abstract
Abstract The mechanism underlying cybersickness during virtual reality (VR) exposure is still poorly understood, although research has highlighted a causal role for visual-vestibular sensory conflict. Recently established methods for reducing cybersickness include galvanic vestibular stimulation (GVS) to mimic absent vestibular cues in VR, or vibration of the vestibular organs to add noise to the sensory modality. Here, we examined if applying noise to the vestibular system using noisy-current GVS also affects sickness severity in VR. Participants were exposed to one of two VR games that were classified as either moderate or intense with respect to their nauseogenic effects. The VR content lasted for 50 minutes and was broken down into 3 blocks: 30 minutes of gameplay during exposure to either noisy GVS (±1750 μA) or sham stimulation (0 μA), and 10 minutes of gameplay before and after this block. We characterized the effects of noisy GVS in terms of post-minus-pre-exposure cybersickness scores. For the intense VR content, we found a main effect of noisy vestibular stimulation. Participants reported lower cybersickness scores during and directly after exposure to GVS. However, this difference was quickly extinguished (∼3-6 min) after further exposure to VR, indicating that sensory adaptation did not persist after stimulation was terminated. In contrast, there were no differences between the sham and GVS group for the moderate VR content. The results show the potential for reducing cybersickness with simple non-invasive sensory stimulation. We discuss the prospect that noise-induced sensory re-weighting is responsible for the observed effects, and address other possible mechanisms.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".