Can Galvanic Vestibular Stimulation Reduce Simulator Adaptation Syndrome?
Bibliographic record
Abstract
Electrical stimulation of the vestibular sensory system during virtual environment simulations has been proposed as a method to reduce the incidence of simulator adaptation syndrome (SAS). However, there is limited empirical evidence to support this hypothesis. It is especially important to provide vestibular stimulation in driving simulators because an absence of vestibular cues may alter driver behaviour and reduce vehicle control. This study examined the application of galvanic vestibular stimulation (GVS) as a technique to reduce symptoms of SAS and improve vehicular control in a fixed-based driving simulator. Nineteen participants drove two visually distinct virtual environments (high and low visual cues). In addition, each of these worlds was experienced with and without GVS. Post-drive scores on the Simulator Sickness Questionnaire (SSQ) were used to evaluate the effect of GVS on SAS. In addition, three driving variables were measured to examine driving performance: steering variability, lane departures, and average vehicular speed. GVS application while driving resulted in significant decreases in total SSQ and disorientation symptoms. Greater vehicular control was also observed (as shown by reduced steering variability) when GVS was used in combination with visual cues along the simulated edge of the road. These results support that GVS may be used in fixed-base driving simulators to create vestibular motion cues and reduce SAS.
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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 | 0.001 | 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".