Vection is facilitated by bone conducted vibration and galvanic vestibular stimulation
Bibliographic record
Abstract
The illusory sense of self-motion that can occur when the visual field moves coherently ('vection') has revealed key insights into how sensory information is integrated. In the natural environment, moving through space generates an immediate perception that we are in motion. In the case of illusory self-motion, there are delays in the region of 5-10 seconds between seeing the visual field move and the feeling of vection. It has been suggested that this delay occurs due to the lack of concurrent vestibular signals accompanying visual motion onset. Any reduction in this delay could improve virtual reality (VR) immersiveness and potentially reduce 'simulator-sickness'. Researchers have attempted to reduce visual-vestibular mismatch using a technique that applies electrical stimulation to the vestibular organs, known as galvanic vestibular stimulation (GVS). Applying GVS can modulate vection and can visibly reduce nausea in VR. However, GVS is an invasive stimulation method that requires significant expertise to use appropriately. Here, we tested two techniques with the potential to provide similar benefits to GVS that are minimally invasive: chair vibration, and bone conducted vibration (BCV) applied to the mastoid processes. We examined vection magnitude and latency for wide field visual rotations, applying transient stimulation either concurrently or asynchronously with the start of visual motion. We found that both GVS and BCV, but not chair vibration, reduced vection latency compared to control when applied at the same time as visual motion onset. This difference vanished when stimulation and visual motion onset were asynchronous. Inspection of vection magnitude responses indicated no consistent differences across conditions. While we had used only roll for visual motion in the first experiment, a second experiment confirmed the same effects for yaw and pitch rotation. We therefore propose BCV as a promising candidate for reducing simulator sickness and increasing immersiveness in virtual environments. Meeting abstract presented at VSS 2016
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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".