Vection Latency Is Reduced by Bone-Conducted Vibration and Noisy Galvanic Vestibular Stimulation
Bibliographic record
Abstract
Studies of the illusory sense of self-motion elicited by a moving visual surround (‘vection’) have revealed key insights about how sensory information is integrated. Vection usually occurs after a delay of several seconds following visual motion onset, whereas self-motion in the natural environment is perceived immediately. It has been suggested that this latency relates to the sensory mismatch between visual and vestibular signals at motion onset. Here, we tested three techniques with the potential to reduce sensory mismatch in order to shorten vection onset latency: noisy galvanic vestibular stimulation (GVS) and bone conducted vibration (BCV) at the mastoid processes, and body vibration applied to the lower back. In Experiment 1, we examined vection latency for wide field visual rotations about the roll axis and applied a burst of stimulation at the start of visual motion. Both GVS and BCV reduced vection latency by two seconds compared to the control condition, whereas body vibration had no effect on latency. In Experiment 2, the visual stimulus rotated about the pitch, roll, or yaw axis and we found a similar facilitation of vection by both BCV and GVS in each case. In a control experiment, we confirmed that air-conducted sound administered through headphones was not sufficient to reduce vection onset latency. Together the results suggest that noisy vestibular stimulation facilitates vection, likely due to an upweighting of visual information caused by a reduction in vestibular sensory reliability.
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.
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) | 0.001 | 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".