Caloric vestibular stimulation induces vestibular circular vection even with a conflicting visual display presented in a virtual reality headset
Bibliographic record
Abstract
This study explored visual-vestibular sensory integration when the vestibular system receives self-motion information using caloric irrigation. The objectives of this study were to (1) determine if measurable vestibular circular vection can be induced in healthy participants using caloric vestibular stimulation and (2) determine if a conflicting visual display could impact vestibular vection. In Experiment 1 (E1), participants had their eyes closed. Air caloric vestibular stimulation cooled the endolymph fluid of the horizontal semi-circular canal inducing vestibular circular vection. Participants reported vestibular circular vection with a potentiometer knob that measured circular vection direction, speed, and duration. In Experiment 2 (E2), participants viewed a stationary display in a virtual reality headset that did not signal self-motion while receiving caloric vestibular stimulation. This produced a visual-vestibular conflict. Participants indicated clockwise vection in the left ear and counter-clockwise vection in right ear in a significant proportion of trials in E1 and E2. Vection was significantly slower and shorter in E2 compared to E1. E2 results demonstrated that during visual-vestibular conflict, visual and vestibular cues are used to determine self-motion rather than one system overriding the other. These results are consistent with optimal cue integration hypothesis.
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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 | 0.001 |
| 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".