Tactile cueing vs. vestibular sensation and nystagmus during yaw rotation.
Bibliographic record
Abstract
INTRODUCTION: It has been reported that vibrating electromechanical tactile stimulation on the torso overrides the vestibular sensation of rotation. The purpose of this study is to further examine the influence of tactile cues on real and apparent sensations of self-rotation and corresponding vestibular nystagmus during passive yaw rotation. METHOD: There were 15 healthy subjects (12 men and 3 women, 31 to 49 yr) who were subjected to clockwise yaw rotation at a constant velocity of 120 degrees. s(-1). A tactile vest using electromechanical tactors (8 columns x 3 rows) was activated either in the clockwise or counterclockwise direction in a randomized design. The sequential activation of the tactors around the body matched the velocity of chair rotation. Eye movements were monitored using infrared eye tracking. Since it is difficult to control for mental set (alertness and imagined target distance) during eye tracking, in addition to paying attention to the apparent sensation of self-motion, the sensation of rotation and the induced vestibular nystagmus were investigated separately. RESULTS: ANOVA failed to reveal any significant effect. Our results suggested that tactile cueing did not enhance or suppress the subjective sensation of rotation during actual rotation, nor did it enhance or suppress the false sensation of rotation experienced. Also, tactile cueing had no effect on the gain and time constant of the induced per-rotation and post-rotation nystagmus with repeated exposures. CONCLUSION: It appears that tactile cueing delivered by a tactile garment provides a sense of direction, but it does not affect any concomitant vestibular responses.
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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) | 0.003 | 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".