Self-Motion Perception Influences Postural Sway More than Environmental Motion Perception
Bibliographic record
Abstract
Motion of the visual field can alter postural sway and cause illusions of self-motion. The relative perceptual sensitivity of self-motion versus visual field motion induced by virtual reality (VR) stimulation and whether observable sway differs based on perceptual task is unknown. Methods to quantify sway perception while concurrently measuring sway do not exist. We measured head sway and motion perception (self or world) in healthy adults who stood with feet together wearing a VR headset while experiencing adaptive staircases of virtual sinusoidal pitch rotation about the ankle axis. In separate conditions of randomly ordered blocks, subjects were asked to indicate (yes/no) if the room moved (regardless of perceived postural sway) or if their postural sway increased (regardless of perceived room motion). Head sway area was measured by tracking movement of the VR headset. Yes/No responses were fit with psychometric curves to determine points of subjective equality (PSEs) for room motion and postural sway. PSEs were compared between conditions. Effects of motion perception (binary responses) on head sway area before, during, and after visual stimulation were examined. The mean PSE for room motion (0.42 degrees) was significantly lower than for postural sway (2.02 degrees) [t(1,18) = 4.4714, p = 0.00029]. Head sway area was significantly larger during (z = 11.53, p < 0.001) and after (z = 5.09, p < 0.001) visual stimulation only when participants perceived increased postural sway. Nearly 5-fold greater amplitudes of oscillating VR visual stimuli were required to induce perceptions of altered self- versus visual field motion. Observed head sway during visual motion was only linked to perceptual responses when participants focused internally on self-motion, not externally on room motion.
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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".