Effect of gain adaptation on postural sway
Bibliographic record
Abstract
In virtual reality (VR) environments, our movements often differ from those in the equivalent physical world. One common discrepancy is visual gain which is a scaling difference between visual and kinesthetic motion. Previous studies have shown that observers reliably perceived the point of subjective stationarity (PSS) in a gain discrimination task during active self-motion. However, the PSS did not shift following prolonged adaptation to non-unity gain. Here we asked whether postural response adapts to gain manipulations. Three adaptation gains were tested in separate, counterbalanced blocks. Each block consisted of a 10-minute initial adaptation, followed by four test segments interleaved with three 2-minute top-up adaptation periods. During adaptation observers were immersed in a virtual room and continuously walked to grab objects and align them with corresponding markers at other locations. Their virtual motion was scaled by 0.67, 1 or 2 times their physical motion. During testing, quiet stance postural sway was recorded while the surrounding environment oscillated sinusoidally at 0.2 Hz over a peak-to-peak distance of 0.5 m, in either the front-back (N=18) or the left-right (N=14) direction. To isolate the visual perturbation effects, we also included a ‘stimulus absent’ condition in which observers viewed a dark HMD screen. Prior to starting, 60 seconds of baseline postural sway data was collected for both visual stimulus present and absent conditions. Results showed that postural sway in dark was larger after adaptation to a gain of 2 (in either motion direction). Further, power analysis at 0.2 Hz suggested that visually-elicited synchronous postural sway was larger under both non-unity gains along the front-back direction, suggesting that gain manipulations resulted in destabilization. Collectively these experiments suggest that gain manipulations produce adaptation in postural responses, while perceived stability does not shift. This dissociation suggests that postural recalibration to gain adaptation operates independently of perceptual mechanisms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".