The effect of gain adaptation on perception and posture
Bibliographic record
Abstract
Consistent motion simulation in Virtual Reality (VR) applications is challenging due to constraints on tracking technologies and locomotion or interaction paradigms that scale motion. In this study, we measured (a) the point of subjective stationarity (PSS) during active self-motion, (b) postural sway in the dark, and (c) visually-induced postural sway during quiet stance, both before and after adapting to different gains between physical self-motion and the motion portrayed in the virtual environment. Participants adapted to each of three adaptation gain levels in separate blocks: normal, reduced, and increased, in which observers' physical motion was scaled and displayed as the virtual motion. We measured PSS during active self-motion and postural sway during quiet stance in both left-right and front-back directions in separate sessions. We found that the PSS measured during self-motion did not vary with adaptation gain. However, postural sway elicited by visual perturbation was modulated after adapting to non-unity gains. We also measured baseline postural sway prior to adaptation and found that exposure to virtual motion under unity (normal) gain increased the postural variability along the left-right direction, when tested without visual feedback (in dark). Collectively these results suggest that while observers adapt to gain, active self-motion provides sufficient somatosensory feedback to counteract the shift in perceived motion. As a result, PSS remains consistent across all gain manipulations in our experiment setup. In contrast, postural responses during quiet stance did not recalibrate immediately after motion gain perturbation was removed suggesting that they operate 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 | 0.003 |
| 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) | 0.002 | 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".