Perceptual assessment of environmental stability modulates postural sway
Bibliographic record
Abstract
We actively maintain postural equilibrium in everyday life, and, although we are unaware of the underlying processing, there is increasing evidence for cortical involvement in this postural control. Converging evidence shows that we make appropriate use of 'postural anchors', for example static objects in the environment, to stabilise our posture. Visually evoked postural responses (VEPR) that are caused when we counteract the illusory perception of self-motion in space (vection) are modulated in the presence of postural anchors and therefore provide a convenient behavioural measure. The aim of this study is to evaluate the factors influencing visual appraisal of the suitability of postural anchors. We are specifically interested in the effect of perceived 'reality' in VR the expected 'stability' of visual anchors. To explore the effect of 'reality' we introduced an accommodation-vergence conflict. We show that VEPR are appropriately modulated only when virtual visual 'anchors' are rendered such that vergence and accommodation cues are consistent. In a second experiment we directly test whether cognitive assessment of the likely stability of real perceptual anchors (we contrast a 'teapot on a stand' and a 'helium balloon') affects VEPR. We show that the perceived positional stability of environmental anchors modulate postural responses. Our results confirm previous findings showing that postural sway is modulated by the configuration of the environment and further show that an assessment of the stability and reality of the environment plays an important role in this process. On this basis we propose design guidelines for VR systems, in particular we argue that accommodation-vergence conflicts should be minimised and that high quality motion tracking and rendering are essential for high fidelity VR.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".