Attenuation of the visual control of balance under virtual postural threat
Bibliographic record
Abstract
Background: Virtual reality (VR) can be used to induce the sensation of self-motion. In presenting a visual stimulus to elicit balance correcting responses, the visual control of balance may be evaluated. It is unclear what type of visual stimulus should be presented to optimally perturb balance and whether subsequent responses are influenced by a threat to posture. Objectives: 1. Explore stimuli relevant for the visual control of balance in VR. 2. Determine whether the use of vision for balance control is influenced by postural threat. Methods: 32 healthy, young adults (18 males) completed 5-minute, standing trials in VR with LOW (0m) and HIGH (7m) height conditions. A continuous, stochastic visual stimulus (frequencies: 0-1Hz, amplitude: +/-5cm) was presented as an anteroposterior translation. Outcome measures included center of pressure (COP), kinematic displacements, electromyography, psychological states, and electrodermal activity (EDA). Results: Significant coherence (between stimulus and COP) was observed across frequencies (~0.2-1Hz) at both LOW and HIGH. There was no difference of coherence between conditions, however, the gain was significantly larger at ~0.4Hz for LOW. Two cumulant density peaks were identified (~0.5s and ~1.9s) with significantly larger amplitudes observed in the LOW condition. At HIGH, there were significant increases in reported anxiety, fear, and EDA, and significant decreases in perceived stability and confidence, when compared to LOW. Conclusions: A wide range of frequencies relevant for the visual control of balance were identified with the stochastic visual stimulus. Results suggest a decrease in the sensory gain of the visual system under postural threat.Acknowledgments: The authors acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC).
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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 | 0.000 | 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".