Investigating the Effect of Visual Threat in Virtual Reality on Perceiving Postural Instability Onset
Bibliographic record
Abstract
Abstract Background Sensory information processing plays a crucial role in monitoring the timing of external and internal events, including the control of balance. While previous research has investigated the role of vision in the perceived timing of postural instability onset with eyes closed and open, it is important to further explore the influence of visual context. Virtual reality offers a unique opportunity to manipulate visual information and assess its impact on balance control and the perceived timing of sensory events. Research Question Does visual information, particularly visual threat presented in virtual reality, alter the perceived timing of postural instability onset? Methods Two temporal order judgment tasks were conducted using virtual reality to manipulate visual information. Participants were placed on a virtual skyscraper to induce visual threat. The experiments investigated the impact of visual information on the perceived onset of postural instability while manipulating the presence/absence of visual threat. Results With vision available but without visual threat, the onset of a postural perturbation needed to occur 10.71-12.33 ms before a reference sound stimulus to be perceived as simultaneous. With visual threat, the onset needed to occur 4.45 ms before auditory cue onset to be perceived as simultaneous. While these delays were not significantly different from true simultaneity of perturbation and sound onset, participants were significantly more precise in their judgments when threatening visual information was present. Significance Our results show that visual context, particularly visual threat presented in virtual reality, may alter the perception of perturbation onset and the precision of judgments made. This has implications for understanding the role of vision in balance control and developing interventions to improve balance and prevent falls.
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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 | 0.001 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".