Behavioral risk models explain locomotor and balance changes when walking at virtual heights
Bibliographic record
Abstract
Walking in daily life requires humans to adapt to environments that can influence one's fear of falling and anxiety about a potential fall. In such environments, individuals may adopt compensatory locomotor and balance changes to maintain a constant expected risk function equal to the product of the probability of some event (e.g., a fall) and the cost of that event (e.g., injury or death). Here, we tested whether locomotor behaviors broadly align with this risk model in two experiments with height-related threats in immersive virtual reality. In Experiment 1, we examined how individuals change their locomotor trajectory while walking along a straight high-elevation walkway. In Experiment 2, we examined how individuals change trajectory and balance control during curved walking where the location of high elevation threat varied. Participants adopted two behaviors that decreased their probability of falling off the edge and aligned with the risk-based model: participants altered their proximity to perceived threats that pose high costs (e.g., a high-elevation ledge), and decreased mediolateral center of mass velocity when that was not possible. Taken together, our results suggest that individuals alter locomotor behavior to change the probability of falling based on the perceived cost of that fall.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".