Adaptation in obstacle circumvention strategies performed in virtual reality in response to repeated practice
Bibliographic record
Abstract
Background: Virtual reality (VR) is increasingly being incorporated into research and clinical practice. Its usefulness, however, remains largely dependent on the extent to which it elicits human behaviors that are similar to those observed in the real world. Objectives: In this study, we estimated whether obstacle avoidance strategies while walking in response to pedestrians approaching from different directions differed when performed in a virtual (VE) vs. physical environment (PE). We also examined the extent to which avoidance strategies in the VE change with repeated practice. Methods: Participants (n=4) were assessed while walking overground and avoiding moving pedestrians in both a VE and a PE, in a random order. Three female collaborators acted as interferers in the PE experiment. For the VE experiment, we created avatars using full body kinematics recorded from the actors. The task consisted of walking towards a target while avoiding one of the interferers, which could approach from either the left or right side (±30°), or from the center (0°). In a fourth condition, all interferers walked away (no obstacle). Results: Delayed onset times of avoidance strategy and slower walking speeds were observed in the VE vs. PE. With practice in the VE, there was a progressive decrease in onset time of avoidance strategy and an increase in walking speed. Differences between the two environments, however, persisted by the end of the experiment. Maximal trajectory deviation and distance at onset of avoidance strategy remained comparable between the VE and PE throughout the experiment. Conclusion: Obstacle avoidance behavior in response to moving pedestrians differs in the VE vs. PE, although spatial parameters (e.g. distance at the onset and maximum lateral deviation) appear to be similar. Differences decrease but remain present despite of repeated exposure to the VE. Results should be taken into consideration when using the VR technology for assessment or training purposes.
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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 | 0.007 |
| 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) | 0.001 | 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".