Constraints matter: Virtual pedestrians with mobility constraints affect individuals' avoidance behaviours
Bibliographic record
Abstract
Walking in urban settings requires people to negotiate crowds. In these situations, people typically want to maintain a level of personal space around themselves. Recent work on one-versus-one interactions demonstrated that whether one of the pedestrians looked distracted or interacted with an object (e.g., stroller, bike) predicted the medial-lateral separation between them as they walked past each other. However, this work did not distinguish between the type of object interaction (or mobility constraint) and thus, it is unclear whether different constraints have different effects on avoidance behaviours. Here we tested the hypothesis that the type of object an approaching pedestrian held or pushed would affect the extent of path deviation, which would also depend on the distractedness of the pedestrian. To address this hypothesis, we created an immersive virtual environment that consisted of a 3.5-m-wide paved urban path. Participants had to walk and avoid colliding with approaching virtual pedestrians that often held a shopping bag or pushed a bike or stroller while looking straight ahead or off to the side as if distracted. Distraction did not affect avoidance behaviours. However, participants increased medial-lateral separation with the virtual pedestrian at the time of crossing when a stroller was present compared to the other mobility constraints. The type of mobility constraint also differentially affected onset of deviation and rate of progression before and after a path deviation. These results support the idea that characteristics of the obstacle to avoid (in this case, a virtual pedestrian) influence collision avoidance behaviours.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".