Collision avoidance behaviours while young adults avoid a virtual pedestrian approaching on a 45° angle under attentionally demanding conditions
Bibliographic record
Abstract
Individuals rely on visual information to determine when to adapt their behaviours (i.e., by changing path and/or speed) to avoid an approaching object or person. After initiating an avoidance behaviour, individuals may control the space (i.e., minimum clearance distance) between themselves and another person or object. The current study aimed to determine the action strategies of young adults while avoiding a virtual pedestrian approaching along a 45° angle in an attentionally demanding task. Twenty-one young adults (22.9 ± 1.9 yrs., 11 males) were immersed in a virtual environment and were instructed to walk along a 7.5 m path towards a goal located along the midline. Two virtual pedestrians (VP) positioned 2.83 m to the left and right of the midline approached participants on a 45° angle. To manipulate the point at which the participants and the VP would intersect during different trials, the VP approached at one of three speeds: 0.8×, 1.0×, or 1.2× each participants' average walking speed. Participants were instructed to walk to a goal without colliding with the VP while performing the attention task; reporting whether a shape changed above the VPs' heads. Results revealed that young adults did not modulate their timing of avoidance to the approach characteristics of the VP, as they consistently avoided the collision 1.67 s after the VP began moving. However, young adults seem to control how they avoid an oncoming collision by maintaining a consistent safety margin after an avoidance behaviour was initiated.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".