Navigating Virtual Collisions: Insights Into Perception–Action Differences in Children and Young Adults Using a Head-On Avoidance Task
Bibliographic record
Abstract
Children tend to make more last-minute locomotor adjustments than adults when avoiding stationary obstacles. The purpose of this study was to compare avoidance behaviors of middle-aged children (10–12 years old) with young adults during a head-on collision course with an approaching virtual pedestrian. Participants were immersed in a virtual environment and completed a perceptual decision-making task in which they had to respond to the future direction of an approaching virtual pedestrian once they disappeared. Following the perceptual task, participants walked along an 8-m pathway toward a goal, while avoiding a collision with a virtual pedestrian who approached along the midline than veered toward the left, right, or continued walking straight. Results revealed that children were able to correctly predict the future directions of the virtual pedestrian similar to adults, albeit at a slower response time (0.55 s vs. 0.33 s). During the action task, children initiated a deviation in their travel path later (i.e., closer to the virtual pedestrian) compared to adults (1.65 s vs. 1.52 s). Children were also more variable in their onset of deviation and time-to-contact. Although children appear to have developed adult-like perceptual abilities and can avoid an approaching virtual pedestrian, children employ riskier avoidance strategies and are highly variable, suggesting middle-aged children are still fine-tuning their perception-action system.
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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 | 0.001 | 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) | 0.001 | 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".