Circumvention of Pedestrians While Walking in Virtual and Physical Environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Virtual environments (VEs) are increasingly used in the context of scientific inquiries and rehabilitation for tasks that are otherwise difficult to control or perform safely in physical environments (PEs), such as avoiding other pedestrians during locomotion. The usefulness of VEs, however, remains constrained by the extent to which they can elicit natural responses. The objectives of the study were to examine circumvention strategies in response to pedestrians approaching from different directions in the VE versus PE and to determine the effects of repeated practice on the circumvention strategies. Twelve participants were assessed over five blocks of eight trials that consisted of walking toward a target while circumventing pedestrians approaching from different directions (0°, ± 30° right or left or none) in the VE and the PE. Similar onset distances of circumvention strategy and preferred side of circumvention were observed between the two environments. Participants, however, maintained enlarged minimum distances from the interferer (13%) and walked slower (11.5%) in the VE. Repeated practice resulted in walking speed increments of 7.4% over the entire session that were similar in the VE versus PE. While the changes observed in VE may reflect the use of more cautious circumvention strategies, the similarities in strategies between the two environments and the advantages of VEs (e.g., controlled exposure, reproduction of ecologically valid conditions, and safety) suggest that virtual reality is a valuable tool to study visually guided locomotor tasks, such as pedestrian circumvention, and shows great potential for assessment and intervention in physical rehabilitation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it