Stay Out of the Blast Radius: Influence of Surgical Masks on Virtual Pedestrian Interactions
Bibliographic record
Abstract
Abstract To combat the global pandemic caused by COVID-19, a series of mitigation strategies have been proposed by governments around the world. While responses varied across different governing bodies, recommendations such as social distancing and the use of facial masks were nearly universal. Considering that even with restrictions in place, walking in community environments remained an important component of everyday life, these public health recommendations, as well as the anxiety generated by the pandemic, are likely to have influenced pedestrian interactions. In this study, we have examined the effect of facial masks and anxiety related to community ambulation in the context of the COVID-19 pandemic. Using virtual reality, obstacle circumvention strategies in response to approaching pedestrians with and without facial masks were measured in a sample of 11 healthy young individuals. Additionally, a questionnaire was developed and used to gain insights into the participant's behaviours during and after a strict period of restrictions that were in effect before the summer of 2020. Results showed that participants maintained a larger obstacle clearance when virtual pedestrians wore a facial mask. The extent of obstacle clearance was also positively associated with anxiety towards community ambulation in the context of the pandemic. Our findings provide evidence that mask-wearing results in an increase in physical distancing during pedestrian interactions, which may help to reduce the risk of infection. Furthermore, results demonstrate the effects of social context and psychological status on pedestrian interactions and highlight the potential of virtual reality simulations to study locomotion in natural community settings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".