Investigating Dynamics of Subjective Anxiety and Behavior Due to Personal Space Violations and COVID-19-Related Stressors in a Social VR Simulation
Bibliographic record
Abstract
Personal space is the physical distance individuals prefer to maintain from others, and its violation induces stress, anxiety, and behavioral change—a phenomenon intensified during the COVID-19 pandemic. While prior research has largely relied on postexperiment questionnaires in passive, one-to-one scenarios, few studies have examined dynamic, crowd-based settings, and to our knowledge, none have collected time-resolved subjective anxiety aligned with moment-to-moment environmental stimuli. We introduce a novel social VR simulation that enables such dynamic analysis by combining continuous self-reports of anxiety with highresolution behavioral tracking during a two-stage, goal-oriented task. In Stage 1, participants navigated immersive VR environments (indoor/outdoor) while encountering virtual agents—some invading personal space, coughing, or (not) wearing masks. In Stage 2, they retrospectively annotated their experiences using first-person video playback. We also administered personality and COVID-related questionnaires. Across$N=109$participants, we collected over 40,000 seconds of annotated data ($\approx 374.56$s per person). Key findings include: 1) Personal space invasions significantly increased anxiety and movement, 2) Coughing agents elevated stress responses, while masks had no significant effect, and 3) Goal achievement consistently reduced anxiety, overriding other stressors. These results provide fine-grained insight into the temporal dynamics of emotional and behavioral responses to social stressors, and offer new design implications for social VR environments.
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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 | 0.000 | 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".