Preparing the Heart for Duty: Virtual Reality Biofeedback in an Arousing Action Game Improves in-Action Voluntary Heart Rate Variability Control in Experienced Police
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
Adequate control over evolutionary engrained bodily stress reactions is essential to avoid disproportionate responses during highly arousing situations in police. This regulation can be trained via heart rate variability (HRV)-biofeedback, a widely used intervention aiming to improve stress regulation, but typically conducted under passive, low arousing conditions. We integrated closed-loop HRV-biofeedback in a newly designed engaging Virtual Reality (VR) action game containing the behavioral elements typically compromised under stress. Specifically, we aimed to train in-action physiological self-control under high arousal to allow improved transfer to real-life. A pre-registered (https://osf.io/cdsbx) quasi-randomized controlled trial in 109 police trainers demonstrated highly significant increases in HRV (32% average), through the engaging and gamified closed loop biofeedback. This ability to voluntarily upregulate in-action HRV transferred to game sessions without biofeedback (near transfer). Critically, we could additionally demonstrate transfer to a professional shooting performance assessment outside VR (far transfer). These results suggest that real time-biofeedback in stressful and active action contexts can help train professionals such as police in real-life stress regulation.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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