Increasing presence via a more immersive VR system increases virtual reality analgesia and draws more attention into virtual reality in a randomized crossover study
Bibliographic record
Abstract
Introduction Researchers frequently speculate that Immersive Virtual Reality (VR) diminishes pain by reducing how much attention is available to process nociceptive signals, but attention has rarely been measured in VR analgesia studies. Methods The current study measured how much attention VR uses. Using a repeated measures crossover design, 72 college students (mean = 19 year old) gave pain ratings (0–10 GRS scale) during brief painful but safe and tolerable heat stimulations during No VR, vs. immersive VR vs. semi-immersive VR (treatment order randomized). Results Compared to semi-immersive VR, during immersive VR, participants reported a significant 25% drop in pain intensity, and a significant 23% increase in fun during the pain stimulus, (p < .001 for each measure). Discussion As predicted by an attention mechanism for how VR reduces pain (the distraction hypothesis), participants made significantly more mistakes on an attention-demanding odd-number divided-attention task during the immersive VR condition than during the less immersive VR condition. Secondary analyses also showed that immersive VR was still effective at higher pain intensity levels, and was widely effective regardless of gender, race, or participant’s tendency to catastrophize.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".