Increasing presence via a more immersive VR system increases virtual reality analgesia and draws more attention into virtual reality in a randomized crossover study
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
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.
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 | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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