Modes of immersion and stress induced by commercial (off-the-shelf) 3D games
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
Developing a stress-management training (SMT) system and protocol for soldiers can help them cope better with stress experienced in theatre operations. Using 3D horror games in virtual reality (VR) can present an attractive simulation method for soldiers. This study was conducted to find out whether it is possible to stress soldiers moderately using VR and which technology is more efficient to do so. A total of 47 soldiers returning from Afghanistan played two 3D first-person shooter (FPS)/horror games (Killing Floor and Left 4 Dead) on three different types of immersive technologies (a 22-inch stereoscopic monitor, a 73-inch stereoscopic TV and a CAVE™). As a control and reference comparison of induced stress, participants were exposed to the Trier Social Stress Test (TSST), a standardized stress-inducing procedure. Results were supporting of our work, devising an effective low-cost and high-buy-in approach to assist in teaching and practicing stress-management skills. Repeated measures analyses of variance (ANOVAs) revealed statistically significant increases in the soldiers’ respiration rates and heart rates while playing the 3D games and during the TSSTs. No significant interactions were found. Increases in physiological arousal among the soldiers were significant when comparing the baseline to the immersion and to the TSST, but not when comparing both stressors. Immersion in 3D games is proposed as a practical and cost-effective option to create a context that allows practicing SMT.
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 | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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