Evaluating a Novel Virtual Reality Stress Induction Against a Standardised Laboratory-Based Paradigm.
Bibliographic record
Abstract
To observe and quantify authentic stress responses in experimental contexts, we need techniques that are under tight experimental control as well as convenient, replicable, ethical and flexible. While much progress has been made using traditional laboratory-based methods of stress induction, we suggest that there is great potential in applying virtual reality (VR) since it enables a diverse range of stressors to be consistently delivered in the form of rich and immersive experiences. To validate these VR stressors, however, we must assess their psychophysiological impact and compare them to established lab-based paradigms. In the current study, we recorded the subjective responses to two virtual reality stressors (“Spiders” and “Horror”). Using a repeated measures design, levels of VR-induced stress were compared to a speeded arithmetic task with negative evaluative feedback (a modified version of the Montreal Imaging Stress Task (MIST)) in healthy participants. For a subset of participants, basic physiological data (mean heart rate (HR) and respiratory frequency) are available. Both MIST and VR produced significant increases in subjective ratings of stress, desire for avoidance, negative affect, and perceived threat, as well as in respiratory frequency. Sizes of effect were comparable across the stressors, apart from negative affect and perceived threat, which were greater for the MIST. Overall, VR allows effective stress induction in healthy participants. Moreover, it was associated with a number of advantages, including convenience, and a lack of need for participant deception. These findings support the use of VR for inducing and exploring patterns of stress responses in humans.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".