Restorative Effects of Visual and Pictorial Spaces After Stress Induction in Virtual Reality
Bibliographic record
Abstract
Exposure to nature has been shown to have a positive effect on people’s mental health. Little research has compared restorative effects of simulated nature presented by different media. Here, we investigated stress recovery when viewing a computer-generated nature setting presented in visual and pictorial space in virtual reality. Participants experienced a stress induction task and were then put into one of two relaxation scenarios: they either viewed the nature scene in visual space, (they were immersed into it; presence condition), or they viewed a large depiction of it in pictorial space (picture condition). Participants’ affective state was assessed before and after stress induction, and after relaxation using the ZIPERS questionnaire. We additionally recorded electrodermal activity as a measure of physiological arousal. The results revealed that relaxation led to an increase in positive affect scores and a decrease in electrodermal activity only in the presence condition. The negative affect scores decreased significantly for both conditions similarly. Our results show that restoration is more effective in visual than in pictorial space.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".