Immersive videos of natural and urban environments can enhance awe and psychological well-being
Bibliographic record
Abstract
Experiencing the emotion of awe has been associated with improvements in psychological wellbeing. This emotion can be systematically elicited in laboratory settings and immersive virtual reality (VR) has been shown effective for this purpose. In this work, we exposed 36 healthy participants to three immersive videos from natural and urban scenes (i.e., mountain, forest with waterfall, and city), and a 3D model of a neutral room as a baseline condition. These environments were compared in terms of self-reported levels of awe and clinically relevant aspects of psychological wellbeing, such as state depression and anxiety. In addition, we took the level of prior experience of the participants with VR into account and investigated whether the psychological effects hold for both novice and experienced VR users. The results suggest that exposure to all three immersive videos elevated the level of awe, reduced current states of depression, and increased positive affect compared to the baseline. We also discovered that, while the urban environment elicited the same amount of awe as both natural environments, only exposure to natural environments decreased current states of anxiety and negative affect. Finally, although experienced VR users had partly lower overall scores, prior experience did not reduce the relative benefits of exposure to immersive videos, as both experienced and novice users showed similar improvements compared to their respective baselines. Our findings can help guide future research and therapeutic applications that use immersive videos to harness the psychological benefits of experiencing awe.
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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) | 0.004 | 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".