Immersive videos of natural and urban environments can enhance awe and psychological well-being
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
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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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 | 0.000 | 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 | 0.001 |
| 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