Simulated nature walks improve psychological well-being along a natural to urban continuum
Bibliographic record
Abstract
Compared to urban environments, interactions with natural environments have been associated with several health benefits including psychological restoration and improved emotional well-being. However, classifying environments dichotomously as either natural or urban may emphasize between-category differences and minimize potentially important within-category variation (e.g., forests versus fields of crops; neighborhoods versus city centers). Therefore, the current experiment assessed how viewing brief videos of different environments, ranging along a continuum from stereotypically natural to stereotypically urban, influenced subjective ratings of mood, restoration, and well-being. Participants were randomly assigned to one of four video conditions, which depicted a simulated walk through a pine forest, a farmed field, a tree-lined urban neighborhood, or a bustling city center essentially devoid of greenery. Immediately before and after the videos, participants rated their current emotional states. Participants additionally rated the perceived restorativeness of the video. The results supported the idea that the virtual walks differentially influenced affect and perceived restoration, even when belonging to the same nominal category of natural or urban. The pine forest walk significantly improved happiness relative to both urban walks, whereas the farmed field walk did not. The bustling city center walk decreased feelings of calmness compared to all other walks, including the tree-lined neighborhood walk. The walks also differed on two perceived restorativeness measures (daydreaming and being away) in a graded fashion; however, the farmed field walk was found to be less fascinating than all other walks, including both urban walks. Taken together, these results suggest that categorizing environments as “natural versus urban” may gloss over meaningful within-category variability regarding the restorative potential of different physical environments.
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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) | 0.003 | 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".