The effect of nature on creativity through mental imagery
Bibliographic record
Abstract
Immersion in nature has been linked to wide-ranging benefits on mental health and cognitive functions, from reducing stress to enhancing creativity. However, a walk in nature is not always feasible, and whether a proxy for nature immersion via a mental walk in nature can elicit the same benefits as a physical walk remains largely unknown. Accordingly, the current study utilized guided imagery to examine whether a mental walk in nature would improve creativity in general and when compared to a mental walk in an urban environment. We implemented a within-subjects design, wherein participants completed both mental walk conditions (in a nature and urban environment) at least five days apart in counterbalanced order on an online platform. During each session, participants (N = 97) completed two pre-walk tasks assessing convergent (measured by the Remote Associates Test) and divergent creative thinking (measured by the Alternate Uses Test), followed by a mental walk in either a nature or urban environment, then finally the identical two post-walk creativity tasks. After five days, they repeated the same procedure with a mental walk in the other environment. While comparisons of post-walk creativity scores between the nature and urban environment did not significantly differ from each other, the comparisons between the pre- and post-walk creativity scores revealed a significant improvement in convergent creative thinking in the nature environment condition, but not the urban environment condition. Our results suggest that taking a mental walk in nature can enhance at least one aspect of creativity, therefore providing preliminary evidence for the potential to access the creative benefits of mentally immersing ourselves in nature. These findings have important implications for those who wish to enjoy the benefits of nature but are unable to readily access nature physically.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".