Viewing Digital Nature Scenes Not Sufficient to Enhance Verbal Creativity in Children
Bibliographic record
Abstract
Some prior studies suggest that nature exposure can bolster creativity in adults and adolescents, but little is known about the effect of nature stimuli on children's creativity. We investigated the effect of exposure to static images of natural and urban scenery on verbal creativity in children using a between-subjects repeated measures design. Fifty-five children (ages 8–15 years; mean = 11.05) were randomly allocated to the urban or nature condition and completed two verbal creativity measures (alternative uses and similarities tasks) before and after viewing 100 urban/nature images. Twenty-four participants were tested in-person and, due to COVID-19, 31 were tested online. Independent samples t-tests indicated that participants tested online versus in-person did not differ on characterization variables (e.g., age, gender, verbal cognitive ability) or creativity measure scores, and thus, the groups were combined for subsequent analyses. Repeated-measures analysis of variance did not support the hypothesis that children in the nature condition would demonstrate more improvement in verbal creativity than those in the urban condition. These findings suggest that short exposures to nature scenery were not sufficient to enhance verbal creativity in children. This research is the first to examine the effect of brief nature exposure on verbal creativity in children and provides important directions for future research.
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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 | 0.000 | 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".