Viewing nature scenes reduces the pain of social ostracism
Bibliographic record
Abstract
In a series of four studies (Ns = 245, 135, 155, 222), we explored the effects of viewing nature scenes on promoting recovery from ostracism. We first manipulated experiences of ostracism, then randomly assigned participants to view photos of either nature, urban scenes, or neutral objects. Across all four studies, participants who viewed nature photos reported significantly lower levels of state social pain, along with significantly higher levels of affect balance and self-esteem. Moreover, when asked to look back and recall how they felt at the time of being ostracized, participants who viewed nature photos reported significantly higher levels of retrospective satisfaction of basic emotional needs than did participants in control conditions. An internal meta-analysis revealed an effect size of d = 0.58. These studies are the first, to our knowledge, to provide experimental evidence of how exposure to nature can alleviate the pain of social ostracism.
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How this classification was reachedexpand
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 | 0.003 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".