Perceptually Degraded Experiences with Nature Are Liked Less but Still Restorative
Bibliographic record
Abstract
Interactions with nature can improve attentional functioning and decrease mental fatigue. However, the perceptual quality of the experience might influence how effectively nature can improve these cognitive measures, as perceptual quality has been linked to how much nature sounds are liked, which may in turn influence aspects of psychological restoration. The current study manipulated the perceptual quality of both nature and urban soundscapes to examine how degraded sounds might influence the typically observed cognitive benefits of nature-based interventions. Participants (n = 227) completed a working memory task (N-back) and a self-reported mental fatigue measure before and after listening to one of four sound categories, using a 2 (sound type: unaltered, degraded) x 2 (environment: nature, urban) between-participant design. Participants additionally rated the restorativeness of the sound intervention via the Perceived Restorativeness Scale (PRS). Despite participants liking degraded sounds less and judging them as lower in sound quality, we found comparable restorative effects of unaltered and degraded nature sounds across all measures. However, the nature-related benefits for the PRS were entirely driven by how much participants liked the sounds. In contrast, the cognitive restoration measures showed nature-related benefits even after controlling for sound liking ratings and pre-intervention scores. The findings of this study suggest that nature-based restoration can be observed even when stimuli are degraded and liked less. However, measures that focus on the restorative experience itself (e.g., PRS) appear be more related to stimulus preference than measures assessing participants’ cognitive performance.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".