People with sensory processing sensitivity connect strongly to nature across five dimensions
Bibliographic record
Abstract
Human connections to nature are critical to the sustainability of life on Earth. Nature connections are often linked with pro-environmental behaviors. Therefore, a better understanding of the nuances of human-nature connections can help inform policies and practices to advance sustainability. Connections to nature research has rarely investigated nuances like the nature connections of subpopulations or distinguished between types of nature connections. This article reports on an investigation of the subpopulation of highly sensitive individuals (HSPs), a group comprising about one-third of the general population thatexhibits higher levels of sensitivity to stimuli, greater depth of processing, and stronger emotional and physiological reactivity to both positive and negative stimuli than the general population, a suite of traits known as “sensory processing sensitivity” or “environmental sensitivity.” We assessed the nature connections of this group across five nature-connection types: material, experiential, cognitive, emotional, and philosophical. We found that HSPs hold deeper nature connections than less sensitive individuals with respect to all five connection types. Additionally, variability in nature-connection scores decreased as sensitivity increased, showing a significant trend. Future research can investigate links between nature connections and pro-environmental behavior in this population and the potential mediating role of specific psychological characteristics. We also recommend that researchers consider including sensitivity in future connections to nature assessments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".