The Differential Impact of Mystery in Nature on Attention: An Oculometric Study
Bibliographic record
Abstract
Nature exposure can provide benefits on stress, health and cognitive performance. According to Attention Restoration Theory (ART), the positive impact of nature on cognition is mainly driven by fascination. Fascinating properties of nature such as water or a winding hiking trail may capture involuntary attention, allowing the directed form of attention to rest and to recover. This claim has been supported by studies relying on eye-tracking measures of attention deployment, comparing exposure to urban and nature settings. Yet, recent studies have shown that promoting higher engagement with a nature setting can improve restorative benefits, hence challenging ART's view that voluntary attention is resting. Besides, recent evidence published by Szolosi et al. (2014) suggests that voluntary attention may be involved during exposure to high-mystery nature images which they showed as having greater potential for attention restoration. The current study explored how exposure to nature images of different scenic qualities in mystery (and restoration potential) could impact the engagement of attention. To do so, participants were shown nature images characterized by either low or high mystery properties (with allegedly low or high restoration potential, respectively) and were asked to evaluate their fascination and aesthetic levels. Concurrently, an eye tracker collected measures of pupil size, fixations and spontaneous blinks as indices of attentional engagement. Results showed that high-mystery nature images had higher engagement than low-mystery images as supported by the larger pupil dilations, the higher number of fixations and the reduced number of blinks and durations of fixations. Taken together, these results challenge ART's view that directed attention is merely resting during exposure to restorative nature and offer new hypotheses on potential mechanisms underlying attention restoration.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".