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Record W2769031909 · doi:10.1177/0013916519882775

A Simulated Walk in Nature: Testing Predictions From the Attention Restoration Theory

2019· article· en· W2769031909 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnvironment and Behavior · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsWestern University
Fundersnot available
KeywordsTreadmillPsychologyCognitive psychologyCognitionPhysical therapy

Abstract

fetched live from OpenAlex

Attention restoration theory (ART) predicts that top-down processing during everyday activities can cause attentional fatigue and that bottom-up processing that occurs when people experience nature will be restorative. This study exposed participants to three different conditions using a repeated measures design: a control condition during which participants walked on a typical treadmill; a nature condition during which participants walked on the same treadmill, experiencing a simulated nature walk; and a perturbation condition that included the same simulated nature scene but also required top-down processing during the walk. The findings supported ART predictions. As measured by the backward digit span test, top-down processing in a simulated natural environment nullified the restorative effects and the nature condition produced a significant improvement in directed attention performance compared to the control and perturbation conditions after a 10-min walk. These findings offer practical insights to enhance cognitive functioning through simulated natural environments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.231
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it