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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 OpenAlexaff
Corey Crossan, Alan W. Salmoni

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations63
Published2019
Admission routes1
Has abstractyes

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