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Record W2345456182 · doi:10.1089/eco.2015.0042

Does Taking a Walk in Nature Enhance Long-Term Memory?

2016· article· en· W2345456182 on OpenAlexafffund
Nathan D. Rider, Glen E. Bodner

Bibliographic record

VenueEcopsychology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRecallPsychologyCognitive psychologyLong-term memoryTerm (time)CognitionNatural (archaeology)Word (group theory)Key (lock)Contrast (vision)Computer scienceArtificial intelligenceNeuroscienceLinguistics

Abstract

fetched live from OpenAlex

Given recent evidence that contact with nature can enhance cognitive processes, we measured whether students who took a brief on-campus walk in a natural environment showed improved retention of learned materials. Using a within-subjects design, we compared the effects of 10-minute walks in nature, urban, and indoor environments on long-term memory for word lists. Recall and recognition for word lists were tested in the indoor environment either after each walk (Experiment 1) or before each walk (Experiment 2). We failed to find an influence of walk type on either memory test in either experiment. Thus, contact with nature did not enhance students' long-term memory under the conditions we tested. Our results contrast with a recent study in which learners showed better memory for lecture materials learned in a nature-enhanced classroom than in a control classroom. We identify potential explanations for our null findings and suggest future research directions. Key Words: Nature—Environment—Memory—Recall—Recognition.

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 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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
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)0.0270.001

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 head0.005
GPT teacher head0.297
Teacher spread0.292 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations13
Published2016
Admission routes2
Has abstractyes

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