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Record W2891729719 · doi:10.1002/jocb.373

Change it Up: Inactivity and Repetitive Activity Reduce Creative Thinking

2018· article· en· W2891729719 on OpenAlexafffund
Kelley Main, Hamed Aghakhani, Aparna A. Labroo, Nathan Greidanus

Bibliographic record

VenueThe Journal of Creative Behavior · 2018
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsDalhousie UniversityUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFeelingSittingFlexibility (engineering)MoodPsychologyPhysical activityCreativityCreative thinkingCognitive psychologySocial psychologyDevelopmental psychologyPhysical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

Abstract Across three experiments, we show that a change in the levels of physical activity increases creative thinking, whereas inactivity or repetitive activity lowers it. Participants walking forward were more creative the first few minutes of initiating physical activity than those sitting, or those merely watching changing scenery, and these effects dissipated when they continued the forward movement over time (within 8 minutes). Furthermore, merely anticipating a change in physical activity, for example, when participants were aware a task is coming to its conclusion, also increased creative thinking. We hypothesize that a change in physical activity cues the need to navigate new situations, and thus, can increase mental flexibility and creative thinking to deal with new circumstances. But once people habituate to their physical state, either of being at rest or being in motion, their level of creative thinking also returns to baseline levels. We confirm that mood, feelings of achievement, and energy are not responsible for the observed effects.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

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 communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.140
GPT teacher head0.439
Teacher spread0.299 · 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 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

Citations12
Published2018
Admission routes2
Has abstractyes

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