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Record W2752582799 · doi:10.3389/fpsyg.2017.01546

The Impact of Bodily States on Divergent Thinking: Evidence for a Control-Depletion Account

2017· article· en· W2752582799 on OpenAlexaff
Yanyun Zhou, Yifei Zhang, Bernhard Hommel, Hao Zhang

Bibliographic record

VenueFrontiers in Psychology · 2017
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsEmily Carr University of Art and Design
FundersSouthwest University
KeywordsLyingPsychologyTask (project management)SittingCognitionControl (management)Cognitive psychologyDivergent thinkingCognitive resource theoryComputer scienceArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

Given previous evidence that bodily states can impact basic cognitive processes, we asked whether such impact can also be demonstrated for creative cognition. In particular, we had participants perform a design improvement task and a consequences imagination task while standing up, walking in a predetermined pattern, or walking freely. Results show better divergent-thinking performance with unconstrained than with constrained walking, and better performance for walking than for standing. A second experiment assessed performance in an alternative uses task and a figural combination task while participants were lying, sitting, or standing. Results showed better performance when standing up than when lying or sitting. Taken altogether, these observations provide evidence for an approach in terms of cognitive-control depletion: the more a bodily activity exhausts control resources, the better divergent thinking can unfold, presumably because reduced top-down control brings more ideas into play.

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)0.004
metaresearch head score (Gemma)0.013
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.087
GPT teacher head0.463
Teacher spread0.377 · 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

Citations44
Published2017
Admission routes1
Has abstractyes

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