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Record W2095967751 · doi:10.1123/jsep.30.4.432

A Social Neuroscience Perspective on Physical Activity

2008· article· en· W2095967751 on OpenAlexaff
Peter A. Hall, Lorin Elias, Geoffrey T. Fong, A Harrison, Ron Borowsky, Gordon E. Sarty

Bibliographic record

VenueJournal of Sport and Exercise Psychology · 2008
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversité de MontréalUniversity of SaskatchewanUniversity of Waterloo
Fundersnot available
KeywordsStroop effectPsychologyDorsolateral prefrontal cortexAnterior cingulate cortexCognitionPrefrontal cortexPerspective (graphical)Orbitofrontal cortexCognitive psychologyNeuroscience

Abstract

fetched live from OpenAlex

The objective of this investigation was to examine the cognitive characteristics of individuals who demonstrate successful and unsuccessful self-regulation of physical activity behavior. In Study 1, participants articulated 1-week intentions for physical activity and wore a triaxial accelerometer over the subsequent 7 days. Among those who were motivated to increase their physical activity, those who were most and least successful were administered an IQ test. In Study 2, a second sample of participants completed the same protocol and a smaller subset of matched participants attended a functional imaging (fMRI) session. In Study 1, successful self-regulators (SSRs) scored significantly higher than unsuccessful self-regulators (USRs) on a test of general cognitive ability, and this difference could not be accounted for by favorability of attitudes toward physical activity or conscientiousness. In Study 2, the IQ effect was replicated, with SSRs showing a full standard deviation advantage over USRs. In the imaging protocol, USRs showed heavier recruitment of cognitive resources relative to SSRs in the anterior cingulate and orbitofrontal cortex during performance of a Stroop task; SSRs showed heavier recruitment in the right dorsolateral prefrontal cortex.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.368

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)0.0000.000

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 head0.104
GPT teacher head0.465
Teacher spread0.361 · 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 teacher head, 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

Citations25
Published2008
Admission routes1
Has abstractyes

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