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Record W2762618839 · doi:10.1177/0276236617735101

Imagery as a Skill: Longitudinal Analysis of Changes in Motivational Imagery

2017· article· en· W2762618839 on OpenAlexaff
Melanie Gregg, Craig Hall

Bibliographic record

VenueImagination Cognition and Personality · 2017
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsWestern UniversityUniversity of Winnipeg
Fundersnot available
KeywordsMental imageAthletesPsychologyPsychological interventionGuided imageryCreative visualizationCognitive psychologyCognitionApplied psychologyComputer scienceVisualizationMedicinePhysical therapyArtificial intelligenceAnxiety

Abstract

fetched live from OpenAlex

Imagery intervention research indicates that athletes improve their imagery skill through practice. As a skill, imagery is expected to improve over time only if athletes increase their use of imagery (i.e., engage in imagery practice). Forty-four track-and-field athletes striving for selection to national games completed the Sport Imagery Questionnaire to assess imagery use and the Motivational Imagery Ability Measure to assess imagery ability. The athletes completed the measures three times within 13 months prior to the games. Although cognitive general imagery use increased over time, paired t-tests indicated that there were no other significant changes across the functions of imagery use. For motivational imagery ability, there were minimal changes from Time 1 to Time 3 indicating the athletes’ motivational imagery ability remained relatively stable. These results add support for targeted imagery interventions as athletes do not spontaneously or independently begin to increase their use of imagery; athletes need purposeful interventions to realize improvements in imagery skills.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0060.000

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 head0.052
GPT teacher head0.395
Teacher spread0.343 · 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.

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

Citations9
Published2017
Admission routes1
Has abstractyes

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