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Record W90494189 · doi:10.1123/tsp.22.1.1

The Effects of Image Speed on the Performance of a Soccer Task

2008· article· en· W90494189 on OpenAlexaff
O Jenny, Krista J. Munroe‐Chandler

Bibliographic record

VenueThe Sport Psychologist · 2008
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of WindsorWestern University
Fundersnot available
KeywordsMotor imageryPsychologyMotion (physics)Task (project management)Mental imageCognitive psychologyPhysical medicine and rehabilitationComputer visionComputer scienceCognitionEngineeringElectroencephalographyMedicine

Abstract

fetched live from OpenAlex

The current study tested the timing element of the PETTLEP approach to motor imagery (Holmes & Collins, 2001) by examining the effects of 3 imagery conditions on the performance of a soccer dribbling task. The imagery conditions were also compared with physical-practice and control-group performance. Ninety-seven participants were randomly assigned to 1 of 5 conditions: real-time imagery, slow-motion imagery, slow motion concluded with real-time imagery, physical practice, or control. Results indicated that all 4 experimental groups significantly improved time and error performance to the same degree after the intervention. The control group significantly improved time but not error performance from pre- to post-intervention. The results of the current study provide inconclusive findings related to the timing element of the PETTLEP approach to motor imagery, however, and do suggest that slow motion might be a viable imagery characteristic. Limitations regarding the examination of slow-motion imagery, possible implications of its use, and suggestions for future image-speed research are discussed.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.999

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 studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.022
GPT teacher head0.308
Teacher spread0.286 · 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

Citations37
Published2008
Admission routes1
Has abstractyes

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