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Record W2169754703 · doi:10.1080/02640410600947165

Demonstration as a rate enhancer to changes in coordination during early skill acquisition

2007· article· en· W2169754703 on OpenAlexaff
Robert R. Horn, Abigail Williams, Spencer J. Hayes, Nicola J. Hodges, M. Scott

Bibliographic record

VenueJournal of Sports Sciences · 2007
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMovement (music)Dreyfus model of skill acquisitionBall (mathematics)Motor learningComputer sciencePsychologyPhysical medicine and rehabilitationCognitive psychologySimulationControl (management)Control theory (sociology)Artificial intelligenceMathematicsNeuroscienceMedicinePhysicsAcoustics

Abstract

fetched live from OpenAlex

We compared the nature and rate of change in intra-limb coordination in participants who observed a video model (model) with those who practised based on verbal guidance only (control). Sixteen male novices threw a ball towards a target with maximal velocity using a back-handed, reverse baseball pitch. Participants in the model group immediately changed their intra-limb relative motion to more closely resemble the model's relative motion pattern. This new coordination pattern, and concomitant changes in ball speed, was maintained throughout acquisition, without further change. In contrast, the control group showed no change in coordination or ball speed across acquisition. Our findings suggest that demonstrations act as a rate enhancer, conveying an immediate movement solution that is adopted early in acquisition. A model may constrain the learner to perceive and imitate the model's relative motion pattern as suggested by Scully and Newell (1985). The stability of this new movement pattern questions accounts of learning, which suggest that prescriptive, directed learning may result in the "soft assembly" of an inaccurate and temporary movement solution.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.228

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.017
GPT teacher head0.280
Teacher spread0.263 · 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 designBench or experimental
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

Citations35
Published2007
Admission routes1
Has abstractyes

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