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Record W2074261032 · doi:10.3200/jmbr.41.4.347-356

Transfer Effects of Practice for Simple Alternating Movements

2009· article· en· W2074261032 on OpenAlexaff
Susan Koeneke, Christian Battista, Lutz Jäncke, Michael Peters

Bibliographic record

VenueJournal of Motor Behavior · 2009
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsThumbTappingTransfer (computing)Middle fingerPsychologyIndex fingerMotor learningFinger tappingTask (project management)Cognitive psychologyMovement (music)CommunicationComputer scienceAudiologyEngineeringAcousticsNeuroscienceLinguistics

Abstract

fetched live from OpenAlex

In studies on transfer of practice effects, researchers use simple or complex movements that involve a significant cognitive element. In the present study, the authors studied intermanual and intramanual transfer of practice with a task that can be considered intermediate in difficulty. Using finger tapping as a motor task, 30 participants practiced tapping 6 days per week for 2 weeks with the left or right middle finger in a between-subject design. Compared with controls, the unpracticed middle finger of both hands showed significant improvement, along with all of the other unpracticed digits. There was no significant difference in the strength of transfer from the practiced finger to other fingers of the same (intramanual transfer) or the other (intermanual transfer) hand. The authors did not observe an asymmetry of transfer effects (the degree to which transfer depends on the particular hand trained). Last, in terms of speed and regularity of movement, the digits broke down into 2 different clusters; the thumb, index finger, and middle finger formed 1 cluster superior to that formed by the ring and small fingers.

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)0.002
metaresearch head score (Gemma)0.020
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.028
GPT teacher head0.320
Teacher spread0.292 · 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

Citations20
Published2009
Admission routes1
Has abstractyes

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