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Record W2020626168 · doi:10.1196/annals.1360.030

Time Course of Retrieval and Movement Preparation in Music Performance

2005· review· en· W2020626168 on OpenAlexafffund
Caroline Palmėr

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2005
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMovement (music)Computer scienceCourse (navigation)CascadeMotion (physics)Sequence learningArtificial intelligenceChemistry

Abstract

fetched live from OpenAlex

Music performance requires that musicians represent many different kinds of sequence structure: musicians must remember which pitch to produce, when to produce it, and how to produce it (with what movements). The time course of item retrieval and movement preparation processes during music performance are considered. Serially ordered stage models of retrieval, in which item retrieval ends before movement preparation begins, are compared with interactive cascade models, in which the time course of both processes overlap, permitting interaction. Evidence from transfer of learning paradigms, production errors, and anticipatory movements, as measured in motion capture, are described. This early evidence suggests different time courses for item retrieval (slower, earlier) than for movement preparation (faster, later) with significant temporal overlap during music performance.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.003

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 head0.211
GPT teacher head0.410
Teacher spread0.200 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations19
Published2005
Admission routes2
Has abstractyes

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Same venueAnnals of the New York Academy of SciencesSame topicNeuroscience and Music PerceptionFrench-language works237,207