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Record W2325298065 · doi:10.1525/mp.2015.33.2.163

Sleep Consolidation of Musical Competence

2015· article· en· W2325298065 on OpenAlexaff
Stephen C. Van Hedger, Anders Hogstrom, Caroline Palmėr, Howard C. Nusbaum

Bibliographic record

VenueMusic Perception An Interdisciplinary Journal · 2015
Typearticle
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychologyCognitive psychologyCompetence (human resources)MorningSleep (system call)MusicalSleep inertiaConsolidation (business)Motor skillEveningAudiologyDevelopmental psychologyCognitionSocial psychologySleep deprivationComputer scienceNeuroscienceMedicineSleep debt

Abstract

fetched live from OpenAlex

Previous research has shown that sleep facilitates skill acquisition by consolidating recent memories into a stabilized form. The way in which sleep benefits the acquisition of a musical skill, however, is unclear. This is because previous studies have not dissociated the extent to which sleep consolidates learned motoric patterns from the conceptual structure of the music. We thus designed two experiments in which pianists performed short pieces – designed to separate conceptual from motoric errors – over the course of a day. In Experiment 1, participants were trained in the morning and tested immediately, 12 hours, and 24 hours after training. While both motor and conceptual errors increased over a waking retention interval, only conceptual errors were significantly reduced after sleep. Moreover, individuals who reported spending more time “playing by ear” showed greater reductions of conceptual errors after sleep. A second control experiment, in which participants were trained in the evening and tested immediately – as well as 12 hours – after training, confirmed that the results from Experiment 1 could not be attributed solely to circadian confounds or to elapsed time since training. Together, these results suggest that conceptual and motor errors consolidate differently and interact with differences in practice style.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.138
GPT teacher head0.387
Teacher spread0.249 · 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 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

Citations11
Published2015
Admission routes1
Has abstractyes

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