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Understanding the Benefits of Musical Training

2009· review· en· W1620348020 on OpenAlexafffund
Laurel J. Trainor, Antoine J. Shahin, Larry E. Roberts

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2009
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcMaster UniversityBaycrest Hospital
FundersCanadian Institutes of Health Research
KeywordsMusicalPsychologyCognitionCognitive psychologyExecutive functionsNeuroscienceArt

Abstract

fetched live from OpenAlex

A number of studies suggest that musical training has benefits for other cognitive domains, such as language and mathematics, and studies of children and adults indicate structural as well as functional differences between the brains of musicians and nonmusicians. The induced gamma-band response has been associated with attentional, expectation, memory retrieval, and integration of top-down, bottom-up, and multisensory processes. Here we report data indicating that the induced gamma-band response to musical sounds is larger in adult musicians than in nonmusicians and that it develops in children after 1 year of musical training beginning at age 4.5 years, but not in children of this age who are not engaged in musical lessons. We conclude that musical training affects oscillatory networks in the brain associated with executive functions, and that superior executive functioning could enhance learning and performance in many cognitive domains.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.944
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0030.000
Research integrity0.0000.001
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 head0.646
GPT teacher head0.436
Teacher spread0.209 · 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 designOther design
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

Citations122
Published2009
Admission routes2
Has abstractyes

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