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Record W2488797099 · doi:10.1177/0305735616657407

Effects of musical training and culture on meter perception

2016· article· en· W2488797099 on OpenAlexaff
Charles M. Yates, Timothy Justus, Nart Bedin Atalay, Nazike Mert, Sandra E. Trehub

Bibliographic record

VenuePsychology of Music · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversity of Toronto
FundersPitzer College
KeywordsTurkishMusicalMetrePerceptionPsychologySimple (philosophy)Visual artsArtLinguisticsNeurosciencePhysics

Abstract

fetched live from OpenAlex

Western music is characterized primarily by simple meters, but a number of other musical cultures, including Turkish, have both simple and complex meters. In Experiment 1, Turkish and American adults with and without musical training were asked to detect metrical changes in Turkish music with simple and complex meter. Musicians performed significantly better than nonmusicians, and performance was significantly better on simple meter than on complex meter, but Turkish listeners performed no differently than American listeners. In Experiment 2, members of Turkish classical and folk music clubs who were tested on the same materials exhibited comparable sensitivity to simple and complex meters, unlike the American and Turkish listeners in Experiment 1. Together, the findings reveal important effects of musical training and culture on meter perception: trained musicians are generally more sensitive than nonmusicians, regardless of metrical complexity, but sensitivity to complex meter requires sufficient exposure to musical genres featuring such meters.

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)0.004
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.068
GPT teacher head0.332
Teacher spread0.264 · 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

Citations31
Published2016
Admission routes1
Has abstractyes

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