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Record W2103108439 · doi:10.1525/mp.2008.25.3.241

Is There an Asian Advantage for Pitch Memory?

2008· article· en· W2103108439 on OpenAlexaffabout
E. Glenn Schellenberg, Sandra E. Trehub

Bibliographic record

VenueMusic Perception An Interdisciplinary Journal · 2008
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTone (literature)MusicalPsychologyTask (project management)Cognitive psychologyAffect (linguistics)Isolation (microbiology)LinguisticsHistoryCommunicationLiteratureArtEngineeringBiology

Abstract

fetched live from OpenAlex

ABSOLUTE PITCH (AP) IS THE ABILITY TO IDENTIFY OR produce a musical note in isolation. As traditionally defined, AP requires accurate pitch memory as well as knowledge of note names. The incidence of AP is higher in Asia than it is in North America.We used a task with no naming requirements to examine pitch memory among Canadian 9- to 12-year-olds of Asian (Chinese) or non-Asian (European) heritage. On each trial, children heard two versions of a 5-s excerpt from a familiar recording, one of which was shifted upward or downward in pitch. They were asked to identify the excerpt at the original pitch. The groups performed comparably, and knowledge of a tone language did not affect performance. Nonetheless, Asians performed better on a test of academic achievement. These results provide no support for the contribution of genetics or tone-language use to cross-cultural differences in pitch memory.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.001

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 head0.091
GPT teacher head0.369
Teacher spread0.279 · 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

Citations58
Published2008
Admission routes2
Has abstractyes

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