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Record W4317039931 · doi:10.26685/urncst.419

Uncovering How Musicians Develop Perfect Pitch: A Literature Review

2023· review· en· W4317039931 on OpenAlexaff
Steven Yang

Bibliographic record

VenueUndergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal · 2023
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNeuroimagingMusicalPsychologyCognitive psychologyLanguage developmentPeriod (music)Function (biology)Absolute pitchBrain functionLanguage acquisitionFrame (networking)PhenomenonCognitive scienceLinguisticsDevelopmental psychologyComputer scienceNeuroscienceEvolutionary biologyBiologyMathematics educationEpistemologyPerceptionLiterature

Abstract

fetched live from OpenAlex

Introduction: Absolute pitch (AP), or more commonly known as “perfect pitch”, is the rare ability to label pitches without needing a reference note. While its mechanisms are still unknown, there are many different theories regarding how and why some individuals develop AP, while others do not. Some argue that the ability is genetically inherited, while others argue that AP is a skill preferentially learned at a young age, with links to musical training and tonal language learning. This review investigates the possible factors that lead to AP development, as well as possible neurobiological underpinnings of the ability. Methods: A literature search was conducted using scientific databases to reveal studies examining the impact of genetic, learning, musical training, language, and neuroimaging on AP development. Empirical studies written in English were examined for this review, with emphasis on how AP is differentially reflected in developmental and neuroimaging perspectives. Results: Genetics, learning, musical training, and language all play a role in AP development, with some having a greater impact than others. Young children are easily taught absolute pitch using training programs, while it is much more difficult in adults. Furthermore, the apparent critical period for AP development closely resembles the time frame of the critical period of language acquisition. The phenomenon of AP is apparent through localized brain function, which differs from non-AP individuals. Discussion: Genetic, musical training, and language all play an interconnected role in the development of AP. However, neuroimaging research is vastly separate, not integrating findings from other areas. Overall, looking at the various factors combined allows for a more complete understanding of AP. Conclusion: Currently, there is a lack of research linking genetic and environmental influences on AP development with specific brain structure and function, which future neuroimaging research should seek to do. Understanding AP development benefits musicians and furthers an understanding of human perception of the auditory environment.

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)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0120.010
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.210
GPT teacher head0.477
Teacher spread0.267 · 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 designSystematic review
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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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