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Record W4288078444 · doi:10.1177/20592043221109958

The Neural Basis of Tonal Processing in Music: An ALE Meta-Analysis

2022· article· en· W4288078444 on OpenAlexafffund
Rie Asano, Vivian Lo, Steven Brown

Bibliographic record

VenueMusic & Science · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcMaster University
FundersJapan Society for the Promotion of ScienceNatural Sciences and Engineering Research Council of Canada
KeywordsFunctional magnetic resonance imagingPsychologyCognitive psychologyInsulaMagnetoencephalographyCognitive scienceElectroencephalographyNeuroscience

Abstract

fetched live from OpenAlex

Music is used as an important medium for communication in human societies, often times to enhance the emotional meaning of narrative scenarios and ritual events. Music has a number of domain-specific tonal devices for doing this, spanning from scale structure to harmonic progressions and beyond. In order to explore the neural basis of tonal processing in music, we carried out an activation likelihood estimation (ALE) meta-analysis of 20 published functional magnetic resonance imaging studies of tonal cognition, with an emphasis on harmony processing. The most concordant areas of activation across these studies occurred at the junction of the inferior frontal gyrus, anterior insula, and orbitofrontal cortex in Brodmann areas 47 and 13 in the right hemisphere. This region is associated not only with emotion in general, but with the conveyance of affective meanings during communication processes, including speech prosody and music.

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)0.010
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.020
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.022
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.189
GPT teacher head0.340
Teacher spread0.151 · 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 designMeta-analysis
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
Published2022
Admission routes2
Has abstractyes

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