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Record W2101912971 · doi:10.1162/089892902317361949

Automatic and Controlled Processing of Melodic Contour and Interval Information Measured by Electrical Brain Activity

2002· article· en· W2101912971 on OpenAlexafffund
Laurel J. Trainor, Kelly L. McDonald, Claude Alain

Bibliographic record

VenueJournal of Cognitive Neuroscience · 2002
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversity of TorontoMcMaster UniversityBaycrest Hospital
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsMelodyAbsolute pitchPitch contourInterval (graph theory)Relative pitchPsychologyMismatch negativitySemitoneSet (abstract data type)Speech recognitionPitch (Music)Auditory cortexCommunicationComputer scienceElectroencephalographyNeuroscienceMathematicsMusicalPerception

Abstract

fetched live from OpenAlex

Most work on how pitch is encoded in the auditory cortex has focused on tonotopic (absolute) pitch maps. However, melodic information is thought to be encoded in the brain in two different "relative pitch" forms, a domain-general contour code (up/down pattern of pitch changes) and a music-specific interval code (exact pitch distances between notes). Event-related potentials were analyzed in nonmusicians from both passive and active oddball tasks where either the contour or the interval of melody-final notes was occasionally altered. The occasional deviant notes generated a right frontal positivity peaking around 350 msec and a central parietal P3b peaking around 580 msec that were present only when participants focused their attention on the auditory stimuli. Both types of melodic information were encoded automatically in the absence of absolute pitch cues, as indexed by a mismatch negativity wave recorded during the passive conditions. The results indicate that even in the absence of musical training, the brain is set up to automatically encode music-specific melodic information, even when absolute pitch information is not available.

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)0.001
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.285
Teacher spread0.249 · 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 designBench or experimental
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

Citations150
Published2002
Admission routes2
Has abstractyes

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