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Record W4232822974 · doi:10.32920/ryerson.14637927.v1

Sensitivity to tonality across the pitch range

2021· preprint· en· W4232822974 on OpenAlexafffund
Frank Russo, Lola L. Cuddy, Alexander Galembo, William Forde Thompson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsQueen's UniversityToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTonalitySensitivity (control systems)Salience (neuroscience)Context (archaeology)Tone (literature)Relative pitchOctave (electronics)AcousticsAudiologyMathematicsSpeech recognitionPsychologyComputer sciencePhysicsCognitive psychologyArtEngineeringGeographyMedicineNeuroscience

Abstract

fetched live from OpenAlex

Striking changes in sensitivity to tonality across the pitch range are reported. Participantswere presented a key-defining context (do-mi-do-sol) followed by one of the 12 chromatic tonesof the octave, and rated the goodness of fit of the probe tone to the context. The set of ratings,called the probe-tone profile, was compared to an established standardised profile for the Westerntonal hierarchy. The presentation of context and probe tones at low and high pitch registersresulted in significantly reduced sensitivity to tonality. Sensitivity was especially poor for presen-tations in the lowest octaves where inharmonicity levels were substantially above the thresholdfor detection. We propose that sensitivity to tonality may be influenced by pitch salience (or aco-varying factor such as exposure to pitch distributional information) as well as suprathresholdinharmonicity.

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)0.000
metaresearch head score (Gemma)0.005
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.093
GPT teacher head0.361
Teacher spread0.268 · 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

Citations4
Published2021
Admission routes2
Has abstractyes

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