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Record W4224289536 · doi:10.1525/mp.2022.39.4.361

Measuring Children’s Harmonic Knowledge with Implicit and Explicit Tests

2022· article· en· W4224289536 on OpenAlexaffabout
Kathleen A. Corrigall, Barbara Tillmann, E. Glenn Schellenberg

Bibliographic record

VenueMusic Perception An Interdisciplinary Journal · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMacEwan UniversityUniversity of Toronto
FundersAgence Nationale de la Recherche
KeywordsChord (peer-to-peer)PsychologyTimbreTonic (physiology)SubdominantCognitionPianoImplicit knowledgeCognitive psychologyDevelopmental psychologyMusicalSocial psychologyComputer scienceAcousticsCognitive science

Abstract

fetched live from OpenAlex

We used implicit and explicit tasks to measure knowledge of Western harmony in musically trained and untrained Canadian children. Younger children were 6–7 years of age; older children were 10–11. On each trial, participants heard a sequence of five piano chords. The first four chords established a major-key context. The final chord was the standard, expected tonic of the context or one of two deviant endings: the highly unexpected flat supertonic or the moderately unexpected subdominant. In the implicit task, children identified the timbre of the final chord (guitar or piano) as quickly as possible. Response times were faster for the tonic ending than for either deviant ending, but the magnitude of the priming effect was similar for the two deviants, and the effect did not vary as a function of age or music training. In the explicit task, children rated how good each chord sequence sounded. Ratings were highest for sequences with the tonic ending, intermediate for the subdominant, and lowest for the flat supertonic. Moreover, the difference between the tonic and deviant sequences was larger for older children with music training. Thus, the explicit task provided a more nuanced picture of musical knowledge than did the implicit task.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.070
GPT teacher head0.318
Teacher spread0.247 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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