MétaCan
Menu
Back to cohort
Record W2122554990 · doi:10.1177/0305735613483847

Perception of a tonal hierarchy derived from Korean music

2013· article· en· W2122554990 on OpenAlexaff
Michael E. Lantz, Jung-Kyong Kim, Lola L. Cuddy

Bibliographic record

VenuePsychology of Music · 2013
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsQueen's UniversityMcGill University
Fundersnot available
KeywordsDuration (music)Tone (literature)PsychologyPitch (Music)PerceptionSalience (neuroscience)Sequence (biology)HierarchyScale (ratio)Speech recognitionCommunicationLinguisticsCognitive psychologyAcousticsGeographyComputer scienceCartography

Abstract

fetched live from OpenAlex

In two experiments, we assessed recovery of a tonal hierarchy in tone sequences. In Experiment 1, sequence tones were five tones of a Korean pentatonic scale plus seven nonscale tones located between scale tones. Sequences included all 12 tones, randomly ordered. Duration of scale tones in each sequence corresponded to the total duration of each tone in a piece of Korean music, as quantified by U. Nam (1998). Nonscale tones were shorter than scale tones. Listeners were either familiar or unfamiliar with the style of Korean music. Sequences were played 12 times, each time followed by 1 of 12 probe tones that had occurred in the sequence. Participants rated goodness-of-fit of the probe tone to the sequence. Ratings by both groups reflected the Korean tonal hierarchy including the relative salience of scale tones. Experiment 2 followed the same method and tones, but duration was assigned to tones quasi-randomly so that duration did not emphasize intervallic relationships in the Korean scale. Ratings differentiated long and short tones, but showed no other clear organization among long tones. Differences in results between experiments suggest that duration helps listeners organize pitch structure only when duration emphasizes intervallic relationships such as the near-perfect fifth.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.858
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.068
GPT teacher head0.314
Teacher spread0.246 · 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

Citations14
Published2013
Admission routes1
Has abstractyes

Explore more

Same venuePsychology of MusicSame topicNeuroscience and Music PerceptionFrench-language works237,207