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Record W2145875708 · doi:10.5539/ijps.v4n1p3

Dynamic Cues in Key Perception

2012· article· en· W2145875708 on OpenAlexvenueno aff
Rie Matsunaga, Jun-ichi Abe

Bibliographic record

VenueInternational Journal of Psychological Studies · 2012
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsMelodyPsychologyPerceptionKey (lock)Active listeningSet (abstract data type)CommunicationStimulus (psychology)Cognitive psychologyTonic (physiology)Speech recognitionComputer scienceMusical

Abstract

fetched live from OpenAlex

The traditional idea of pitch full-set alone cannot explain different key perceptions for melodies that consist of the same pitch full-set but differ in pitch sequence. Three experiments, in which presentation styles, participant groups, and stimulus sets were manipulated independently, traced the process of key development back from a final stage of key identification to earlier stages of listening to a melody. In all the experiments, two results were confirmed: First, key responses in earlier stages influenced those in later stages to the extent that subsequent tones correspond to scale tones of a previously interpreted key, revealing a phenomenon termed perceptual inertia. Second, when multiple keys were possible, listeners tended to perceive the diatonic key that can contain more pitch classes within a pitch set given at the point of time as stable scale tones of that key (i.e., tonic triad).

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)0.001
metaresearch head score (Gemma)0.008
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 score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.003
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 head0.156
GPT teacher head0.459
Teacher spread0.304 · 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

Citations6
Published2012
Admission routes1
Has abstractyes

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