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Record W1957390242 · doi:10.1177/0305735615593409

Perception of nonadjacent tonic-key relationships

2015· article· en· W1957390242 on OpenAlexaff
Matthew Woolhouse, Ian Cross, Timothy James Horton

Bibliographic record

VenuePsychology of Music · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcMaster University
FundersArts and Humanities Research Council
KeywordsChord (peer-to-peer)PsychologyCadencePerceptionTonic (physiology)Key (lock)Cognitive psychologyCommunicationStimulus (psychology)Speech recognitionComputer scienceNeuroscienceAcoustics

Abstract

fetched live from OpenAlex

The issue of structural nonadjacency in music and language was explored from a musical perspective in an experiment employing a stimulus-matching paradigm. The experiment measured the perceptual effect of a temporally nonadjacent key on the closure of a musical phrase; participants rated a stimulus-ending two-chord probe cadence for its closural properties. The temporal rate of decay of the nonadjacent key in memory was observed by varying the length of the intervening key area; that is, the key temporally adjacent to the probe cadence. Evidence emerged that listeners were able to hold the nonadjacent key in memory for over 10 seconds, indicating “global” nonadjacent harmonic perceptions. The study provides qualified evidence to support the notion that there are syntactic parallelisms between language and music, particularly in respect of nonadjacent key relationships.

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 score0.002
Threshold uncertainty score0.007

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.224
GPT teacher head0.365
Teacher spread0.141 · 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

Citations18
Published2015
Admission routes1
Has abstractyes

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