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Record W4408439976 · doi:10.1525/mp.2025.2321624

Metric Accent Affects Perception of Key Center in Pop-Music Chord Loops

2025· article· en· W4408439976 on OpenAlexaff
Nicholas Shea, Christopher W. White, Bryn Hughes, Dominique T. Vuvan

Bibliographic record

VenueMusic Perception An Interdisciplinary Journal · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsChord (peer-to-peer)Stress (linguistics)Center (category theory)Key (lock)Metric (unit)PerceptionSpeech recognitionComputer scienceMathematicsPsychologyLinguisticsEngineeringPhilosophyChemistry

Abstract

fetched live from OpenAlex

Looped chord progressions in popular music can often be heard as having multiple plausible key centers. In a series of two experiments, we investigate meter’s role as an orienting force within these progressions. Both experiments presented listeners with a progression that looped the C major, F major, A minor, and G major triads, a series that could plausibly be heard in multiple keys. While the ordering of the triads remained constant across trials, we modified which chord began the loop, thus altering the placement of the initiating metric accent. In Experiment 1, participants heard the loop followed by a probe chord and were asked to rate the probe’s stability, a proxy for identifying a key center. In Experiment 2, participants saw a notated loop and were asked to select the most stable chord. We found a significant effect of metric position and chord identity, with participants rating metric accents and the C major triad as the most stable/centric event. We use these findings to create an algorithmic key-finding model that incorporates both pitch and metric information. Our study argues that metric position has a strong influence on key perception in popular music, challenging an inherited Western art music bias toward purely pitch-based understandings of musical key.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.973
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
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.045
GPT teacher head0.349
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 teacher head, not a consensus.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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