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Record W3177724977 · doi:10.1525/mp.2021.39.1.21

The Perceptual Attraction of Pre-Dominant Chords

2021· article· en· W3177724977 on OpenAlexaff
Jenine Brown, Daphne Tan, David John Baker

Bibliographic record

VenueMusic Perception An Interdisciplinary Journal · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChord (peer-to-peer)SemitonePsychologyAttractionPerceptionChromatic scaleMathematicsTonic (physiology)Cognitive psychologySocial psychologyCommunicationSpeech recognitionCombinatoricsLinguisticsComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

Among the three primary tonal functions described in modern theory textbooks, the pre-dominant has the highest number of representative chords. We posit that one unifying feature of the pre-dominant function is its attraction to V, and the experiment reported here investigates factors that may contribute to this perception. Participants were junior/senior music majors, freshman music majors, and people from the general population recruited on Prolific.co. In each trial, four Shepard-tone sounds in the key of C were presented: 1) the tonic note, 2) one of 31 different chords, 3) the dominant triad, and 4) the tonic note. Participants rated the strength of attraction between the second and third chords. Across all individuals, diatonic and chromatic pre-dominant chords were rated significantly higher than non-pre-dominant chords and bridge chords. Further, music theory training moderated this relationship, with individuals with more theory training rating pre-dominant chords as being more attracted to the dominant. A final data analysis modeled the role of empirical features of the chords preceding the V chord, finding that chords with roots moving to V down by fifth, chords with less acoustical roughness, and chords with more semitones adjacent to V were all significant predictors of attraction ratings.

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 categoriesScience 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 score0.871
Threshold uncertainty score0.999

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 studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.056
GPT teacher head0.354
Teacher spread0.298 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

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