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Record W3087173707 · doi:10.1525/mp.2020.38.1.27

The Influence of Rate and Accentuation on Subjective Rhythmization

2020· article· en· W3087173707 on OpenAlexaff
Ève Poudrier

Bibliographic record

VenueMusic Perception An Interdisciplinary Journal · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsStress (linguistics)PsychologyTone (literature)Variation (astronomy)RhythmDuration (music)Speech recognitionCommunicationMathematicsCombinatoricsPattern recognition (psychology)StatisticsLinguisticsCognitive psychologyComputer sciencePhysics

Abstract

fetched live from OpenAlex

The parsing of undifferentiated tone sequences into groups of qualitatively distinct elements is one of the earliest rhythmic phenomena to have been investigated experimentally (Bolton, 1894). The present study aimed to replicate and extend these findings through online experimentation using a spontaneous grouping paradigm with forced-choice response (from 1 to 12 tones per group). Two types of isochronous sequences were used: equitone sequences, which varied only with respect to signal rate (200, 550, or 950 ms interonset intervals), and accented sequences, in which accents were added every two or three tones to test the effect of induced grouping (duple vs. triple) and accent type (intensity, duration, or pitch). In equitone sequences, participants’ grouping percepts (N = 4,194) were asymmetrical and tempo-dependent, with “no grouping” and groups of four being most frequently reported. In accented sequences, slower rate, induced triple grouping, and intensity accents correlated with increases in group length. Furthermore, the probability of observing a mixed metric type—that is, grouping percepts divisible by both two and three (6 and 12)—was found to be highest in faster sequences with induced triple grouping. These findings suggest that lower-level triple grouping gives rise to binary grouping percepts at higher metrical levels.

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 categoriesnone
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 score0.962
Threshold uncertainty score0.902

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.326
Teacher spread0.276 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations13
Published2020
Admission routes1
Has abstractyes

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