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Record W4412314326

Information dynamics of boundary perception:Entropy in self-paced music listening

2016· article· en· W4412314326 on OpenAlexaff
Haley E. Kragness, Niels Chr. Hansen, Peter Vuust, Laurel J. Trainor, Marcus T. Pearce

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsActive listeningPerceptionDynamics (music)PsychologyCognitive psychologyEntropy (arrow of time)Computer scienceSpeech recognitionStatistical physicsCommunicationPhysicsThermodynamics
DOInot available

Abstract

fetched live from OpenAlex

It has long been noted that expert musicians lengthen notes at phrase boundaries in expressive performance. Recently, we have extended research on this phenomenon by showing that undergraduates with no formal musical training and children as young as 3 years lengthen phrase boundaries during self-paced listening to chord sequences in a lab setting (the musical dwell-time effect). However, the origin of the musical dwell-time effect is still unknown. Recent work has demonstrated that musicians and non-musicians are sensitive to entropy in musical sequences, experiencing high-entropy contexts as more uncertain than low-entropy contexts. Because phrase boundaries tend to afford high-entropy continuations, thus generating uncertain expectations in the listener, one possibility is that boundary perception is directly related to entropy. In other words, it may be hypothesized that entropy underlies the musical dwell-time effect rather than boundary status per se. The current experiment thus investigates the contributions of boundary status and predictive uncertainty to the musical dwell time effect by controlling these usually highly-correlated factors independently. In this procedure, participants selfpace through short melodies (derived from a corpus of Bach chorales) using a computer key to control the onset of each successive note with the explicit goal of optimizing their memorization of the music. Each melody contains a target note that (1) is phrase ending or beginning and (2) has high or low entropy (as estimated by the Information Dynamics of Music Model, IDyOM, trained on a large corpus of hymns and folksongs). Data collection is ongoing. The main analysis will examine whether longer dwelling is associated with boundary status or entropy. Results from this study will extend recent work on predictive uncertainty to the timing domain, as well as potentially answer key questions relating to boundary perception in musical listening.

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)0.017
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
Metaresearch0.0010.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.007
GPT teacher head0.209
Teacher spread0.203 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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