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Record W4387531263 · doi:10.1177/10298649231203641

Tracking auditory attention in group performances: A case study on Éliane Radigue’s <i>Occam Delta XV</i>

2023· article· en· W4387531263 on OpenAlexaff
Emanuelle Majeau-Bettez, Aliénor Golvet, Clément Canonne

Bibliographic record

VenueMusicae Scientiae · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcGill University
FundersAgence Nationale de la Recherche
KeywordsActive listeningPsychologyMusicalCognitive psychologyNarrativeoccamVisual artsCommunicationComputer scienceArt

Abstract

fetched live from OpenAlex

While the empirical study of the processes and mechanisms underlying joint musical performance has gained a lot of traction in the past few decades, surprisingly little attention has been paid to the study of musicians’ listening strategies in what remains a mainly audio-centric medium. Yet, understanding how musicians listen to each other is particularly crucial for more open-ended, improvised, or indeterminate musical practices, as it plays a crucial role in shaping how the performance will unfold. In this article, we report on an exploratory study that was designed to investigate the dynamics of musicians’ auditory attention in such settings, using Quatuor Bozzini’s performance of Éliane Radigue’s Occam Delta XV (2018) as a case study. Using a post hoc annotation procedure, we found striking differences between musicians’ overall auditory attention, with each musician’s listening orientation relating differently to the general narrative of Occam Delta XV. We also found that joint listening between musicians was more likely to emerge when coordination was more challenging, suggesting that attentional focus was used strategically by the performers as a way of enhancing coordination within the group. Taken together, our findings shed important light on musicians’ listening and interactional strategies in collective music-making.

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 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 score0.953
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.088
GPT teacher head0.321
Teacher spread0.233 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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