Tracking auditory attention in group performances: A case study on Éliane Radigue’s <i>Occam Delta XV</i>
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".