Musical creativity and the embodied mind
Bibliographic record
Abstract
The phenomenon of creativity has received a growing amount of attention from scholars working across a range of disciplines. While this research has produced many important insights, it has also traditionally tended to explore creativity in terms of the reception of products or outcomes, conceiving of it as a cognitive process that is limited to the individual domain of the creative agent. More recently, however, researchers have begun to develop perspectives on creativity that highlight the patterns of adaptive embodied interaction that occur between multiple agents, as well as the broader socio-material milieu they are situated in. This has promoted new understandings of creativity, which is now often considered as a distributed phenomenon. Because music involves such a wide range of socio-cultural, bodily, technological, and temporal dimensions it is increasingly taken as a paradigmatic example for researchers who wish to explore creativity from this more relational perspective. In this article, we aim to contribute to this project by discussing musical creativity in light of recent developments in embodied cognitive science. More specifically, we will attempt to frame an approach to musical creativity based in an 4E (embodied, embedded, enactive, and extended) understanding of cognition. We suggest that this approach may help us better understand creativity in terms of how interacting individuals and social groups bring forth worlds of meaning through shared, embodied processes of dynamic interactivity. We also explore how dynamical systems theory (DST) may offer useful tools for research and theory that align closely with the 4E perspective. To conclude, we summarize our discussion and suggest possibilities for future research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".