How Do Aesthetics Get into Muscles and Muscles into Aesthetics? Insights from Musical Interactions in an Experimental Context
Bibliographic record
Abstract
In certain contexts, processes of perception, action and skill development and those related to aesthetic experiences can have mutual influence upon each other. Concepts from the domains of ecological psychology and aesthetics may therefore be distinct but entangled. These entanglements are explored here through observations from an experiment in which musicians’ behaviours and experiences were recorded while they interacted with a computer music controller instrument operating different modes of sound synthesis. Processes of action-perception exploration and enacting the instrument’s various affordances had an impact upon the musicians’ aesthetic judgements about the instrument, and their imagining its virtual potential for application in music cultural practices. Conversely, musicians’ prior experience in different aesthetic cultures constrained the affordances of the instrument that were discovered and taken up by them. These insights are used to expand upon the different ways that perceptual-motor and social aesthetic processes can constrain and shape each other. Ongoing and further directions for both theoretical and empirical research are highlighted.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".