Bibliographic record
Abstract
Musical improvisation is a sophisticated cognitive process that shares many commonalities with language in stimulus features, social behavior, and neural demands . With a renewed interest in quantifying creative processes facilitated by recent advances in neuroimaging technology, musical improvisation has emerged as an ideal paradigm to study creativity. However, many studies isolate the top-down processes related to creativity from those related to production and auditory perception, leaving the question of how creative behaviours integrate sensory information with higher cognitive processes unanswered. The bottom-up neural correlates of music perception have been extensively quantified, often in tandem with language, comprising a common auditory processing network and analogous networks for parsing semantic and syntactic content from music and speech signals. In studies of spontaneously generated music and language, executive control and goal-directed movement networks are added to the perceptual foundation. This review summarizes previous work on music and speech perception and production, and presents a conceptual model of musical improvisation with known neural correlates. We make recommendations on future directions for the study of social interaction with music and language, and discuss the challenges posed by such endeavours.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".