Bibliographic record
Abstract
Abstract Perception and Cognition of Music: The Sorbonne Lectures presents revised and updated materials delivered in four distinguished lectures at the Université Paris-Sorbonne in 2009 and the Université de Montréal in 2010, originally published in French. It aims to bridge the fields of music psychology, music theory, and music analysis by considering several aspects of music listening through the lens of cognitive psychology. Auditory grouping processes play a role in organizing the continuous incoming sensory information into events, streams of events, and segments of streams that form musical units. Perceived properties of events and streams depend on how the incoming information is organized. Special attention is given to timbre as an understudied musical parameter, which can be a strong structuring force and form-bearing element in music through orchestration practice. The development of systems of abstract knowledge built on different musical parameters within a given culture focuses on the cognitive processing of pitch systems and structures and their role in the mental representation of hierarchical event structures in listeners’ minds. Finally, given that music is a temporal art par excellence, the temporality of music listening is explored through a collaborative project involving a composer, psychologists, and musicologists around the conception and creation of a musical work and the perception and affective response it engenders in a live-concert experiment. Each chapter concludes with elements for reflection to expand the necessary transdisciplinary approach that music scholarship needs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".