Bibliographic record
Abstract
Melody has been defined as a distinct perceptual unit that exhibits stability and coherence to listeners and performers. These psychological processes (distinctiveness, stability, coherence) contribute to the foundations of three theories of music cognition (Bregman, 1990; Krumhansl, 1990; Narmour, 1990), yet several mysteries still exist in the human experience of melody. From early exposure to lullabies and brief exposures in advertising jingles, to the full-length concert exposure of complex musical works, listeners’ imagination and focus are captured in unique ways by the experience of melody. People with various amounts of musical training hum, tap, clap, and find other ways of interacting with a melody; they perform to it. Listeners report the experience of a recurring melody playing in their minds (earworms). I discuss neuroscience findings that aid in modeling the fine-level time course of melodic experiences, and address how the listener/performer identifies a melody as distinct in a complex auditory scene, how expectations unfold in implications and realizations that contribute to coherence, and how hierarchical tonal relationships of stability are detected. The life cycle of a melody in the ears, brain, and heart of a listener/performer sheds light on the human experience of music.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".