Beyond the notes: Clarifying the role of expressivity in conveying musical emotion
Bibliographic record
Abstract
Understanding how performance expression affects perceived emotion requires separating the effects of notated music from its interpretation by performers. Previous studies suggest that compositional cues (e.g., the pitches of a melody) primarily convey valence (negative–positive emotional quality), whereas performance cues (e.g., performance timing, intensity) convey arousal (low–high emotional intensity). However, these conclusions largely follow from simple single-line stimuli that lack the complexity of real-world music. To explore compositional and performance contributions to emotion in more complex works, we conducted experiments comparing participants’ ( N = 120) valence and arousal ratings of 48 recorded excerpts from a Grammy-winning pianist against parallel deadpan versions lacking emotionally expressive aspects. By comparing differences in ratings of stimuli presented in expressive and deadpan conditions, we corroborate past findings highlighting performance contributions to perceived emotion, while also providing novel insight into the relative importance of analyzed cues. Our findings reveal that removing expressive aspects (i.e., the deadpan condition) significantly affects arousal ratings of 21 excerpts, but valence ratings of only 4. Additionally, we highlight how cues differ in importance between expressive and deadpan conditions through a novel analytical approach employing elastic nets. Our analyses shed new light on how performance expression affects emotions communicated across complex musical works with different levels of compositional cues.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".