Nonmusicians Express Emotions in Musical Productions Using Conventional Cues
Bibliographic record
Abstract
Expert musicians use a number of expressive cues to communicate specific emotions in musical performance. In turn, listeners readily identify the intended emotions. Previous studies of cue utilization have studied the performances of expert or highly trained musicians, limiting the generalizability of the results. Here, we use a musical self-pacing paradigm to investigate expressive cue use by non-expert individuals with varying levels of formal music training. Participants controlled the onset and offset of each chord in a musical sequence by repeatedly pressing and lifting a single key on a MIDI piano, controlling tempo and articulation. In addition, the velocity with which they pressed the key controlled the sound level ( dynamics). Participants were asked to “perform” the music to express basic emotions that were (1) positively or negatively valenced and (2) high- or low-arousal ( joy, sadness, peacefulness, and anger). Nonmusicians’ expressive cue use was consistent with patterns of cue use by professional musicians described in the literature. In a secondary analysis, we explored whether formal training affected how tempo, articulation, dynamics, rhythm, and phrasing were employed to express the target emotions. We observed that the patterns of cue use were strikingly consistent across groups with differing levels of formal musical training. Future work could investigate whether expertise is implicated in the expression of more complex emotions and/or in the expression of more complex musical structures, as well as explore the role of emotional intelligence and informal musical experiences in expressive performance.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".