Feelings and perceptions of happiness and sadness induced by music: Similarities, differences, and mixed emotions.
Bibliographic record
Abstract
The authors examined similarities and differences between (1) listeners’ perceptions of emotions conveyed by 30-s pieces of music and (2) their emotional responses to the same pieces. Using identical scales, listeners rated how happy and how sad the music made them feel, and the happiness and the sadness expressed by the music. The music was manipulated to vary in tempo (fast or slow) and mode (major or minor). Feeling and perception ratings were highly correlated but perception ratings were higher than feeling ratings, particularly for music with consistent cues to happiness (fast-major) or sadness (slow-minor), and for sad-sounding music in general. Associations between the music manipulations and listeners’ feelings were mediated by their perceptions of the emotions conveyed by the music. Happiness ratings were elevated for fast-tempo and major-key stimuli, sadness ratings were elevated for slow-tempo and minor-key stimuli, and mixed emotional responses (higher happiness and sadness ratings) were elevated for music with mixed cues to happiness and sadness (fast-minor or slow-major). Listeners also exhibited ambivalence toward sad-sounding 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 | 0.003 |
| 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) | 0.001 | 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".