Feelings and perceptions of happiness and sadness induced by music: Similarities, differences, and mixed emotions.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it