Changing the Tune: Listeners Like Music that Expresses a Contrasting Emotion
Bibliographic record
Abstract
Theories of esthetic appreciation propose that (1) a stimulus is liked because it is expected or familiar, (2) a stimulus is liked most when it is neither too familiar nor too novel, or (3) a novel stimulus is liked because it elicits an intensified emotional response. We tested the third hypothesis by examining liking for music as a function of whether the emotion it expressed contrasted with the emotion expressed by music heard previously. Stimuli were 30-s happy- or sad-sounding excerpts from recordings of classical piano music. On each trial, listeners heard a different excerpt and made liking and emotion-intensity ratings. The emotional character of consecutive excerpts was repeated with varying frequencies, followed by an excerpt that expressed a contrasting emotion. As the number of presentations of the background emotion increased, liking and intensity ratings became lower compared to those for the contrasting emotion. Consequently, when the emotional character of the music was relatively novel, listeners' responses intensified and their appreciation increased.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".