Misery loves company: Mood-congruent emotional responding to music.
Why this work is in the frame
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Bibliographic record
Abstract
We examined emotional responding to music after mood induction. On each trial, listeners heard a 30-s music excerpt and rated how much they liked it, whether it sounded happy or sad, and how familiar it was. When the excerpts sounded unambiguously happy or sad (Experiment 1), the typical preference for happy-sounding music was eliminated after inducing a sad mood. When the excerpts sounded ambiguous with respect to happiness and sadness (Experiment 2), listeners perceived more sadness after inducing a sad mood. Sad moods had no influence on familiarity ratings (Experiments 1 and 2). These findings imply that "misery loves company." Listeners in a sad mood fail to show the typical preference for happy-sounding music, and they perceive more sadness in music that is ambiguous with respect to mood.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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