Perceived and Induced Affective Responses to Musical Sounds
Bibliographic record
Abstract
Local features of instrument family and pitch register can influence the affect a music listener perceives and feels. We investigate the effect of those features on perceived and induced affect in response to single tones (Experiment 1) and chromatic scales (Experiment 2) and explore the moderating effect of individual differences. In two online experiments, participants (N1 = 263; N2 = 152) rated their affective response on dimensional (valence, tension, energy) and discrete (anger, fear, sadness, happiness, tenderness) affect scales, and completed questionnaires to measure their pre-existing mood, personality traits (Big-Five and empathy), musical sophistication, and musical expertise. Polynomial mixed-effects analyses showed a mostly quadratic effect of register, although energy and sadness responded more linearly to changes in register. The quadratic aspect of pitch effects may explain previous discrepancies in results that considered pitch linearly. Comparing the different instrument families, (pitched) percussion was most distinctive as the most positively valenced, happy, and tender, and least fearful, sad, and angry. Affects that may be considered unpleasant were less strongly induced than perceived, which may extend the “sad music paradox” to other unpleasant affects. Musical sophistication most frequently moderated the effects of the polynomial mixed-effects models, especially the effect of instrument family. The influence of individual differences calls for future studies to recruit a representative population sample, report on the variation that is present in their sample, and/or consider the moderating effect of individual differences in their subject of interest.
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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 | 0.006 |
| 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".