Pitch height and mode have asymmetrical effects on the perception of mixed emotions in seventh chords
Bibliographic record
Abstract
Music has long been recognized for evoking emotion from the listener. While prior empirical studies have investigated the effect of various auditory features (i.e., timbre, tempo, articulation) on emotion perception, the relationship between mode and emotion in chords, beyond basic associations of “major”-”happy” and “minor”-”sad” in triads, remains poorly understood. The present study investigates how mode contributes to the perception of mixed emotions in major and minor seventh chords, containing a triad from both modes. In Experiment 1, participants were asked to identify the emotion they perceived (happy, sad, or bittersweet) in response to a selection of major and minor triads and seventh chords. To observe the effect of changing mode salience, participants were presented with the same seventh chords whose root or seventh was lowered in volume. Experiment 2 additionally asked participants to respond to seventh chords with roots or sevenths that were quieted in multiple increments. Overall, participants were more likely to report a seventh chord as bittersweet (Experiments 1, 2) or happy (Experiment 2) but not sad. The likelihood of a seventh chord being rated as ‘happy’ increased with highlighting the major triad present in a minor seventh chord through quieting the chordal root, but the likelihood of a seventh chord being rated as ‘sad’ increased with lowering the volume of a chordal seventh, regardless of mode. The effect of pitch height on emotional perception is considered, and implications for the general understanding of complex emotional categories are discussed.
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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 | 0.005 |
| 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".