A Comparison of Dimensional and Discrete Models for the Representation of Perceived and Induced Affect in Response to Short Musical Sounds
Bibliographic record
Abstract
In musical affect research, there is considerable discussion on the best method to represent affective response. This discussion mainly revolves around the dimensional (valence, tension arousal, energy arousal) and discrete (anger, fear, sadness, happiness, tenderness) models of affect. Here, we compared these models’ ability to capture self-reported affect in response to short, affectively ambiguous sounds. In two online experiments (n1 = 263, n2 = 152), participants rated perceived and induced affect in response to single notes (Exp 1) and chromatic scales (Exp 2), which varied across instrument family and pitch register. Additionally, participants completed questionnaires measuring pre-existing mood, trait empathy, Big-Five personality, musical sophistication, and musical preferences. Rater consistency and agreement were high across all affect scales. Correlation and principal component analyses showed that two dimensions or two affect categories captured most of the variation in affective response. Canonical correlation and regression analyses also showed that energy arousal varied in a manner that was not captured by discrete affect ratings. Furthermore, all sources of individual differences were moderately correlated with all affect scales, particularly pre-existing mood and dimensional affect. We conclude that when it comes to single notes and chromatic scales, the dimensions of valence and energy arousal best capture the perceived and induced affective response to affectively ambiguous sounds, although the role of individual differences should also be considered.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".