Exploring Cognitivist and Emotivist Positions of Musical Emotion Using Neural Network Models
Bibliographic record
Abstract
<p>There are two positions in the classic debate regarding musical emotion: the cognitivist position and the emotivist position. According to the cognitivist position, music expresses emotion but does not induce it in listeners. So, listeners may recognize emotion in music without feeling it, unlike real, everyday emotion. According to the emotivist position, listeners not only recognize emotion but also feel it. This is supported by their physiological responses during music listening, which are similar to responses occurring with real emotion. When listeners provide emotion appraisals, if the cognitivist position were true, then these appraisals might be based on audio features in the music. However, if the emotivist position were true, then appraisals would be based on the emotion experienced by listeners as opposed to what they perceived in the audio features. We propose a hypothesis combining both positions according to which, listeners make emotion appraisals based on a combination of what they perceive in the music as well as what they experience during the listening process. In this paper, we explore all three positions using connectionist prediction models, specifically four different neural networks: (a) using only audio features as input, (b) using only physiological features as input, (c) using both audio and physiological features as input, and (d) using a committee machine that combines contributions from an audio network and a physiology network. We examine the performance of these networks and discuss their implications as possible cognitive models of emotion appraisal within listeners.</p>
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".