Musical emotion recognition in bimodal patients
Bibliographic record
Abstract
Several cues are used to convey musical emotion, the two primary being musical mode and musical tempo. Specifically, major and minor modes are regarded as having positive and negative valence, respectively, and songs at fast tempi (i.e., quarter note = 92-196, Gosselin et al. (2005); quarter note = 80-255, Peretz et al. (1998)) are associated with more positive valence compared to songs at slow tempi (i.e., quarter note = 40-60, Gosselin et al. (2005); quarter note = 20-100, Peretz et al. (1998)). Caldwell et al. (2015) demonstrated that CI users relied on tempo (fast vs. slow) with little regard for mode (major vs. minor) when interpreting musical emotion, a finding consistent with spectral cues being poorly represented in CI users. The current study is an extension of Caldwell et al. (2015) examining how mode and tempo cues impact musical emotion recognition in bimodal listeners. Our primary hypothesis is that, unlike CI only users, bimodal listeners—with access to F0 and fine structure—will utilize both mode and tempo cues in a manner similar to normal-hearing listeners. Our secondary hypothesis is that low-frequency spectral resolution in the contralateral ear (via tuning curves and spectral modulation detection) will be significantly correlated with degree of bimodal benefit for musical emotion perception.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".