Perception of extreme musical dissonance in cochlear implant users using a novel listening task
Bibliographic record
Abstract
Hypothesis We hypothesized that a novel harmonic consonance-dissonance music perception task would reveal a monotonic relationship between harmonic consonance and pleasantness ratings by NH listeners. Additionally, we hypothesized that CI recipients will be able to distinguish between the most consonant and most dissonant music samples, although with more variability and less contrast between each condition than the NH cohort and with lower overall ratings of sound quality. Finally, we hypothesized that listeners with extensive music training would show more pronounced differences in pleasantness ratings across the four tiers of consonance to dissonance. Background Harmonic consonance and dissonance are key components of music's perceived quality and pleasantness. However, tools to evaluate these musical aspects, especially for CI users, are scarce, leading to significant knowledge gaps. This study aimed to refine previous methods by emphasizing the variability and typically lower scores among CI users, aligning these findings more closely with their reported experiences and existing literature. Methods A total of 34 participants (21 NH and 13 CI) completed the 30-min music task, which involved listening to music samples with various levels of harmonic consonance-dissonance ranging from complete consonance to extreme dissonance, and then rating the samples on a 5-point “pleasantness” scale. Participants also provided details about their musical training and listening habits. Results NH listeners consistently rated Tier D (extreme dissonance) as the least pleasant and confirmed the expected monotonic relationship between consonance and pleasantness. CI recipients, while unable to distinguish between adjacent tiers (A and B, B and C, C and D), did show a significant difference in ratings between Tiers A and D, and between B and D. Their ratings for Tiers A–C were centered around “slightly pleasant,” reflecting lower overall pleasantness scores compared to NH participants. Musical training was correlated with greater differentiation in pleasantness ratings in both NH and CI groups, suggesting that formal training enhances sensitivity to harmonic dissonance. Conclusions The findings suggest that CI users perceive extreme manipulations of dissonance, and propose the potential for a shorter, refined version of this test for clinical use or further research. This task could aid in optimizing CI configurations for enhanced music enjoyment.
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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 | 0.003 |
| 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) | 0.002 | 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".