Predicting emotion perception abilities for cochlear implant users
Bibliographic record
Abstract
OBJECTIVE: In daily life, failure to perceive emotional expressions can result in maladjusted behaviour. For cochlear implant users, perceiving emotional cues in sounds remains challenging, and the factors explaining the variability in patients' sensitivity to emotions are currently poorly understood. Understanding how these factors relate to auditory proficiency is a major challenge of cochlear implant research and is critical in addressing patients' limitations. DESIGN: To fill this gap, we evaluated different auditory perception aspects in implant users (pitch discrimination, music processing and speech intelligibility) and correlated them to their performance in an emotion recognition task. STUDY SAMPLE: Eighty-four adults (18-76 years old) participated in our investigation; 42 cochlear implant users and 42 controls. Cochlear implant users performed worse than their controls on all tasks, and emotion perception abilities were correlated to their age and their clinical outcome as measured in the speech intelligibility task. RESULTS: As previously observed, emotion perception abilities declined with age (here by about 2-3% in a decade). Interestingly, even when emotional stimuli were musical, CI users' skills relied more on processes underlying speech intelligibility. CONCLUSIONS: These results suggest that speech processing remains a clinical priority even when one is interested in affective skills.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".