Identifying emotions in music through electrical hearing in deaf children using cochlear implants
Bibliographic record
Abstract
OBJECTIVE: Cochlear implant (CI) devices are the choice of treatment for individuals with severe to profound hearing loss. The CI devices provide the opportunity for children who are deaf to perceive sound by electrical stimulation of the auditory nerve, with the goal of optimizing oral communication. A natural benefit of acquiring hearing using CIs is the ability to hear, and perhaps enjoy, music. Music is a non-verbal auditory stimulus and a powerful tool for transmitting emotion. Identifying emotional cues is an important part of normal social development and communication and thus music may play an important role in establishing these skills during development. To date, it is not known whether children who use cochlear implants to hear can identify the emotional content carried in music. Our objective in the present study was to determine whether children who have been deaf from infancy and are experienced CI users have acquired the ability to identify emotion in musical phrases. METHOD: Study participants were 18 CI users (ages 7-13 years) who received right unilateral CIs (mean age at CI activation of 2.9 years) and 18 age-and gender-matched controls. Participants were asked to judge 32 brief musical excerpts as happy or sad by pointing to simple graphics of a smiling or frowning face. RESULTS: Children using CIs were able to correctly distinguish happy versus sad music well above chance levels, but performed more poorly on this task than their peers with typical hearing. Age at CI activation and time since CI activation were both uncorrelated with outcome measures. CONCLUSION: Children with CIs show the ability to perceive emotion in music but do so less accurately than typically hearing peers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".