Perception of emotional music by children with cochlear implants reveals developmental plasticity
Bibliographic record
Abstract
Cochlear implants provide poor access to the frequency components needed to accurately identify emotional music and speech. Nonetheless, a series of studies revealed that children with cochlear implants develop unique strategies to identify emotion in music. Although musical changes were difficult for them to detect, children with cochlear implants responded most accurately to rhythmic changes and also relied mainly on temporal information to judge whether music was happy or sad. By contrast, mode (frequency) information dominated perception of musical emotion in typically developing children and was used more clearly by bimodal device users (cochlear implant in one ear with acoustic hearing in the other) than children with bilateral deafness using unilateral or bilateral cochlear implants. Mode became the more dominant cue with increasing residual hearing in the non-implanted ear. Children with unilateral cochlear implants showed prolonged response times relative to a control group of normal hearing peers while judging whether music was happy or sad. Response times in children with bimodal devices and bilateral cochlear implants were more similar to controls. Together, these results demonstrate development of novel strategies for music listening and perception of emotion in music based on available cues in children with cochlear implants and suggest that these strategies require cognitive resource.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".