Children with bilateral cochlear implants identify emotion in speech and music
Bibliographic record
Abstract
OBJECTIVES: This study examined the ability of prelingually deaf children with bilateral implants to identify emotion (i.e. happiness or sadness) in speech and music. METHODS: Participants in Experiment 1 were 14 prelingually deaf children from 5-7 years of age who had bilateral implants and 18 normally hearing children from 4-6 years of age. They judged whether linguistically neutral utterances produced by a man and woman sounded happy or sad. Participants in Experiment 2 were 14 bilateral implant users from 4-6 years of age and the same normally hearing children as in Experiment 1. They judged whether synthesized piano excerpts sounded happy or sad. RESULTS: Child implant users' accuracy of identifying happiness and sadness in speech was well above chance levels but significantly below the accuracy achieved by children with normal hearing. Similarly, their accuracy of identifying happiness and sadness in music was well above chance levels but significantly below that of children with normal hearing, who performed at ceiling. For the 12 implant users who participated in both experiments, performance on the speech task correlated significantly with performance on the music task and implant experience was correlated with performance on both tasks. DISCUSSION: Child implant users' accurate identification of emotion in speech exceeded performance in previous studies, which may be attributable to fewer response alternatives and the use of child-directed speech. Moreover, child implant users' successful identification of emotion in music indicates that the relevant cues are accessible at a relatively young age.
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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) | 0.001 | 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 | 0.000 |
| 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".