Sad and happy emotion discrimination in music by children with cochlear implants
Bibliographic record
Abstract
Children using cochlear implants (CIs) develop speech perception but have difficulty perceiving complex acoustic signals. Mode and tempo are the two components used to recognize emotion in music. Based on CI limitations, we hypothesized children using CIs would have impaired perception of mode cues relative to their normal hearing peers and would rely more heavily on tempo cues to distinguish happy from sad music. Study participants were children with 13 right CIs and 3 left CIs (M = 12.7, SD = 2.6 years) and 16 normal hearing peers. Participants judged 96 brief piano excerpts from the classical genre as happy or sad in a forced-choice task. Music was randomly presented with alterations of transposed mode, tempo, or both. When music was presented in original form, children using CIs discriminated between happy and sad music with accuracy well above chance levels (87.5%) but significantly below those with normal hearing (98%). The CI group primarily used tempo cues, whereas normal hearing children relied more on mode cues. Transposing both mode and tempo cues in the same musical excerpt obliterated cues to emotion for both groups. Children using CIs showed significantly slower response times across all conditions. Children using CIs use tempo cues to discriminate happy versus sad music reflecting a very different hearing strategy than their normal hearing peers. Slower reaction times by children using CIs indicate that they found the task more difficult and support the possibility that they require different strategies to process emotion in music than normal.
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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 | 0.004 |
| 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 | 0.001 |
| 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".