Sing for me, Mama! Infants' discrimination of novel vowels in song
Bibliographic record
Abstract
When adults speak or sing with infants, they sound differently than in adult communication. Infant-directed (ID) communication helps caregivers to regulate infants' emotions and helps infants to process speech information, at least from ID-speech. However, it is largely unclear whether infants might also process speech information presented in ID-singing. Therefore, we examined whether infants discriminate vowels in ID-singing, as well as potential differences with ID-speech. Using an alternating trial preference procedure, infants aged 4-6 and 8-10 months were tested on their discrimination of an unfamiliar non-native vowel contrast presented in ID-like speech and singing. Relying on models of early speech sound perception, we expected that infants in their first half year of life would discriminate the vowels, in contrast to older infants whose non-native sound perception should deteriorate, at least in ID-like speech. Our results showed that infants of both age groups were able to discriminate the vowels in ID-like singing, while only the younger group discriminated the vowels in ID-like speech. These results show that infants process speech sound information in song from early on. They also hint at diverging perceptual or attentional mechanisms guiding infants' sound processing in ID-speech versus ID-singing toward the end of the first year of life.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".