Pre-babbling infants prefer listening to infant speech: Implications for vocal learning in humans
Bibliographic record
Abstract
For human infants to engage in vocal learning, they must effectively monitor and assess their own self-produced speech, which entails perceiving speech produced by an infant. Yet, little is known about how infants respond to infant-produced speech. Here, we demonstrate that pre-babbling infants prefer listening to infant speech. Across four experiments, 3-to-6-month-olds were tested in a preferential listening procedure, using vowels synthesized to emulate productions by female adults and infants. In experiment 1, infants listened longer to vowels produced by infant than adult speakers. However, in experiment 2, infants failed to show any listening preference for infant versus adult vowels synthesized with matching, infant-appropriate pitch values, suggesting that infants were either attracted to higher voice pitch per se or to infant-like voice pitch. Failing to support a bias of the first type, infants in experiment 3 showed no listening preference when presented infant vowels with higher and lower infant-appropriate pitch values. Moreover, in experiment 4, infants showed a preference for infant versus adult vowels when synthesized with pitch values that are appropriate for a female using adult-directed speech; this suggests that infants are also attracted to infant vocal resonance properties. The implications of these results for speech development are discussed.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".