Foundational Tuning: How Infants' Attention to Speech Predicts Language Development
Bibliographic record
Abstract
Orienting biases for speech may provide a foundation for language development. Although human infants show a bias for listening to speech from birth, the relation of a speech bias to later language development has not been established. Here, we examine whether infants' attention to speech directly predicts expressive vocabulary. Infants listened to speech or non-speech in a preferential listening procedure. Results show that infants' attention to speech at 12 months significantly predicted expressive vocabulary at 18 months, while indices of general development did not. No predictive relationships were found for infants' attention to non-speech, or overall attention to sounds, suggesting that the relationship between speech and expressive vocabulary was not a function of infants' general attentiveness. Potentially ancient evolutionary perceptual capacities such as biases for conspecific vocalizations may provide a foundation for proficiency in formal systems such language, much like the approximate number sense may provide a foundation for formal mathematics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".