Native language governs interpretation of salient speech sound differences at 18 months
Bibliographic record
Abstract
One of the first steps infants take in learning their native language is to discover its set of speech-sound categories. This early development is shown when infants begin to lose the ability to differentiate some of the speech sounds their language does not use, while retaining or improving discrimination of language-relevant sounds. However, this aspect of early phonological tuning is not sufficient for language learning. Children must also discover which of the phonetic cues that are used in their language serve to signal lexical distinctions. Phonetic variation that is readily discriminable to all children may indicate two different words in one language but only one word in another. Here, we provide evidence that the language background of 1.5-year-olds affects their interpretation of phonetic variation in word learning, and we show that young children interpret salient phonetic variation in language-specific ways. Three experiments with a total of 104 children compared Dutch- and English-learning 18-month-olds' responses to novel words varying in vowel duration or vowel quality. Dutch learners interpreted vowel duration as lexically contrastive, but English learners did not, in keeping with properties of Dutch and English. Both groups performed equivalently when differentiating words varying in vowel quality. Thus, at one and a half years, children's phonological knowledge already guides their interpretation of salient phonetic variation. We argue that early phonological learning is not just a matter of maintaining the ability to distinguish language-relevant phonetic cues. Learning also requires phonological interpretation at appropriate levels of linguistic analysis.
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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 | 0.002 |
| 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 | 0.000 | 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".