Monolingual, bilingual, trilingual: infants' language experience influences the development of a word‐learning heuristic
Bibliographic record
Abstract
How infants learn new words is a fundamental puzzle in language acquisition. To guide their word learning, infants exploit systematic word-learning heuristics that allow them to link new words to likely referents. By 17 months, infants show a tendency to associate a novel noun with a novel object rather than a familiar one, a heuristic known as disambiguation. Yet, the developmental origins of this heuristic remain unknown. We compared disambiguation in 17- to 18-month-old infants from different language backgrounds to determine whether language experience influences its development, or whether disambiguation instead emerges as a result of maturation or social experience. Monolinguals showed strong use of disambiguation, bilinguals showed marginal use, and trilinguals showed no disambiguation. The number of languages being learned, but not vocabulary size, predicted performance. The results point to a key role for language experience in the development of disambiguation, and help to distinguish among theoretical accounts of its emergence.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".