14‐month‐olds exploit verbs’ syntactic contexts to build expectations about novel words
Bibliographic record
Abstract
During their second year of life, infants develop a rudimentary understanding of grammatical categories based on their knowledge and use of frequent function words. The current study inquired whether, at only 14 months of age, infants can track co-occurrence patterns between function words and content words (e.g., determiners can precede nouns, and pronouns can precede verbs), and use these previously encountered syntactic contexts to build expectations about which function words can co-occur with novel words. Using a habituation paradigm, French-learning 14-month-olds were presented with utterances containing two novel words preceded by function words (either two determiners in the Novel Nouns condition or two pronouns in the Novel Verbs conditions). We found that at test, infants looked longer during trials in which the novel words occurred in an unexpected syntactic context (following a pronoun for infants in the Novel Nouns condition and following a determiner for infants in the pooled analysis of the three Novel Verbs conditions). Hence, our results confirm previous findings on infants' sensitivity to noun contexts and most importantly demonstrate that their sensitivity to the co-occurrence of verbs with pronouns begins much earlier than previously understood.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".