Preverbal infants’ sensitivity to grammatical dependencies
Bibliographic record
Abstract
During their first months of life, infants can already distinguish function words (e.g., pronouns and determiners) from content words (e.g., verbs and nouns). Little research has explored preverbal infants' sensitivity to the relationships between these word categories. This preregistered study examines whether French-learning 8- and 11-month-olds track the grammatical dependencies between determiners and nouns as well as pronouns and verbs. Using the Visual Fixation Procedure, infants were presented with lists containing either grammatical (e.g., tu manges "you eat", des biberons "some bottles") or ungrammatical (e.g., des manges "some eat", tu biberons "you bottle") phrases. In Experiment 1 (N = 59), the lists involved common nouns and verbs, while in Experiment 2 (N = 28), only common verbs were used. Eleven-month-olds showed a clear preference for correct over incorrect co-occurrences in both experiments, while 8-month-olds showed a trend in the same direction. These results suggest that before their first birthday, infants' storage and access of words and word sequences are sufficiently sophisticated to include the means to track categorical dependencies. This early sensitivity to co-occurrence patterns may be greatly beneficial for constraining lexical access and later on for learning novel words' syntactic and semantic properties.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".