Syntactic Prediction Adaptation Accounts for Language Processing and Language Learning
Bibliographic record
Abstract
Abstract A previous study has shown that children use recent input to adapt their syntactic predictions and use these adapted predictions to infer the meaning of novel words. In the current study, we investigated whether children could use this mechanism to disambiguate words whose interpretation as a noun or a verb is ambiguous. We tested 2‐ to 4‐year‐old French children using the phrase la petite followed by a homophone that could be interpreted as either a noun or a verb. We assigned the children to a noun condition or a verb condition. Before the test, those in the noun condition were exposed to sentences where la petite predicted nouns, and those in the verb condition to sentences where la petite predicted verbs. At testing, 3‐ to 4‐year‐olds, but not 2‐year‐olds, from the verb condition looked at the verb interpretation longer than did the children in the noun condition. This suggests a progression in children's ability to rely on input to adapt their predictions in language comprehension.
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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 | 0.005 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".