Looking for Wugs in all the Right Places: Children's Use of Prepositions in Word Learning
Bibliographic record
Abstract
To help infer the meanings of novel words, children frequently capitalize on their current linguistic knowledge to constrain the hypothesis space. Children's syntactic knowledge of function words has been shown to be especially useful in helping to infer the meanings of novel words, with most previous research focusing on how children use preceding determiners and pronouns/auxiliary to infer whether a novel word refers to an entity or an action, respectively. In the current visual world experiment, we examined whether 28- to 32-month-olds could exploit their lexical semantic knowledge of an additional class of function words-prepositions-to learn novel nouns. During the experiment, children were tested on their ability to use the prepositions in, on, under, and next to to identify novel creatures displayed on a screen (e.g., The wug is on the table), as well as their ability to later identify the creature without accompanying prepositions (e.g., Look at the wug). Children overall demonstrated understanding of all the prepositions but next to and were able to use their knowledge of prepositions to learn the associations between novel words and their intended referents, as shown by greater-than chance looks to the target referent when no prepositional phrase was provided.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".