Learning Count Nouns and Adjectives: Understanding the Contributions of Lexical Form Class and Social-Pragmatic Cues
Bibliographic record
Abstract
In two experiments, one hundred ninety-two 3-year-olds, 4-year-olds, and adults heard a novel word for a target object and then were asked to extend the label to one of two test objects, one matching in shape-based object category (the shape match) and the other matching in a property other than shape (the property match). We independently manipulated the lexical form class cues (count noun, adjective) and social-pragmatic cues (point actions, property-highlighting actions) accompanying the label. The impact of these two types of cue on extension differed markedly across age groups. Adults and 4-year-olds extended the word to the property match significantly more often when the term was modeled as an adjective and when it was presented with property-highlighting actions; but adults extended both adjectives and count nouns systematically to the property match when the speaker highlighted the non-shape property, whereas 4-year-olds systematically extended only adjectives to the property match under these conditions. Three-year-olds extended the word to the property match significantly more often when the label was modeled as an adjective but were not significantly affected by the social-pragmatic cues; and they failed to extend either adjectives or count nouns systematically to the property match when the speaker highlighted the non-shape property. We discuss the results in terms of the proposal that word learning draws on cues from multiple sources and the nature of the “shape bias” in lexical development.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".