Compositionality and Statistics in Adjective Acquisition: 4-year-olds Interpret Tall and Short Based on the Size Distributions of Novel Noun Referents
Bibliographic record
Abstract
We investigated 4-year-olds’ understanding of adjective/nouncompositionality and their sensitivity to statistics when interpretingscalar adjectives. In Experiments 1 and 2, children selected tall andshort itemsfrom nine novel objects called pimwits (1-9” in height), or from this arrayplus four taller or shorter distractor objects of the same kind. Changingthe height distributions of the sets shifted children’s judgments of whatcounted as tall and short. However, when distractors differed in name andsurface features from targets, in Experiment 3, judgments did not shift. InExperiment 4, dissimilar distractors did affect judgments when theyreceived the same name as targets. We conclude that 4-year-olds deploy acompositional semantics that is sensitive to statistics and mediated bylinguistic labels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".