Words Are Not Merely Features: Only Consistently Applied Nouns Guide 4-year-olds' Inferences About Object Categories
Bibliographic record
Abstract
Although there is considerable evidence that nouns highlight category-based commonalities, including both those that are perceptually available and those that reflect underlying conceptual similarity, some have claimed that words function merely as features of objects. Here, we directly test these alternative accounts. Four-year-olds (n = 140) were introduced to two different novel animals that were highlighted with nouns, adjectives, or stickers. Children heard a nonobvious novel property applied to the first animal and were asked whether this property applied to other animals that filled the similarity space between the original two animals. When the two animals were named with the same noun, children extended the property broadly throughout the similarity space. When the animals were marked with adjectives or stickers, children adopted a similarity-based pattern. These findings demonstrate clearly that nouns exert a unique effect on categorization—they promote category formation and engage conceptual reasoning beyond perceptual similarity alone.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".