Distinct Labels Attenuate 15-Month-Olds’ Attention to Shape in an Inductive Inference Task
Bibliographic record
Abstract
We examined the role of distinct labels on infants' inductive inferences. Thirty-six 15-month-old infants were presented with target objects that possessed a non-obvious property, followed by test objects that varied in shape similarity relative to the target. Infants were tested in one of two groups, a Same Label group in which target and test objects were labeled with the same noun, and a Distinct Label group in which target and test objects were labeled with different nouns. When target and test objects were labeled with the same count noun, infants generalized the non-obvious property to both test objects, regardless of similarity to the target. In contrast, labeling the target and test objects with different count nouns attenuated infants' generalization of the non-obvious property to both high and low-similarity test objects. Our results suggest that by 15 months, infants recognize that object labels provide information about underlying object kind and appreciate that distinct labels are used to designate members of different categories.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".