Infants’ categorization of novel objects with more or less obvious features
Bibliographic record
Abstract
In five experiments, 14- to 15-month-old infants' categorization of objects on the basis of more or less obvious features was investigated. Using an object examining paradigm, a total of 200 infants were familiarized with novel objects that shared either more obvious features (i.e., easily visible) or less obvious features (i.e., accessible by lifting a flap), followed by an in-category object and an out-of-category object. When only perceptual information was available, infants formed a category on the basis of the more obvious features but not on the basis of the less obvious features (Experiments 1 and 3). When infants were provided with animacy cues and/or object names, they formed categories on the basis of either more or less obvious features (Experiments 2, 4, and 5). The results of these studies delineate the role of animacy cues and object names in establishing categories on the basis of less obvious features.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".