Perceptual Category Learning: Similarity and Differences Between Children and Adults
Bibliographic record
Abstract
Studies of category learning have supported the idea that people rely on at least two cognitive systems when learning new categories.A verbally-mediated system is best suited for learning rule-based categories, and a nonverbal, procedural system is best suited for learning categories that are not defined by a rule.To further examine the cognitive systems involved in categorization, two experiments explored developmental differences in perceptual category learning.In the first experiment, children and adults were asked to learn a set of categories consisting of stimuli equated on feature salience.A single-feature rule (the criterial attribute) or overall similarity would allow for perfect performance on this task.We found that adults made significantly more rule-based responses to the test stimuli than did children.A second experiment examined non-rule-based category learning by having children and adults complete a prototype abstraction task.Children showed evidence of prototype abstraction, with many children showing a similar pattern of responding to adults.Results are discussed within the COVIS framework.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".