Children automatically abstract categorical regularities during statistical learning
Bibliographic record
Abstract
Statistical learning allows us to discover myriad structures in our environment, which is saturated with information at many different levels-from items to categories. How do children learn different levels of information-about regularities that pertain to items and the categories they come from-and how does this differ from adults? Studies on category learning and memory have suggested that children may be more focused on items than adults. If this is also the case for statistical learning, children may not extract and learn the multi-level regularities that adults can. We report three experiments showing that children and adults extract both item- and category-level regularities in statistical learning. In Experiments 1 and 2, we show that both children and adults can learn structure at the item and category levels when they are measured independently. In Experiment 3, we show that both children and adults learn about categories even when exposure does not require this: both are able to generalize their learning from the item to the category level. Results indicate that statistical learning operates across multi-level structure in children and adults alike, enabling generalization of learning from specific items to exemplars from categories of those items that observers have never seen. Even though children may be more focused on items during other forms of learning, they learn about categories from item-level input during statistical learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 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".