24-Month-Olds’ Selective Learning Is Not an All-or-None Phenomenon
Bibliographic record
Abstract
Evidence that children maintain some memories of labels that are unlikely to be shared by the broader linguistic community suggests that children's selective learning is not an all-or-none phenomenon. Across three experiments, we examine the contexts in which 24-month-olds show selective learning and whether they adjust their selective learning if provided with cues of in-context relevance. In each experiment, toddlers were first familiarized with a source who acted on familiar objects in either typical or atypical ways (e.g., used a car to mimic driving or hop like a rabbit) or labeled familiar objects incorrectly (e.g., called a spoon a "brush"). The source then labeled unfamiliar objects using either a novel word (e.g., fep; Experiment 1) or sound (e.g., ring; Experiments 2 and 3). Results indicated that toddlers learnt words from the typical source but not from the atypical or inaccurate source. In contrast, toddlers extended sound labels only when a source who had previously acted atypically provided the sound labels. Thus, toddlers, like preschoolers, avoid forming semantic representations of new object labels that are unlikely to be relevant in the broader community, but will form event-based memories of such labels if they have reason to suspect such labels will have in-context relevance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".