Modeling Age Differences in Infant Category Learning
Bibliographic record
Abstract
We used an encoder version of cascade correlation to simulate Younger and Cohen's (1983, 1986) finding that 10-month-olds recover attention on the basis of correlations among stimulus features, but 4- and 7-month-olds recover attention on the basis of stimulus features. We captured these effects by varying the score threshold parameter in cascade correlation, which controls how deeply training patterns are learned. When networks learned deeply, they showed more error to uncorrelated than to correlated test patterns, indicating that they abstracted correlations during familiarization. When prevented from learning deeply, networks decreased error during familiarization and showed as much error to correlated as to uncorrelated tests but less than to test items with novel features, indicating that they learned features but not correlations among features. Our explanation is that older infants learn more from the same exposure than do younger infants. Unlike previous explanations that postulate unspecified qualitative shifts in processing with age, our explanation focuses on quantitatively deeper learning with increasing age. Finally, we provide some new empirical evidence to support this explanation.
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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".