Familiarization May Minimize Age-Related Declines in Rule-Based Category Learning
Bibliographic record
Abstract
Being able to categorize promotes cognitive economy by reducing the amount of information that an individual needs to remember. This ability is particularly important in older adulthood, when executive functioning abilities are known to decline. Prior research has shown that older adults can learn simple categories quite well but struggle when learning more complex categories which place a demand on executive function resources. The goal of Experiments 1 to 3 were to assess whether familiarizing older adults with complex rule-based or non-rule-based categories prior to beginning a categorization task would minimize age-related categorization deficits. Both rule-based and non-rule-based category learning improved among older adults following pretraining, but the improvements to rule-based learning were more drastic, suggesting that executive functioning plays a heavier role in rule-based category learning. Findings provide a potential solution for improving the category learning abilities of older adults.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".