Expertise supports memory for arbitrary relations in aging.
Bibliographic record
Abstract
Accessing knowledge acquired across the lifespan differs from our ability to recall recent episodes or experiences, although the two processes are highly interrelated. Whereas episodic memory function typically declines with normal aging, semantic memory, including language and factual knowledge, are more robust to age-related decline. The structure and stability of acquired knowledge make it a potential asset in helping remember new information, even when it is completely unrelated. In the present study, we examined whether specialized knowledge about birds may help bird experts retain arbitrary episodic associations between (faces) paired with domain-relevant information (bird images) versus domain-irrelevant information (car images). After studying bird-face or car-face pairs, participants decided whether test pairs were either intact or recombined. Experts showed a large memory advantage for pairs in which faces had previously been paired with a bird versus a car, but no difference was found in novices. Although broad age-related declines in memory persisted, this benefit of prior knowledge was prevalent across the age range, such that relational memory performance in 75-year-old experts was roughly equivalent to corresponding performance in 20-year-old novices. These results show how expertise can offset age-related memory decline by allowing experts of all ages to efficiently link novel information to structured knowledge that has been accumulated across the lifetime. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".