Learning facts during aging: the benefits of curiosity
Bibliographic record
Abstract
Background/study context: Recent studies have shown that young adults better remember factual information they are curious about. It is not entirely clear, however, whether this effect is retained during aging. Here, the authors investigated curiosity-driven memory benefits in young and elderly individuals. METHODS: In two experiments, young (age range 18-26) and older (age range 65-89) adults read trivia questions and rated their curiosity to find out the answer. They also attended to task-irrelevant faces presented between the trivia question and the answer. The authors then administered a surprise memory test to assess recall accuracy for trivia answers and recognition memory performance for the incidentally learned faces. RESULTS: In both young and elderly adults, recall performance was higher for answers to questions that elicited high levels of curiosity. In Experiment 1, the authors also found that faces presented in temporal proximity to curiosity-eliciting trivia questions were better recognized, indicating that the beneficial effects of curiosity extended to the encoding of task-irrelevant material. CONCLUSIONS: These findings show that elderly individuals benefit from the memory-enhancing effects of curiosity. This may lead to the implementation of learning strategies that target and stimulate curiosity in aging.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".