Reward-Enhanced Memory in Younger and Older Adults
Bibliographic record
Abstract
OBJECTIVES: We investigated how the anticipation of remote monetary reward modulates intentional episodic memory formation in younger and older adults. On the basis of prior findings of preserved reward-cognition interactions in aging, we predicted that reward anticipation would be associated with enhanced memory in both younger and older adults. On the basis of previous demonstrations of a time-dependent effect of reward anticipation on memory, we expected the memory enhancement to increase with study-test delay. METHOD: In Experiment 1, younger and older participants encoded a series of picture stimuli associated with high- or low-reward values. At test (24-hr postencoding), recognition hits resulted in either high or low monetary rewards, whereas false alarms were penalized to discourage guessing. Experiment 2 was similar to Experiment 1, but the study-test delay was manipulated within subjects (immediate vs 24hr). RESULTS: In Experiment 1, younger and older adults showed enhanced recognition for high-reward pictures compared with low-reward pictures. Experiment 2 replicated this finding and additionally showed that the effect did not extend to immediate recognition. DISCUSSION: The current findings provide support for a time-dependent mechanism of reward-based memory enhancement. They also suggest that aging leaves intact the positive influence of reward anticipation on intentional long-term memory formation.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".