Aging shifts the relative contributions of episodic and semantic memory to decision-making.
Bibliographic record
Abstract
Healthy aging is accompanied by well-characterized shifts in memory systems: episodic memory tends to decline with age while semantic memory remains relatively intact, with some knowledge domains strengthening. Beyond reflecting on the past, these distinct memory systems often guide decisions about the future. Yet how such age-related memory shifts influence simple value-based choices remain understudied. Here, younger (18-24 years) and older (61-75 years) adults completed a card game in which they could use task-relevant episodic memories to maximize the number of points they earned. Critically, they could also use task-irrelevant semantic memories to guide their choices. Both younger and older adults successfully used episodic memory to make decisions, but older adults did so less reliably than younger adults. Further, while younger adults strategically suppressed task-irrelevant semantic memories when a relevant episodic memory could be used, older adults used semantic memory to guide their decisions regardless of the relevance of episodic memory. We provide evidence that declining inhibitory control may play a role in how older adults arbitrate between competing memory sources when making decisions. These effects are consistent with the literature on age-related shifts in memory and cognitive control systems and add to a growing body of work on how episodic memories inform reinforcement learning and value-based decision-making. Our findings highlight how patterns of age-related memory differences can have consequences for value-based choices, which has implications for other types of decision-making, from the economic to the mundane. (PsycInfo Database Record (c) 2022 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".