Age Differences in Motivated Cognition: A Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: The goal of this preregistered study was to synthesize empirical findings on age differences in motivated cognition using a meta-analytic approach, with a focus on the domains of cognitive control and episodic memory. METHODS: A systematic search of articles published before July 2022 yielded 27 studies of cognitive control (N = 1,908) and 73 studies of memory (N = 5,837). Studies had to include healthy younger and older adults, a within-subjects or between-subjects comparison of motivation (high vs low), and a measure of cognitive control or memory. The Age × Motivation effect size was meta-analyzed using random-effects models, and moderators were examined using meta-regressions and subgroup analyses. RESULTS: Overall, the Age × Motivation interaction was not significant in either cognitive domain, but the effect sizes in both domains were significantly heterogeneous, indicating a possible role of moderating factors in accounting for effect size differences. Moderator analyses revealed significant moderation by incentive type for episodic memory, but not for cognitive control. Older adults' memory was more sensitive to socioemotional rewards, whereas younger adults' memory was more sensitive to financial gains. DISCUSSION: Findings are discussed with reference to the dopamine hypothesis of cognitive aging and to life-span theories of motivational orientation. None of these theories is fully supported by the meta-analysis findings, highlighting the need for an integration of neurobiological, cognitive process, and life-span-motivational perspectives.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.036 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".