Transparent Meta-Analysis: Does Aging Spare Prospective Memory with Focal vs. Non-Focal Cues?
Bibliographic record
Abstract
BACKGROUND: Prospective memory (ProM) is the ability to become aware of a previously-formed plan at the right time and place. For over twenty years, researchers have been debating whether prospective memory declines with aging or whether it is spared by aging and, most recently, whether aging spares prospective memory with focal vs. non-focal cues. Two recent meta-analyses examining these claims did not include all relevant studies and ignored prevalent ceiling effects, age confounds, and did not distinguish between prospective memory subdomains (e.g., ProM proper, vigilance, habitual ProM) (see Uttl, 2008, PLoS ONE). The present meta-analysis focuses on the following questions: Does prospective memory decline with aging? Does prospective memory with focal vs. non-focal cues decline with aging? Does the size of age-related declines with focal vs. non-focal cues vary across ProM subdomains? And are age-related declines in ProM smaller than age-related declines in retrospective memory? METHODS AND FINDINGS: A meta-analysis of event-cued ProM using data visualization and modeling, robust count methods, and conventional meta-analysis techniques revealed that first, the size of age-related declines in ProM with both focal and non-focal cues are large. Second, age-related declines in ProM with focal cues are larger in ProM proper and smaller in vigilance. Third, age-related declines in ProM proper with focal cues are as large as age-related declines in recall measures of retrospective memory. CONCLUSIONS: The results are consistent with Craik's (1983) proposal that age-related declines on ProM tasks are generally large, support the distinction between ProM proper vs. vigilance, and directly contradict widespread claims that ProM, with or without focal cues, is spared by 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 | 0.041 | 0.103 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.039 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".