Aging and recognition memory: A meta-analysis.
Bibliographic record
Abstract
Recognizing a stimulus as previously encountered is a crucial everyday life skill and a critical task motivating theoretical development in models of human memory. Although there are clear age-related memory deficits in tasks requiring recall or memory for context, the existence and nature of age differences in recognition memory remain unclear. The nature of any such deficits is critical to understanding the effects of age on memory because recognition tasks allow fewer strategic backdoors to supporting memory than do tasks of recall. Consequently, recognition may provide the purest measure of age-related memory deficit of all standard memory tasks. We conducted a meta-analysis of 232 prior experiments on age differences in recognition memory. As an organizing framework, we used signal-detection theory (Green & Swets, 1966; Macmillan & Creelman, 2005) to characterize recognition memory in terms of both discrimination between studied items and unstudied lures (d') and response bias or criterion (c). Relative to young adults, older adults showed reduced discrimination accuracy and a more liberal response criterion (i.e., greater tendency to term items new). Both of these effects were influenced by multiple, differing variables, with larger age deficits when studied material must be discriminated from familiar or related material, but smaller when studying semantically rich materials. These results support a view in which neither the self-initiation of mnemonic processes nor the deployment of strategic processes is the only source of age-related memory deficits, and they add to our understanding of the mechanisms underlying those changes. (PsycINFO Database Record (c) 2019 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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.024 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".