Individual differences in executive functions and retrieval efficacy in older adults.
Bibliographic record
Abstract
Two prominent aspects of memory problems in older adults are a difficulty in retrieving recent episodic events and an often transient inability to retrieve names and other well-known facts from semantic memory. The question addressed in the present studies was whether these age-related difficulties reflect a common cause-a retrieval problem related to inefficient executive functions (EF). In the first study, 50 older adults were given 4 tests of EF; a derived composite measure correlated strongly with a measure of retrieval efficacy in free recall, less strongly with paired-associate recall, and nonsignificantly with retrieval of general knowledge. A second study used somewhat different measures of EF and also different measures of retrieval from semantic memory, and this study did find significant relations between EF, episodic memory, and knowledge retrieval. Changes in the specific tests representing both EF and memory retrieval changed the relations between them, suggesting that no one task is a pure measure of the theoretical constructs of either EF or episodic and semantic memory. Taken together, the 2 studies showed that individual differences in EF in older adults are correlated with retrieval efficacy in both episodic and semantic memory but also that these relations depend on the specific measures chosen to represent both EF and memory retrieval. (PsycINFO Database Record (c) 2018 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".