Ask how they did it: untangling the relationships between task-specific strategy use, everyday strategy use, and associative memory
Bibliographic record
Abstract
OBJECTIVE: Past research has shown that self-reported everyday strategy use and task-specific strategy use are related to associative memory performance in aging. Understudied is the relationship between these types of strategy use, whether they predict associative memory performance, and how this may differ across genders. METHOD: = 566, 53% female, ages 60-80) completed this online study. Study measures included 1. Multifactorial Memory Questionnaire (MMQ) Strategy Use subscale, a self-report measure of everyday strategy use, 2. Face-Name Task (FNT), a measure of associative memory, and 3. self-initiated number and types of strategies used on the FNT. Analyses examined the interrelationships among all study measures and their relative contributions to FNT performance while accounting for intraindividual factors. RESULTS: Participants who reported using more strategies on the FNT performed better than those who used fewer or no strategies; those who reported using at least three strategies and relating FNT to past experience performed best. Women outperformed men on the FNT but did not differ in task-specific strategy use. Participants who reported using no strategies on the FNT had lower MMQ Strategy Use scores. A multiple regression analysis indicated that female gender and using at least two task strategies were significant predictors of greater FNT performance. CONCLUSIONS: The results indicate that task-specific strategy use relates more to associative memory performance than to everyday strategy use, but neither accounts for the female advantage in FNT performance. Findings encourage querying task-specific strategy use to contextualize age-related associative memory decline.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".