2 Untangling Subjective and Objective Memory in Aging: The Effects of Strategy Use and Gender Differences on Associative Memory Performance
Bibliographic record
Abstract
Objective: In normative aging, there is a decline in associative memory that appears to relate to self-reported everyday use of general memory strategies (Guerrero et al., 2021). Self-reported general strategy use is also strongly associated with self-reported memory abilities (Frankenmolen et al., 2017), which, in turn, are weakly associated with objective memory performance (Crumley et al., 2014). Associative memory abilities and strategy use appear to differ by gender, with women outperforming men and using more memory strategies (Hertzog et al., 2019). In this study, we examine how actual performance and self-reported use of specific strategies on an associative memory task relate to each other and to general, everyday strategy use, and whether these differ by gender. Participants and Methods: An international sample of older adults (N = 566, 53% female, aged 60-80) were administered a demographic questionnaire and online tasks, including 1. the Multifactorial Memory Questionnaire (MMQ) which measures self-reported memory ability, satisfaction, and everyday strategy use (Troyer & Rich, 2018); and 2. the Face-Name Task which measures associative memory (Troyer et al., 2011). Participants were also asked about specific strategies that were used to complete the Face-Name Task. Results: On the Face-Name Task, participants who reported using more strategies performed better (F(3, 562) = 6.51, p < 0.001, n2 = 0.03), with those who reported using three or four strategies performing best (p < .05). There was a significant difference in performance based on the type of strategy used (p(2, 563) = 11.36, p < 0.001, n2 = 0.04), with individuals who relied on a “past experiences/knowledge” strategy performing best (p < .01). Women (M = 0.79, SD = 0.19) outperformed men (M = 0.71, SD = 0.20), f(545) = -4.64, p < 0.001, d = -0.39. No gender differences were found in the number (X2(3, N = 564) = 2.06, p = 0.561) or type (x2(2, N = 564) = 5.49, p = 0.064) of strategies used on the Face-Name Task. Only participants who reported using no strategies on the Face-Name Task had lower scores on the MMQ everyday strategy use subscale (p < .05). A multiple-regression model was used to investigate the relative contributions of the number of strategies used on the Face-Name Task, MMQ everyday strategy subscale score, gender, age, education, and psychological distress to Face-Name Task performance. The only significant predictors in the model were gender (B = 0.08, t(555) = 4.55, p < 0.001) and use of two or more strategies (B = 0.07, f(555) = 2.82, p = 0.005). Conclusions: Reports of greater self-initiated strategy use, and use of a semantic strategy in particular, related to better performance on an associative memory test in older adults. Self-initiated, task-specific strategy use also related to everyday strategy use. The findings extend past work on gender differences to show that women outperform men on an associative memory task but that this is unlikely to be due to self-reported differences in strategy use. The results suggest that self-reported strategy use predicts actual associative memory performance and should be considered in clinical practice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".