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Record W4390078629 · doi:10.1017/s1355617723008494

2 Untangling Subjective and Objective Memory in Aging: The Effects of Strategy Use and Gender Differences on Associative Memory Performance

2023· article· en· W4390078629 on OpenAlexaff
Caitlin M. Terao, Sara Pishdadian, Morris Moscovitch, R. Shayna Rosenbaum

Bibliographic record

VenueJournal of the International Neuropsychological Society · 2023
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsBaycrest HospitalUniversity of TorontoYork University
Fundersnot available
KeywordsTask (project management)PsychologyNormativeContent-addressable memoryCognitive psychologyDevelopmental psychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.077
GPT teacher head0.361
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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Same venueJournal of the International Neuropsychological SocietySame topicAging and Gerontology ResearchFrench-language works237,207