2 Untangling Subjective and Objective Memory in Aging: The Effects of Strategy Use and Gender Differences on Associative Memory Performance
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».