Stereotype Threat Effects on Older Adults’ Episodic and Working Memory: A Meta-Analysis
Bibliographic record
Abstract
Background and Objectives: Prior research has shown that exposure to negative age-based stereotype threat (ST) can undermine older adults' memory performance. The objective of the current meta-analysis was to examine the reliability and magnitude of ST effects on older adults' episodic and working memory performance-two forms of memory that typically show the greatest age-related declines. In addition, we examined potential moderators of age-based ST including type of ST manipulation, type and timing of memory task, participant age and education level. Research Design and Method: A total of 23 samples for episodic memory and 15 samples for working memory were derived from 19 published and 4 unpublished articles and analyzed in two separate meta-analyses. Results: Analyses revealed a reliable effect of ST on both older adults' episodic (d = 0.373) and working memory performance (d = 0.253). Interestingly, the age-based ST effect was only significant when blatant ST manipulations were used with episodic memory tasks or when subtle ST manipulations were used with working memory tasks. Moreover, within episodic memory, the ST effect only reached significance for recall but not cued-recall or recognition performance, and for immediate but not delayed tests of memory. Neither age nor level of education moderated the association between ST and older adults' memory performance. Discussion and Implications: These results highlight the vulnerability of both older adults' episodic and working memory performance to age-based ST. When measuring older adults' memory performance in a research context, we must therefore be wary of exposing participants to common stereotypes about aging and memory.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.023 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".