Sex differences in verbal working memory: A systematic review and meta-analysis.
Bibliographic record
Abstract
The present meta-analysis aimed to quantify sex differences in verbal working memory and to examine potential moderators of these differences. We examined 802 effect sizes from 478 samples in 284 studies in a multilevel meta-analysis. Results revealed a small overall female advantage (g = .028, 95% CI [.006, .050]). In the overall sample, results showed that sex differences differed across tasks. Specifically, the female advantage was significant for cued tasks (g = .079, 95% CI [.030, .128]) and Free Recall tasks (g = .145, 95% CI [.102, .188]) whereas there was a male advantage on Complex Span (g = -.042, 95% CI [-.083, -.002]), and no sex differences on Serial Recall (g = .003, 95% CI [-.055, .050]), and Simple Span tasks (g < .001, 95% CI [-.034, .033]). Within each task, we found that recall direction, stimulus type, presentation format, response format, and age accounted for significant variance in at least 1 of the tasks. Analyses provided no evidence of a publication bias, although the female advantage varied as a function of sample source, whether the title made reference to sex, and whether authors had to be contacted to obtain relevant data. Results are discussed in terms of their implications for sex differences in episodic memory and in the context of clinical applications and theory building. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.029 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".