Supplemental Material for Effects of Action Video Game Play on Cognitive Skills: A Meta-Analysis
Bibliographic record
Abstract
The possibility that playing action video games is associated with enhancements in certain aspects of cognitive function has attracted significant interest from researchers in education, psychology, and neuroscience. Previous meta-analyses indicated an overall positive relationship between action video game play and cognitive skills. However, follow-up to this previous work is warranted, not only because the amount of data has grown significantly since previous meta-analyses were conducted, but also because previous work left several issues unresolved (e.g., certain meta-analytic procedures). We conducted a literature search using predefined keywords and inclusion criteria to identify studies that examined the relationship between action video game play and cognitive skills. Data from (a) 105 cross-sectional studies (221 effect sizes) and (b) 28 intervention studies with an active control group (91 effect sizes) were analyzed separately via meta-analytic models for dependent effect sizes with robust variance estimates for correlated and hierarchical effects (CHE) and small-sample corrections. Consistent with our hypotheses, action video game players outperformed nonvideo game players in the cross-sectional meta-analysis (large effect, g = 0.64, 95% CI [0.53, 0.74]). Action video game play was causally related to improvements in cognitive skills in the intervention meta-analysis (small effect, g = 0.30, 95% CI [0.11, 0.50]). Publication bias was detected in the cross-sectional data set, with sensitivity analysis showing high heterogeneous estimates of the average unbiased effect. Publication bias was not detected in the intervention data set, but sensitivity analyses also point to heterogeneity. No significant moderators were found for either data set; however, this may be limited by small sample sizes.
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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 | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.466 | 0.016 |
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".