Reporting bias, not external focus: A robust Bayesian meta-analysis and systematic review of the external focus of attention literature.
Bibliographic record
Abstract
Evidence has ostensibly been accumulating over the past 2 decades suggesting that an external focus on the intended movement effect (e.g., on the golf club during a swing) is superior to an internal focus on body movements (e.g., on your arms during a swing) for skill acquisition. Seven previous meta-studies have all reported evidence of external focus superiority. The most comprehensive of these concluded that an external focus enhances motor skill retention, transfer, and performance and leads to reduced eletromyographic activity during performance and that more distal external foci are superior to proximal external foci for performance. Here, we reanalyzed these data using robust Bayesian meta-analyses that included several plausible models of publication bias. We found moderate to strong evidence of publication bias for all analyses. After correcting for publication bias, estimated mean effects were negligible: g = 0.01 (performance), g = 0.15 (retention), g = 0.09 (transfer), g = 0.06 (electromyography), and g = -0.01 (distance effect). Bayes factors indicated data favored the null for each analysis, ranging from BF01 = 1.3 (retention) to 5.75 (performance). We found clear evidence of heterogeneity in each analysis, suggesting the impact of attentional focus depends on yet unknown contextual factors. Our results contradict the existing consensus that an external focus is always more effective than an internal focus. Instead, focus of attention appears to have a variety of effects that we cannot account for, and, on average, those effects are small to nil. These results parallel previous metascience suggesting publication bias has obfuscated the motor learning literature. (PsycInfo Database Record (c) 2024 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 | 0.332 | 0.589 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.038 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".