Assessing and adjusting for publication bias in the relationship between anxiety and the error-related negativity
Bibliographic record
Abstract
Many clinical neuroscience investigations have suggested that trait anxiety is associated with increased neural reactivity to mistakes in the form of an event-related potential called the error-related negativity (ERN). Several recent meta-analyses indicated that the anxiety-ERN association was of a small-to-medium effect size, however, these prior investigations did not comprehensively adjust effect sizes for publication bias. Here, in an updated meta-analysis (k=58, N=3819), we found support for an uncorrected effect size of r =-.19, and applied a range of methods to test for and correct publication bias (trim-and-fill, PET, PEESE, Peters’ test, three-parameter selection model). The majority of bias-correction methods suggested that the correlation between anxiety and the ERN is non-zero, but smaller than the uncorrected effect size (average adjusted effect size: r =-.12, range: r =-.05 to -.18). Moderation analyses also revealed more robust effects for clinical anxiety and anxious samples characterised by worry, however, it should be noted that these larger effects were also associated with elevated indicators of publication bias relative to the overall analysis. Mixed anxiety and sub-clinical anxiety were not associated with the amplitude of the ERN. Our results suggest that the anxiety-ERN relationship survives multiple corrections for publication bias, albeit not among all sub-types and populations of anxiety. Nevertheless, only 50% of the studies included in our analysis reported significant results, indicating that future research exploring the anxiety-ERN relationship would benefit from increased statistical power.
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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 | 0.213 | 0.414 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.022 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".