Diminished error-related negativity and error positivity in children and adults with externalizing problems and disorders: a meta-analysis on error processing
Bibliographic record
Abstract
Background: Deficits in error processing are reflected in an inability of people with externalizing problems to adjust their problem behaviour. The present study contains 2 meta-analyses, testing whether error processing — indexed by the event-related potentials error-related negativity (ERN) and error positivity (Pe) — is reduced in children and adults with externalizing problems and disorders compared to healthy controls. Methods: We conducted a systematic search in PubMed (1980 to December 2018), PsycInfo (1980 to December 2018) and Scopus (1970 to December 2018), identifying 328 studies. We included studies that measured error processing using the Eriksen flanker task, the go/no-go task or the stop-signal task in healthy controls and in adults or children with clearly described externalizing behavioural problems (e.g., aggression) or a clinical diagnosis on the externalizing spectrum (e.g., addiction). Results: Random-effect models (ERN: 23 studies, 1739 participants; Pe: 27 studies, 1456 participants) revealed a reduced ERN amplitude (Hedges’ g = 0.44, 95% confidence interval [CI] 0.29 to 0.58) and a reduced Pe amplitude (Hedges’ g = −0.27, 95% CI −0.44 to −0.09) during error processing in people with externalizing problems or disorders compared to healthy controls. Type of diagnosis, age and the presence of performance feedback or comorbidity did not moderate the results. The employed cognitive task was a moderator for Pe but not for ERN. The go/no-go task generated a greater amplitude difference in Pe than the Eriksen flanker task. Small-sample assessment revealed evidence of publication bias for both event-related potentials. However, a p curve analysis for ERN showed that evidential value was present; for Pe, the p curve analysis was inconclusive. Limitations: The moderators did not explain the potential heterogeneity in most of the analysis, suggesting that other disorder- and patient-related factors affect error processing. Conclusion: Our findings indicate the presence of compromised error processing in externalizing psychopathology, suggesting diminished activation of the prefrontal cortex during performance monitoring.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.019 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".