No association between error‐related <scp>ERP</scp>s and trait anxiety in a nonclinical sample: Convergence across analytical methods including mass‐univariate statistics
Bibliographic record
Abstract
Enhanced error monitoring, as indexed by increased amplitude of the error-related negativity (ERN) event-related potential (ERP) component, has been suggested to reflect a vulnerability neuro-marker of anxiety disorders. Another error-related ERP component is the error positivity (Pe), which reflects late-stage error processing. The associations between heightened ERN and Pe amplitudes and anxiety levels in the nonclinical population have been inconsistent. In this preregistered study, we examined the association between anxiety, ERN, and Pe, using different analytical methods (mass-univariate analyses, MUAs and conventional analyses), self-reported anxiety scales (STAI and STICSA), and trial numbers (all correct trials and equal numbers of correct and error trials). In a sample of 82 healthy adults, both conventional and MUAs demonstrated a robust enhancement of the ERN and Pe to errors relative to the correct-response ERPs. However, the mass-univariate approach additionally unveiled a wider array of electrodes and a longer effect duration for this error enhancement. Across the analytic methods, the results showed a lack of consistent correlation between trait anxiety and error-related ERPs. Findings were not modulated by trial numbers, analyses, or anxiety scales. The present results suggest a lack of enhancement of error monitoring by anxious traits in individuals with subclinical anxiety and those with clinical anxiety but without a clinical diagnosis. Importantly, the absence of such correlation questions the validity of the ERN as a neural marker for anxiety disorders. Future studies that investigate neuro-markers of anxiety may explore alternative task designs and employ robust statistics to provide a more comprehensive understanding of anxiety vulnerability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".