Age and anxiety symptoms jointly moderated the curvilinear changes in trial-level ERN following repeated errors on a Go/No-Go task during early adolescence
Bibliographic record
Abstract
The ability to detect and monitor errors enables us to maintain optimal performance across tasks. One neurophysiological index of error monitoring is the error-related negativity (ERN), a fronto-central negative deflection peaking between 0-150 ms following an erroneous response. The developmental literature has illustrated age-related differences in the ERN and its association with anxiety during development. However, this literature focuses on the between-person differences of the ERN and cannot speak to whether there exist meaningful within-person changes in the ERN throughout a task. We examined the within-person variations of the ERN in 115 community-dwelling nine-to-12-year-olds (66 girls; Mean age/SD=11.00/1.16 years), a critical period for the development of error processing and anxiety psychopathology. Participants completed an EEG Go/No-Go task and reported their anxiety symptoms. Multilevel growth analyses yielded significant within-person, curvilinear changes in the ERN throughout the task. Youths’ trial-level ERN increased (i.e., became more negative) as more errors were made earlier in the task, but decreased as subsequent errors were committed later in the task. This curvilinear pattern was evident in older, but not younger, youths. Age further interacted with anxiety symptoms: younger youths with higher anxiety showed a continuous increase in the ERN throughout the task, whereas older youths with higher anxiety showed an increase in the ERN with early errors, followed by a decline in the ERN in later error trials. Our study contributed novel evidence for the development of the ERN and the underlying mechanisms of the ERN-anxiety relationship that cannot be captured by between-person approaches.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".