Error processing in the adolescent brain: Age-related differences in electrophysiology, behavioral adaptation, and brain morphology
Bibliographic record
Abstract
Detecting errors and adjusting behaviour appropriately are fundamental cognitive abilities that are known to improve through adolescence. The cognitive and neural processes underlying this development, however, are still poorly understood. To address this knowledge gap, we performed a thorough investigation of error processing in a Flanker task in a cross-sectional sample of participants 8 to 19 years of age (n = 98). We examined age-related differences in event-related potentials known to be associated with error processing, namely the error-related negativity (ERN) and the error positivity (Pe), as well as their relationships with task performance, post-error adjustments and regional cingulate cortex thickness and surface area. We found that ERN amplitude increased with age, while Pe amplitude remained constant. A more negative ERN was associated with higher task accuracy and faster reaction times, while a more positive Pe was associated with higher accuracy, independently of age. When estimating post-error adjustments from trials following both incongruent and congruent trials, post-error slowing and post-error improvement in accuracy both increased with age, but this was only found for post-error slowing when analysing trials following incongruent trials. There were no age-independent associations between either ERN or Pe amplitude and cingulate cortex thickness or area measures.
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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".