Conflict monitoring, error detection, and inhibition: behavioural and electrophysiological features
Bibliographic record
Abstract
Event-related potentials of performance monitoring, including N2 (conflict monitoring), error-related negativity and error positivity (ERN and Pe; error monitoring), and P3 (inhibition) have been studied. However, conflict monitoring lacks a behavioural measure, and the functional significance of ERN, Pe, and P3 are debated. To address these issues, a behavioural measure of conflict monitoring was tested by subtracting the reaction time (RT) of a simple from a choice RT task to isolate conflict monitoring; the functions of error monitoring and inhibition were examined. The RT difference correlated with the N2 area (longer conflict monitoring related to a larger N2). ERN and Pe areas were negatively and positively correlated with errors, respectively. P3 magnitude and onset were correlated with an inhibition index. The new behavioural measure provides an accessible way to study conflict monitoring. Theories of conflict monitoring for ERN, error awareness for Pe, and inhibition for P3 were replicated and extended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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".