Dorsal Medial Prefrontal Cortex Plays a Necessary Role in Rapid Error Prediction in Humans
Bibliographic record
Abstract
Activity in human dorsal anterior cingulate cortex (dACC) is correlated with errors, near-misses, and response conflict. Based on these observations, this region has been cast as playing a central role in models of error processing, conflict monitoring, and cognitive control. However, clear evidence that this region of the brain is necessary for these processes has been elusive. We studied the effects of damage to this region on four different error-related measures in five patients, and 19 healthy participants. Most error-related indices were not affected by such damage: patients had intact post-error slowing, and were able to report and to correct errors after they were made with accuracies comparable with the control group. However, all five patients were notably slow to correct errors, suggesting a deficit in on-line error prediction. This slowing was associated with impairment in the conscious prediction of error likelihood before a response. This finding constitutes important converging evidence for a critical role for human dACC in error monitoring, and sheds light on the selectivity and timing of the error-related process affected by dACC damage.
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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 | 0.001 |
| 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 | 0.001 |
| 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".