Proactive and Reactive Processes in the Medial Frontal Cortex: An Electrophysiological Study
Bibliographic record
Abstract
The posterior medial frontal cortex (pMFC) is known to be involved in adaptive goal-directed behavior, but its specific function is not yet clear. Most theories have proposed that the pMFC monitors performance in a reactive manner only, but it is possible that the pMFC also contributes to performance monitoring in a proactive manner. To date, the evidence for proactive pMFC activity is equivocal. Here, we investigated pMFC activity before, during and after the performance of a challenging motor task. Participants navigated a cursor through narrow and wide mazes in randomly intermixed trials. On each trial, participants saw previews of the actual maze display prior to gaining control of the cursor. Event-related potentials (ERPs) to the preview displays were compared to ERPs elicited by no-go signals and errors. Compared to the wider maze, the preview display for the more challenging narrow maze elicited a medial-frontal negativity (MFN) similar to the ERP components elicited by no-go signals and errors. Like these known ERP components, the preview-elicited MFN appeared to be generated from a source in pMFC. This is consistent with the hypothesis that the pMFC participates in adaptive behavior whenever there is a need for increased effort to maintain successful task performance.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".