Toward a More Sophisticated Response Representation in Theories of Medial Frontal Performance Monitoring: The Effects of Motor Similarity and Motor Asymmetries
Bibliographic record
Abstract
Cognitive control in the posterior medial frontal cortex (pMFC) is formulated in models that emphasize adaptive behavior driven by a computation evaluating the degree of difference between 2 conflicting responses. These functions are manifested by an event-related brain potential component coined the error-related negativity (ERN). We hypothesized that the ERN represents a regulative rather than evaluative pMFC process, exerted over the error motor representation, expediting the execution of a corrective response. We manipulated the motor representations of the error and the correct response to varying degrees. The ERN was greater when 1) the error response was more potent than when the correct response was more potent, 2) more errors were committed, 3) fewer and slower corrections were observed, and 4) the error response shared fewer motor features with the correct response. In their current forms, several prominent models of the pMFC cannot be reconciled with these findings. We suggest that a prepotent, unintended error is prone to reach the manual motor processor responsible for response execution before a nonpotent, intended correct response. In this case, the correct response is a correction and its execution must wait until the error is aborted. The ERN may reflect pMFC activity that aimed to suppress the error.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".