Performance Monitoring in the Anterior Cingulate is Not All Error Related: Expectancy Deviation and the Representation of Action-Outcome Associations
Bibliographic record
Abstract
Several converging lines of evidence suggest that the anterior cingulate cortex (ACC) is selectively involved in error detection or evaluation of poor performance. Here we challenge this notion by presenting event-related potential (ERP) evidence that the feedback-elicited error-related negativity, an ERP component attributed to the ACC, can be elicited by positive feedback when a person is expecting negative feedback and vice versa. These results suggest that performance monitoring in the ACC is not limited to error processing. We propose that the ACC acts as part of a more general performance-monitoring system that is activated by violations in expectancy. Further, we propose that the common observation of increased ACC activity elicited by negative events could be explained by an overoptimistic bias in generating expectations of performance. These results could shed light into neurobehavioral disorders, such as depression and mania, associated with alterations in performance monitoring and also in judgments of self-related events.
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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 | 0.003 |
| 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 | 0.001 | 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".