Impact of Commitment on Performance Evaluation in the Rostral Cingulate Motor Area
Bibliographic record
Abstract
Performance evaluation is a prerequisite for behavioral adaptation. Although the anterior cingulate cortex (ACC) is thought to play a central role in error detection, little is known about the electrophysiological activity of this structure during the performance-monitoring process. We directly addressed this issue by training monkeys to perform a Stroop-like task and then recorded neuronal activity in the rostral cingulate motor area (CMAr), a relatively unexplored region of the ACC known to be involved in motor processing. We found that most CMAr neurons responded during the evaluation period to both positive and negative feedback, but neuronal changes were more important after an error than after a successful trial. Interestingly, this performance-monitoring activity was not directly modulated by the degree of difficulty of the cognitive situation because changes in discharge frequency were similar whatever the level of attentional control imposed on the monkey. Firing activity during the evaluation period increased more, however, in erroneously completed than in incompleted trials and when the reward was delivered in an active rather than passive context, indicating that performance evaluation was conditioned by the degree of commitment of the animal to the task. It would thus seem that CMAr neurons could constitute a system for the evaluation of behavioral performance contingent on the subject's commitment to the task.
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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 | 0.001 |
| 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".