Anterior cingulate error‐related activity is modulated by predicted reward
Bibliographic record
Abstract
Learning abilities depend on detection and exploitation of errors. In primates, this function involves the anterior cingulate cortex. However, whether anterior cingulate error-related activity indicates occurrence of inappropriate responses or results from other computations is debated. Here we have tested whether reward-related parameters modulate error-related activity of anterior cingulate neurons. Recordings in monkeys performing stimulus-reward associations and preliminary data obtained with a problem-solving task revealed major properties of error-related unit activity: (i) their amplitude varies with the amount of predicted reward or the proximity to reward delivery; (ii) they appear both after execution and performance errors; (iii) they do not indicate which error occurred or which correction to make; and (iv), importantly, the activity of these neurons also increases following an external signal indicating the necessity to shift response. Hence, we conclude that anterior cingulate 'error' activity might represent a negative deviation from a predicted goal, and does not only reflect error detection but signals events interrupting potentially rewarded actions.
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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) | 0.001 | 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".