Rostral and dorsal anterior cingulate cortex make dissociable contributions during antisaccade error commission
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Bibliographic record
Abstract
The anterior cingulate cortex (ACC) participates in both performance optimization and evaluation, with dissociable contributions from dorsal (dACC) and rostral (rACC) regions. Deactivation in rACC and other default-mode regions is important for performance optimization, whereas increased rACC and dACC activation contributes to performance evaluation. Errors activate both rACC and dACC. We propose that this activation reflects differential error-related involvement of rACC and dACC during both performance optimization and evaluation, and that these two processes can be distinguished by the timing of their occurrence within a trial. We compared correct and error antisaccade trials. We expected errors to correlate with an early failure of rACC deactivation and increased activation of both rACC and dACC later in the trial. Eighteen healthy subjects performed a series of prosaccade and antisaccade trials during event-related functional MRI. We estimated the hemodynamic responses for error and correct antisaccades using a finite impulse-response model. We examined ACC activity by comparing error and correct antisaccades with a fixation baseline and error to correct antisaccades directly. Compared with correct antisaccades, errors were characterized by an early bilateral failure of deactivation of rACC and other default-mode regions. This difference was significant in rACC. Errors also were associated with increased activity in both rACC and dACC later in the trial. These results show that accurate performance involves deactivation of the rACC and other default mode regions and suggest that both rACC and dACC contribute to the evaluation of error responses.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it