Anterior Cingulate Cortex Cells Identify Process-Specific Errors of Attentional Control Prior to Transient Prefrontal-Cingulate Inhibition
Bibliographic record
Abstract
Errors indicate the need to adjust attention for improved future performance. Detecting errors is thus a fundamental step to adjust and control attention. These functions have been associated with the dorsal anterior cingulate cortex (dACC), predicting that dACC cells should track the specific processing states giving rise to errors in order to identify which processing aspects need readjustment. Here, we tested this prediction by recording cells in the dACC and lateral prefrontal cortex (latPFC) of macaques performing an attention task that dissociated 3 processing stages. We found that, across prefrontal subareas, the dACC contained the largest cell populations encoding errors indicating (1) failures of inhibitory control of the attentional focus, (2) failures to prevent bottom-up distraction, and (3) lapses when implementing a choice. Error-locked firing in the dACC showed the earliest latencies across the PFC, emerged earlier than reward omission signals, and involved a significant proportion of putative inhibitory interneurons. Moreover, early onset error-locked response enhancement in the dACC was followed by transient prefrontal-cingulate inhibition, possibly reflecting active disengagement from task processing. These results suggest a functional specialization of the dACC to track and identify the actual processes that give rise to erroneous task outcomes, emphasizing its role to control attentional performance.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".