Theta-activity in anterior cingulate cortex predicts task rules and their adjustments following errors
Bibliographic record
Abstract
Accomplishing even simple tasks depend on neuronal circuits to configure how incoming sensory stimuli map onto responses. Controlling these stimulus-response (SR) mapping rules relies on a cognitive control network comprising the anterior cingulate cortex (ACC). Single neurons within the ACC convey information about currently relevant SR mapping rules and signal unexpected action outcomes, which can be used to optimize behavioral choices. However, its functional significance and the mechanistic means of interaction with other nodes of the cognitive control network remain elusive and poorly understood. Here, we report that core aspects of cognitive control are encoded by rhythmic theta-band activity within neuronal circuits in the ACC. Throughout task performance, theta-activity predicted which of two SR mapping rules will be established before processing visual target information. Task-selective theta-activity emerged particularly early during those trials, which required the adjustment of SR rules following an erroneous rule representation in the preceding trial. These findings demonstrate a functional correlation of cognitive control processes and oscillatory theta-band activity in macaque ACC. Moreover, we report that spike output of a subset of cells in ACC is synchronized to predictive theta-activity, suggesting that the theta-cycle could serve as a temporal reference for coordinating local task selective computations across a larger network of frontal areas and the hippocampus to optimize and adjust the processing routes of sensory and motor circuits to achieve efficient sensory-motor control.
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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 | 0.000 | 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".