Successful choice behavior is associated with distinct and coherent network states in anterior cingulate cortex
Bibliographic record
Abstract
Successful decision making requires an ability to monitor contexts, actions, and outcomes. The anterior cingulate cortex (ACC) is thought to be critical for these functions, monitoring and guiding decisions especially in challenging situations involving conflict and errors. A number of different single-unit correlates have been observed in the ACC that reflect the diverse cognitive components involved. Yet how ACC neurons function as an integrated network is poorly understood. Here we show, using advanced population analysis of multiple single-unit recordings from the rat ACC during performance of an ecologically valid decision-making task, that ensembles of neurons move through different coherent and dissociable states as the cognitive requirements of the task change. This organization into distinct network patterns with respect to both firing-rate changes and correlations among units broke down during trials with numerous behavioral errors, especially at choice points of the task. These results point to an underlying functional organization into cell assemblies in the ACC that may monitor choices, outcomes, and task contexts, thus tracking the animal's progression through "task space."
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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) | 0.000 | 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 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".