Tracking Progress toward a Goal in Corticostriatal Ensembles
Bibliographic record
Abstract
When performing sequences of actions, we constantly keep track of our current position in the sequence relative to the overall goal. The present study searched for neural representations of sequence progression in corticostriatal circuits. Neurons within the anterior cingulate cortex (ACC) and its target region in the dorsal striatum (DS) were recorded from simultaneously as rats performed different sequences of lever presses. We analyzed the responses of the neurons to presses occurring in the "first," "second," or "third" serial position regardless of the particular sequence or physical levers. Principal component analysis revealed that the main source of firing rate variance in the ACC was a smooth ramp-like change as the animal progressed through the sequence toward the reward. No such smooth-ramping activity was observed in DS ensembles as firing tended to be tightly linked to each action. In the ACC, the progression in firing was observed only for correct choices and not errors, whereas in the DS, firing associated with each action in a sequence was similar regardless of whether the action was correct or not. Therefore, different forms of a signal exist within corticostriatal circuits that evolve across a sequence of actions, with DS ensembles tracking every action and ACC ensembles tracking actual progress toward the goal.
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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".