Accumbal cholinergic interneurons regulate decision making or motor impulsivity depending on latent task state
Bibliographic record
Abstract
Abstract Dopaminergic transmission within the nucleus accumbens is broadly implicated in risk/reward decision making and impulse control, and the rat gambling task (rGT) measures both behaviours concurrently. While the resulting indices of risky choice and impulsivity correlate at the population level, dopaminergic manipulations rarely impact both behaviours uniformly, with changes in choice more likely when dopaminergic transmission is altered during task acquisition. Although the task structure of the rGT remains constant, the importance of accumbal dopamine signals relevant for reward prediction versus impulse control may vary over time; the former should dominate while learning which option maximises sugar pellet profits, while the suppression of premature responses becomes more valuable once a decision-making strategy is set and can be exploited. Cholinergic interneurons (CINs) critically control dopamine release within the striatum, and can also encode latent task states deciphered by the frontal cortex. We theorised that aCINs may set the dopaminergic tone of the accumbens to maximise reward learning or impulse control during task acquisition or performance, respectively. Using chemogenetics, we found some support for this hypothesis: activation and inhibition of aCINs once behaviour was stable increased and decreased motor impulsivity in both sexes but had no effect on choice patterns. In contrast, activating and inhibiting aCINs throughout task acquisition did not alter motor impulsivity, but decreased and increased risky choice respectively. However, the former effect was only seen in males and the latter in females. We conclude by proposing a set of testable predictions regarding interactions between acetylcholine and dopamine that could explain these sex differences.
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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 | 0.000 |
| 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) | 0.001 | 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".