Dynamic Fluctuations in Dopamine Efflux in the Prefrontal Cortex and Nucleus Accumbens during Risk-Based Decision Making
Bibliographic record
Abstract
Mesocorticolimbic dopamine (DA) has been implicated in cost/benefit decision making about risks and rewards. The prefrontal cortex (PFC) and nucleus accumbens (NAc) are two DA terminal regions that contribute to decision making in distinct manners. However, how fluctuations of tonic DA levels may relate to different aspects of decision making remains to be determined. The present study measured DA efflux in the PFC and NAc with microdialysis in well trained rats performing a probabilistic discounting task. Selection of a small/certain option always delivered one pellet, whereas another, large/risky option yielded four pellets, with probabilities that decreased (100-12.5%) or increased (12.5-100%) across four blocks of trials. Yoked-reward groups were also included to control for reward delivery. PFC DA efflux during decision making decreased or increased over a session, corresponding to changes in large/risky reward probabilities. Similar profiles were observed from yoked-rewarded rats, suggesting that fluctuations in PFC DA reflect changes in the relative rate of reward received. NAc DA efflux also showed decreasing/increasing trends over the session during both tasks. However, DA efflux was higher during decision making on free- versus forced-choice trials and during periods of greater reward uncertainty. Moreover, changes in NAc DA closely tracked shifts in choice biases. These data reveal dynamic and dissociable fluctuations in PFC and NAc DA transmission associated with different aspects of risk-based decision making. PFC DA may signal changes in reward availability that facilitates modification of choice biases, whereas NAc DA encodes integrated signals about reward rates, uncertainty, and choice, reflecting implementation of decision policies.
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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) | 0.000 | 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".