Dissociable Contributions by Prefrontal D1 and D2 Receptors to Risk-Based Decision Making
Bibliographic record
Abstract
Choices between certain and uncertain rewards of different magnitudes have been proposed to be mediated by both the frontal lobes and the mesocorticolimbic dopamine (DA) system. In rats, systemic manipulations of DA activity or inactivation of the medial prefrontal cortex (PFC) disrupt decision making about risks and rewards. However, it is unclear how PFC DA transmission contributes to these processes. We addressed this issue by examining the effects of pharmacological manipulations of D(1) and D(2) receptors in the medial (prelimbic) PFC on choice between small, certain and large, yet probabilistic rewards. Rats were trained on a probabilistic discounting task where one lever delivered one pellet with 100% probability, and the other delivered four pellets, but the probability of receiving reward decreased across blocks of trials (100, 50, 25, 12.5%). D(1) blockade (SCH23390) in the medial PFC decreased preference for the large/risky option. In contrast, D(2) blockade (eticlopride) reduced probabilistic discounting and increased risky choice. The D(1) agonist SKF81297 caused a slight, nonsignificant increase in preference for the large/risky lever. However, D(2) receptor stimulation (quinpirole) induced a true impairment in decision making, flattening the discounting curve and biasing choice away from or toward the risky option when it was more or less advantageous, respectively. These findings suggest that PFC D(1) and D(2) receptors make dissociable, yet complementary, contributions to risk/reward judgments. By striking a fine balance between D(1)/D(2) receptor activity, DA may help refine these judgments, promoting either exploitation of current favorable circumstances or exploration of more profitable ones when conditions change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".