Medial Orbitofrontal, Prefrontal, and Amygdalar Circuits Support Dissociable Component Processes of Risk/Reward Decision-Making
Bibliographic record
Abstract
The medial orbitofrontal cortex (mOFC) has been implicated in shaping decisions involving reward uncertainty, in part by using memories to infer future outcomes. This region is interconnected with other key systems that mediated these decisions, including the basolateral amygdala (BLA) and prelimbic (PL) region of the medial prefrontal cortex, yet the functional importance of these circuits remains unclear. The present study used chemogenetic silencing to examine the contribution of different input and output pathways of the mOFC to risk/reward decision-making. Male rats were well-trained on a probabilistic discounting task where they chose between a small/certain (one pellet) and a large/uncertain (four pellets) option, the odds for which changed systematically across a session. Suppressing activity of descending mOFC terminals in the BLA impaired adjustment in choice biases as reward probabilities change, suggesting this circuit tracks changes in relative value to support flexible reward-seeking. Inhibiting bottom-up BLA → mOFC circuits had no effect on choice. With respect to corticocortical circuits, inhibiting mOFC inputs to PL led to more random choice patterns, indicating this circuit promotes advantageous choice by processing context-dependent information regarding wins and losses. In comparison, PL inputs to mOFC attenuate the allure of larger yet uncertain rewards and reduce loss sensitivity, particularly early in the choice sequence. The present findings provide novel insight into the functional contribution that mOFC-BLA and PL interactions make to distinct processes that shape decision-making in situations of reward uncertainty.
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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".