Selective Involvement by the Medial Orbitofrontal Cortex in Biasing Risky, But Not Impulsive, Choice
Bibliographic record
Abstract
Separate regions of the orbitofrontal cortex (OFC) have been implicated in mediating different aspects of cost-benefit decision-making in humans and animals. Anatomical and functional imaging studies indicate that the medial (mOFC) and lateral OFC may subserve dissociable functions related to reward and decision-making processes, yet the majority of studies in rodents have focused on the lateral OFC. The present study investigated the contribution of the rat mOFC to risk and delay-based decision-making, assessed with probabilistic and delay-discounting tasks. In well-trained rats, reversible inactivation of the mOFC increase a risky choice on the probabilistic discounting task, irrespective of whether the odds of obtaining a larger/risky reward decreased (100-12.5%) or increased (12.5-100%) over the course of a session. The increase in risky choice was associated with enhanced win-stay behavior, wherein rats showed an increased tendency to choose the risky option after being rewarded for the risky choice on a preceding trial. In contrast, mOFC inactivation did not alter delay discounting. These findings suggest that the mOFC plays a selective role in decisions involving reward uncertainty, mitigating the impact that larger, probabilistic rewards exert on subsequent choice behavior. This function may promote the exploration of novel options when reward contingencies change.
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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".