Complementary yet Dissociable Influences of Medial and Lateral Orbitofrontal Cortex over Cue-Guided Decisions Involving Reward Magnitude and Uncertainty
Bibliographic record
Abstract
Converging evidence suggests that orbitofrontal cortex (OFC) subregions subserve distinct roles in decision-making across a variety of tasks. Cost/benefit decisions can require an organism to choose between options based on information available in the environment (externally guided) and knowledge from experience (internally guided). Studies in humans have implicated both medial and lateral subdivisions of OFC (mOFC, lOFC) in externally and internally guided choice, yet rodent studies have primarily focused on OFC regulation of internally guided decisions. To address this gap, we examined how inactivation of these OFC subregions alters cue-guided, probabilistic decision-making using a "Blackjack" task. Male rats were required to choose between a certain, 1-pellet small reward and larger, 4-pellet reward delivered with varying probability, signaled trail-to-trial with explicit auditory stimuli indicating whether the odds of receiving the larger reward was good (50%) or poor (12.5%). Inactivation of the mOFC or lOFC induced generalized decreases or increases in large/risky choice, respectively, that were associated with opposite effects on loss (but not win) sensitivity and on rats' likelihood of making consecutive choices of the small/certain option. Inactivation of the adjacent anterior agranular insular cortex had no effect. Inactivation of either OFC subregion also disrupted cue-guided reward magnitude discrimination, where tones signal which action delivered a deterministic larger reward, but did not affect a simpler conditional discrimination involving choice between rewarded and unrewarded actions. Together these data highlight complementary yet heterogeneous roles for different OFC regions in using discriminative stimuli to guide action toward higher-value targets.
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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 | 0.001 |
| 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".