Multifaceted Contributions by Different Regions of the Orbitofrontal and Medial Prefrontal Cortex to Probabilistic Reversal Learning
Bibliographic record
Abstract
Different subregions of the prefrontal cortex (PFC) contribute to the ability to respond flexibly to changes in reward contingencies, with the medial versus orbitofrontal cortex (OFC) subregions contributing differentially to processes such as set-shifting and reversal learning. To date, the manner in which these regions may facilitate reversal learning in situations involving reward uncertainty remains relatively unexplored. We investigated the involvement of five distinct regions of the rat OFC (lateral and medial) and medial PFC (prelimbic, infralimbic, and anterior cingulate) on probabilistic reversal learning wherein "correct" versus "incorrect" responses were rewarded on 80% and 20% of trials, respectively. Contingencies were reversed repeatedly within a session. In well trained rats, inactivation of the medial or lateral OFC induced dissociable impairments in performance (indexed by fewer reversals completed) when outcomes were probabilistic, but not when they were assured. Medial OFC inactivation impaired probabilistic learning during the first discrimination, increased perseverative responding and reduced sensitivity to positive and negative feedback, suggestive of a deficit in incorporating information about previous action outcomes to guide subsequent behavior. Lateral OFC inactivation preferentially impaired performance during reversal phases. In contrast, prelimbic inactivation caused an apparent improvement in performance by increasing the number of reversals completed. This was associated with enhanced sensitivity to recently rewarded actions and reduced sensitivity to negative feedback. Infralimbic inactivation had no effect, whereas the anterior cingulate appeared to play a permissive role in this form of reversal learning. These results clarify the dissociable contributions of different regions of the frontal lobes to probabilistic learning. SIGNIFICANCE STATEMENT: The ability to adjust behavior in response to changes involving uncertain or probabilistic reward contingencies is an essential survival skill that is impaired in a variety of psychiatric disorders. It is well established that different forms of cognitive flexibility are mediated by anatomically distinct regions of the frontal lobes when reinforcement contingencies are assured, however, less is known about the contribution of these regions to probabilistic reinforcement learning. Here we show that different regions of the orbitofrontal and medial prefrontal cortex make distinct contributions to probabilistic reversal learning. These findings provide novel information about the complex interplay between frontal lobe regions in mediating these processes and accordingly provide insight into possible pathophysiology that underlies impairments in cognitive flexibility observed in mental illnesses.
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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".