Prefrontal Cortical Contribution to Risk-Based Decision Making
Bibliographic record
Abstract
Damage to various regions of the prefrontal cortex (PFC) impairs decision making involving evaluations about risks and rewards. However, the specific contributions that different PFC subregions make to risk-based decision making are unclear. We investigated the effects of reversible inactivation of 4 subregions of the rat PFC (prelimbic medial PFC, orbitofrontal cortex [OFC], anterior cingulate, and insular cortex) on probabilistic (or risk) discounting. Rats were well trained to choose between either a "Small/Certain" lever that always delivered 1 food pellet, or another, "Large/Risky" lever, which delivered 4 pellets, but the probability of receiving reward decreased across 4 trial blocks (100%, 50%, 25%, and 12.5%). Infusions of gama-aminobutyric acid agonists muscimol/baclofen into the medial PFC increased risky choice. However, similar medial PFC inactivations decreased risky choice when the Large/Risky reward probability increased over a session. OFC inactivation increased response latencies in the latter trial blocks without affecting choice. Anterior cingulate or insular inactivations were without effect. The effects of prelimbic inactivations were not attributable to disruptions in response flexibility or judgments about the relative value of probabilistic rewards. Thus, the prelimbic, but not other PFC regions, plays a critical role in risk discounting, integrating information about changing reward probabilities to update value representations that facilitate efficient decision making.
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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".