Memory, Decision-Making, and the Ventromedial Prefrontal Cortex (vmPFC): The Roles of Subcallosal and Posterior Orbitofrontal Cortices in Monitoring and Control Processes
Bibliographic record
Abstract
The ventromedial prefrontal cortex (vmPFC) prominently and separately features in neurobiological models of decision-making (e.g., value-encoding) and of memory (e.g., automatic veracity-monitoring). Recent decision-making models propose value judgments that inherently comprise of second-order confidence estimates. These demonstrate quadratic relationships with first-order judgments and are automatically encoded in vmPFC activity. Memory studies use Quantity-Accuracy Profiles to capture similar first-order and second-order meta-mnemonic processes, suggesting convergence across domains. Patients with PFC damage answered general knowledge questionnaires under 2 conditions. During forced report, they chose an answer and rated the probability of it being correct (first-order "monitoring"). During free report, they could choose to volunteer or withhold their previous answers (second-order "control") to maximize performance. We found quadratic relationships between first-order and second-order meta-mnemonic processes; voxel-based lesion-symptom mapping demonstrated that vmPFC damage diminished that relationship. Furthermore, damage to subcallosal vmPFC was specifically associated with impaired monitoring and additional damage to posterior orbitofrontal cortex led to deficient control. In decision-making, these regions typically support valuation and choice, respectively. Persistent spontaneous confabulation (false memory production) confirmed the clinical relevance of these dissociations. Compared with patients with no confabulation history, patients who currently confabulate were impaired on both monitoring and control, whereas former confabulators demonstrated impaired monitoring but intact control.
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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 | 0.001 |
| 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".