Schema Representation in Patients with Ventromedial PFC Lesions
Bibliographic record
Abstract
Human neuroimaging and animal studies have recently implicated the ventromedial prefrontal cortex (vmPFC) in memory schema, particularly in facilitating new encoding by existing schemas. In humans, the most conspicuous memory disorder following vmPFC damage is confabulation; strategic retrieval models suggest that aberrant schema activation or reinstatement plays a role in confabulation. This raises the possibility that beyond its role in schema-supported memory encoding, the vmPFC is also implicated in schema reinstatement itself. If that is the case, vmPFC lesions should lead to impaired schema-based operations, even on tasks that do not involve memory acquisition. To test this prediction, ten patients with vmPFC damage, four with present or prior confabulation, and a group of twelve matched healthy controls made speeded yes/no decisions as to whether words were closely related to a schema (a visit to the doctor). Ten minutes later, they repeated the task for a new schema (going to bed) with some words related to the first schema included as lures. Last, they rated the degree to which stimuli were related to the second schema. All four vmPFC patients with present or prior confabulation were impaired in rejecting lures and in classifying stimulus belongingness to the schema, even when they were not lures. Nonconfabulating patients performed comparably to healthy adults with high accuracy, comparable reaction times, and similar ratings. These results show for the first time that damage to the human vmPFC, when associated with confabulation, leads to deficient schema reinstatement, which is likely a prerequisite for schema-mediated memory integration.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".