Bibliographic record
Abstract
Executive function allows us to interact with the world in a purposive, goal-directed manner. It relies on several cognitive control operations that are mediated by different regions of the prefrontal cortex. While much of our knowledge about the functional subdivisions of the prefrontal cortex comes from the systematic assessment of patients with brain damage, animal models have served as the predominant tool for investigating specific structure-function relationships within the prefrontal cortex, especially as they relate to complex executive behaviors. These studies generally involve the targeted disruption of neural circuits combined with behavioral testing using carefully designed cognitive paradigms. In this review, I will describe a broad range of such experiments conducted in rats and monkeys that together reveal the distinct contributions of dorsal, medial, and ventral prefrontal cortex to different aspects of executive function. The effects of lesions and local pharmacological manipulations have provided valuable insights into the neural underpinnings of executive function and its neurochemical modulation. Despite the challenges associated with establishing a precise homology between animal models of prefrontal function and the human brain, such models currently offer the best means to systematically investigate the cognitive building blocks of executive function. This helps define the neural circuits that lead to a range of neurological and psychiatric disorders and facilitate the development of effective therapeutic strategies to ameliorate the associated cognitive impairments.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".