Computational Properties of the Prefrontal Cortex
Bibliographic record
Abstract
The term “prefrontal” appears to have been coined in the first volume of Brain in the context of describing the curious case of Phineas Gage, inspired by the skull anatomy of fish (Girard, 1851; Anon, 1878). This initial report details how prefrontal lesions defied simple explanations of disrupted movement or sensation and alluded to more complex changes in personality, motivation, and mood. Insights from this article, combined with early lesion studies in nonhuman primates, linked prefrontal function with a loss of the “faculty of attention and intelligent observation” (Ferrier, 1878). In the 150 years since these early reports, neuroscience and artificial intelligence have exploded in knowledge and technical ability. Still, the computations that endow the prefrontal cortex with its unique abilities defy easy definition. Attempts have focused on anatomical properties, such as thalamic projections, neuromodulatory input, and laminar structure, or its apparent roles in overt behavior through neuropsychological investigation. These contributions have advanced our understanding on different fronts but still have not captured the full breadth of the prefrontal cortex's role in cognition and behavior. A unifying feature of prefrontal networks is believed to be the computations it carries out—higher-order algorithms that transform immediate sensory, memory, and internal information to perform such flexible functions as inference, planning, delayed responses, … Correspondence should be addressed to Nandakumar S. Narayanan at nandakumar-narayanan{at}uiowa.edu.
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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 | 0.004 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".