Dual Cytoarchitectonic Trends: An Evolutionary Model of Frontal Lobe Functioning and Its Application to Psychopathology
Bibliographic record
Abstract
OBJECTIVE: To introduce and discuss an evolutionary model of frontal lobe functioning (the dual cytoarchitectonic trends theory [DTT]) and its application to understanding the neurobiology of schizophrenia and anxiety disorders. METHOD: An introduction to the DTT with respect to neural architecture, connectivity, and function is presented. In addition, neurobiologic, neuropathologic, clinical, and cognitive research supporting the application of this model to schizophrenia and anxiety disorders is reviewed. RESULTS: Traditional neuropsychologic models of acquired brain damage have been limited in their ability to explain frontal lobe dysfunction and its consequences in relation to psychopathology. The DTT offers an appropriately general neural-systems framework that may be better able to account for the diversity of symptoms, widespread neuropathology, and developmental abnormalities that are associated with most forms of psychopathology. CONCLUSIONS: Research investigating the neurobiology of psychopathology would benefit from adopting models of brain dysfunction that are consistent with neurodevelopmental pathology and evolution. Such efforts would likely lead to a greater understanding of neurobiologic mechanisms and, ultimately, better treatment strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".