Contingent Tunes of Neurochemical Ensembles in the Norm and Pathology: Can We See the Patterns?
Bibliographic record
Abstract
BACKGROUND/AIMS: Progress in the development of DSM/ICD taxonomies has revealed limitations of both label-based and dimensionality approaches. These approaches fail to address the contingent, nonlinear, context-dependent, and transient nature of those biomarkers linked to specific symptoms of psychopathology or to specific biobehavioural traits of healthy people (temperament). The present review aims to highlight the benefits of a functional constructivism approach in the analysis of neurochemical biomarkers underlying temperament and psychopathology. METHOD: A review was performed. RESULTS: Eight systems are identified, and 7 neurochemical ensembles are described in detail. None of these systems is represented by a single neurotransmitter; all of them work in ensembles with each other. The functionality and relationships of these systems are presented here in association with their roles in action construction, with brief examples of psychopathology. The review introduces formal symbols for these systems to facilitate their more compact analysis in the future. CONCLUSION: This analysis demonstrates the possibility of constructivism-based unifying taxonomies of temperament (in the framework of the neurochemical model functional ensemble of temperament) and classifications of psychiatric disorders. Such taxonomies would present the biobehavioural individual differences as consistent behavioural patterns generated within a formally structured space of parameters related to the generation of behaviour.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".