Bibliographic record
Abstract
Introduction Temperament and mental illness are considered to be variations along the same continuum of imbalance in the neurophysiological regulation of behaviour. Objectives This presentation presents the benefits of constructivism approach to psychiatric taxonomies. Methods The presentation reviews findings in neurochemistry that link temperament traits in healthy individuals and symptoms of psychiatric disorders to complex relationships between neurotransmitter systems. Results Specialization between neurotransmitter systems underlying temperament traits is analyzed here from a functional ecology perspective that considers the structure of adult temperament corresponding to the functional structure of human activities. In contrast to a more popular search for neuroanatomic biomarkers of psychopathology and temperament traits in healthy individuals, this presentation focuses on neurochemistry-based biomarkers. The roles of monoamine neurotransmitters (serotonin, dopamine, noradrenalin), as well as the roles of acetylcholine, neuropeptides and opioid receptor systems in the regulation of specific dynamical properties of behaviour are summarized within the neurochemical Functional Ensemble of Temperament (FET) model (Table 1) (Trofimova & Robbins, Neurosci Biobehav Rev, 2016, 64, 382-402; Trofimova, Neuropsychobiology, 2021, 80(2), 101-133). Image 2: Conclusions The FET framework allows having a neurochemistry-based structure of a taxonomy that can classify both, healthy bio-psychological traits and symptoms of psychopathology. The presentation will give examples of how the FET framework can be used in psychiatry and clinical psychology. Disclosure of Interest None Declared
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".