Emotionality vs. Other Biobehavioural Traits: A Look at Neurochemical Biomarkers for Their Differentiation
Bibliographic record
Abstract
This review highlights the differential contributions of multiple neurochemical systems to temperament traits related and those that are unrelated to emotionality, even though these systems have a significant overlap. The difference in neurochemical biomarkers of these traits is analysed from the perspective of the neurochemical model, Functional Ensemble of Temperament (FET) that uses multi-marker and constructivism principles. Special attention is given to a differential contribution of hypothalamic-pituitary hormones and opioid neuropeptides implicated in both emotional and non-emotional regulation. The review highlights the role of the mu-opioid receptor system in dispositional emotional valence and the role of the kappa-opioid system in dispositional perceptual and behavioural alertness. These opioid receptor (OR) systems, microbiota and cytokines are produced in three neuroanatomically distinct complexes in the brain and the body, which all together integrate dispositional emotionality. In contrast, hormones could be seen as neurochemical biomarkers of non-emotional aspects of behavioural regulation related to the construction of behaviour in fast-changing and current situations. As examples of the role of hormones, the review summarised their contribution to temperament traits of Sensation Seeking (SS) and Empathy (EMP), which FET considers as non-emotionality traits related to behavioural orientation. SS is presented here as based on (higher) testosterone (fluctuating), adrenaline and (low) cortisol systems, and EMP, as based on (higher) oxytocin, reciprocally coupled with vasopressin and (lower) testosterone. Due to the involvement of gonadal hormones, there are sex and age differences in these traits that could be explained by evolutionary theory. There are, therefore, specific neurochemical biomarkers differentiating (OR-based) dispositional emotionality and (hormones-based) body's regulation in fast-changing events. Here we propose to consider dispositional emotionality associated with OR systems as emotionality in a true sense, whereas to consider hormonal ensembles regulating SS and EMP as systems of behavioural orientation and not emotionality.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".