The effect of raising and lowering tryptophan levels on human mood and social behaviour
Bibliographic record
Abstract
Acute tryptophan depletion (ATD) studies indicate that low serotonin can lower mood and also increase aggression, although results vary somewhat between studies with similar participants. Lowering of mood after ATD is related to the susceptibility of the study participants to clinical depression, and some participants show no effect on mood. This indicates that low serotonin can contribute to lowered mood, but cannot-by itself-cause lowered mood, unless other unknown systems interact with serotonin to lower mood. Studies using tryptophan supplementation demonstrate that increased serotonin can decrease quarrelsomeness and increase agreeableness in everyday life. Social interactions that are more agreeable and less quarrelsome are associated with better mood. Thus, serotonin may have direct effects on mood, but may also be able to influence mood through changes in social behaviour. The increased agreeableness and decreased quarrelsomeness resulting from increases in serotonin will help foster congenial relations with others and should help to increase social support. As social support and social isolation have an important relationship with both physical and mental health, more research is needed on the implications of the ability of serotonin to modulate social behaviour for the regulation of mood, and for future physical and mental health.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".