Winning and Losing: An Evolutionary Approach to Mood Disorders and Their Therapy
Bibliographic record
Abstract
OBJECTIVE: To advance a new evolutionary model that examines the effects of winning and losing on mood and physiological variables. Previous studies have focused on the involuntary defeat strategy in de-escalating conflict. Here, we propose that there also exists an involuntary winning strategy (IWS) that is triggered by success and characterized by euphoria and increased self-confidence. It motivates efforts to challenge, and promotes reconciliation. METHOD: Previous studies are presented, including data on student athletes, demonstrating the impact of winning and losing on mood. RESULTS: Winning is consistently shown to be related to physiological changes such as increased testosterone and serotonin levels in primates. It reliably leads to mood changes that serve to motivate winners to continue their competitive efforts. CONCLUSION: When the IWS functions optimally, success leads to success in an adaptive cycle. Over time, the initial differences between the winners and losers of agonistic encounters become magnified in a process known as difference amplification. As a result of assortative mating, the children of people who have entered into an adaptive cycle will inherit traits from both parents that will, in turn, give them an increased competitive advantage. In this manner, difference amplification could have accelerated human evolution by natural selection. Vignettes of clinical interventions are also used to illustrate therapeutic strategies designed to disrupt maladaptive cycles and promote adaptive 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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".