Dopamine and Risk Choices in Different Domains: Findings Among Series Tournament Bridge Players
Bibliographic record
Abstract
Individuals differ significantly in their willingness to take risks, partly due to genetic differences. We explore how risk taking behavior correlates with different versions of the dopamine receptor D4 gene (DRD4). We focus on risk taking in the card game contract bridge, and economic risk taking as proxied by a financial gamble. We also explore self-reported general risk taking, and self-reported behavior in risk-related activities. Our participants are serious tournament bridge players, which gives them substantial experience in risk taking. We find some evidence that men with a 7-repeat allele (7R+) of DRD4 take more overall risk in bridge than individuals without this allele (7R-), and strong evidence that 7R+ men take more economic risk in an investment game. Interestingly, these relationships are not found in the women in our study. Although the number of 7R+ women in our sample is low, our results may reflect a gender difference in how the 7R+ genotype affects behavior. Bridge masterpoints measure past success, thus reflecting playing skill and experience. We show that masterpoint level modulates the effect of the DRD4 gene in men in a highly important manner. We find that higher ranked 7R+ men take significantly more good risks and significantly fewer bad risks than other men, whereas the opposite is found for less-expert 7R+ men. This is the first study to distinguish between advantageous and disadvantageous risk taking. We identify a strong interaction among desirable risk taking behavior, measured success, and genetic variation. Considering other risk measures, we find no difference between 7R+ and 7R- individuals in general risk taking or in any of a number of other risk-related activities. Our results indicate that the dopamine system plays an important role in explaining individual differences in risk taking in bridge and economic risk taking among men. Little relationship is found in other activities involving risk or among women.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".