Culture and cannabinoid receptor gene polymorphism interact to influence the perception of happiness
Bibliographic record
Abstract
Previous studies have shown that a cytosine (C) to thymine (T) single nucleotide polymorphism (SNP) of the human cannabinoid receptor 1 (CNR1) gene is associated with positive emotional processing. C allele carriers are more sensitive to positive emotional stimuli including happiness. The effects of several gene polymorphisms related to sensitivity to emotional stimuli, such as that in the serotonin transporter gene-linked polymorphic region (5HTTLPR), on emotional processing have been reported to differ among cultures-e.g., between those that are independent and interdependent. Thus, we postulated that the effects of the CNR1 genotype on happiness might differ among different cultures because the concept of happiness varies by culture. We recruited healthy male and female young adults in Japan, where favorable external circumstances determine the concept of happiness, and Canada, where the concept of happiness centers on positive inner feelings, and compared the effects of the CNR1 genotype on both subjective happiness levels (self-evaluation as being a happy person) and situation-specific happiness (happy feelings accompanying various positive events) by using a questionnaire. We found that the effect of CNR1 on subjective happiness was different between the Japanese and Canadian groups. The subjective happiness level was the highest in Japanese individuals with the CC genotype, whereas in Canadian participants, it was the highest in individuals with the TT genotype. Furthermore, the effects of CNR1 genotype on situation-specific happiness were also different between the groups. Happiness accompanied with being surrounded by happy people was the highest among Japanese individuals with the CC genotype, whereas among Canadian individuals, it was the highest in TT genotype carriers. These findings suggest that culture and CNR1 polymorphism interact to influence the perception of happiness.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".