Laughter and Resiliency: A Behavioral Genetic Study of Humor Styles and Mental Toughness
Bibliographic record
Abstract
This study investigated phenotypic correlations between mental toughness and humor styles, as well as the common genetic and environmental effects underlying these correlations. Participants were 201 adult twin pairs from North America. They completed the Humor Styles Questionnaire, assessing individual differences in two positive (affiliative, self-enhancing) and two negative (aggressive, self-defeating) humor styles. They also completed the MT48, measuring individual differences in global mental toughness and its eight factors (Commitment, Control, Emotional Control, Control over Life, Confidence, Confidence in Abilities, Interpersonal Confidence, Challenge). Positive correlations were found between the positive humor styles and all of the mental toughness factors, with all but one reaching significance. Conversely, negative correlations were found between all mental toughness factors and the negative humor styles, with the mental toughness factors of Control, Emotional Control, Confidence, Confidence in Abilities, and Interpersonal Confidence exhibiting significant correlations. Subsequent behavioral genetic analyses revealed that these phenotypic correlations were primarily attributable to common genetic and common non-shared environmental factors. The implications of these findings regarding the potential effects of humor styles on wellbeing, and the possible selective use of humor by mentally tough individuals are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".