How Do Emotional Restrictions Affect the Use of Humor? A Behavior Genetic Analysis of Alexithymia and Humor Styles
Bibliographic record
Abstract
This article reports the first behavioral genetic study of relationships between alexithymia and four styles of humor: affiliative, self-enhancing, self-defeating, and aggressive. A total of 509 MZ pairs and 264 DZ pairs of twins completed the Toronto Alexithymia Scale-20 (TAS-20) and the Humor Styles Questionnaire (HSQ). Consistent with our predictions, alexithymia correlated negatively with affiliative and self-enhancing humor and positively with self-defeating and aggressive humor. All but one of the 16 phenotypic correlations that we report are significant at the 0.01 level. Also consistent with our predictions, the phenotypic correlations between alexithymia and humor styles were primarily attributable to correlated genetic factors and to a lesser extent to correlated non-shared environmental factors. Correlated shared environmental factors had no significant effect. Implications and limitations of this study 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 | 0.000 |
| 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".