Sex differences in resting-state fMRI functional connectivity related to humor styles
Bibliographic record
Abstract
Research on how functional connectivity (FC) during resting-state relates to humor styles and sex is limited. This study aimed to address this knowledge gap by analyzing resting-state fMRI data from 56 healthy participants and measuring FC. In addition, participants completed the Humor Styles Questionnaire. We found distinct FC patterns linked to humor styles that differed by sex. Men demonstrated stronger FC between the anterior cingulate cortex (ACC) and the right anterior insula (rAI), right inferior frontal gyrus (rIFG) and right frontal pole (rFP), and between the right rostral prefrontal cortex (rRPFC) and rIFG. These patterns were associated with aggressive and self-enhancing humor. Conversely, women exhibited stronger FC between rRPFC and the posterior cingulate gyrus (PCC), left rostral prefrontal cortex (lRPFC) and right thalamus, correlating with self-defeating and less aggressive humor. These findings suggest a neurobiological basis for sex differences in humor, indicating that men's FC between the salience network (SN), particularly in cognitive monitoring regions, may be linked to aggressive humor. Their FC between the executive control network (ECN) and between the SN and ECN are related to self-enhancing humor and reflect an emphasis on executive function. Conversely, women's FC between the SN and default mode network is correlated with self-defeating humor, suggesting a stronger focus on self-reflection and social relationships.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.007 | 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".