Resting-state functional connectivity in gelotophobes: A neuroscientific perspective on the fear of laughter
Bibliographic record
Abstract
Gelotophobia, the fear of being laughed at, is characterized by heightened sensitivity to ridicule and a tendency to perceive laughter in social situations as mocking. Resting-state functional magnetic resonance imaging (rs-fMRI) acquires brain functional connectivity while the individual remains at rest, without engaging in specific tasks. Recent studies have investigated task-based fMRI and white matter in gelotophobes; however, the resting-state functional connectivity (rsFC) in this group remains unclear. This study aimed to examine differences in rsFC between gelotophobes and non-gelotophobes, to provide insights into the neural networks underlying gelotophobia. Using a seed-based correlation approach, the present study analyzed rsFC in three key networks: the limbic system, default mode network (DMN), and executive control network (ECN). Compared to non-gelotophobes, gelotophobes exhibited significantly stronger amygdala-putamen connectivity within the limbic system, suggesting heightened sensitivity to social cues and altered processing of fear. Within the DMN, gelotophobes demonstrated stronger precuneus-temporoparietal junction (TPJ) and posterior cingulate cortex-TPJ functional connectivity, implying increased self-awareness and vigilance toward social evaluation. In the ECN, enhanced connectivity between the superior frontal gyrus and supplementary motor area in gelotophobes may reflect heightened attention to social cues. Notably, while individuals with gelotophobia exhibited greater amygdala-putamen functional connectivity, controls showed stronger amygdala-supplementary motor area connectivity. These distinct connectivity patterns across the limbic system, DMN, and ECN provide new insights into the neural basis of gelotophobia and its associated heightened sensitivity to social evaluation.
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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 | 0.000 |
| 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) | 0.001 | 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".