Bibliographic record
Abstract
The objective of this study was to investigate potential sex differences in the neural response to human bodies using fMRI carried out in healthy young adults. We presented human bodies in a block-design experiment to identify body-responsive regions of the brain, namely, extrastriate body area (EBA) and fusiform body area (FBA). In a separate event-related "adaptation" experiment, carried out in the same group of subjects, we presented sets of four human bodies of varying body size and shape. Varying levels of body morphing were introduced to assess the degree of morphing required for adaptation release. Analysis of BOLD signal in the block-design experiment revealed significant Sex x Hemisphere interactions in the EBA and the FBA responses to human bodies. Only women showed greater BOLD response to bodies in the right hemisphere compared with the left hemisphere for both EBA and FBA. The BOLD response in right EBA was higher in women compared with men. In the adaptation experiment, greater right versus left hemisphere response for EBA and FBA was also identified among women but not men. These findings are particularly novel in that they address potential sex differences in the lateralization of EBA and FBA responses to human body images. Although previous studies have found some degree of right hemisphere dominance in body perception, our results suggest that such a functional lateralization may differ between men and women.
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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 | 0.002 |
| 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) | 0.003 | 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".