this post was submitted on 27 Feb 2024
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Found myself a copy of the paper for a read-through and it's immediately obvious to me why they couldn't get above 90% accuracy.
The word "Gender" occurs exactly zero times in the text and the datasets they worked with were divided into a strict sex binary. As a result, the accuracy of their models' predictions could not significantly improve upon prior work in the field.
The only new info here is that their XAN is able to point out the specific brain features that influenced its predictions. Potentially useful with regards to the development of treatments for gendered brain issues in neurotypical people, but anyone who falls outside of the 90th percentile of sexually dimorphic normativity won't see any benefit here.
It's not unusual for studies like these to exclude people taking medication, or with any kind of medical condition, such as gender dysphoria, autism ,etc. It's to control as many variables as possible.
(I've personally been excluded from FMRI studies for being autistic and left handed.)
It doesn't sound like they excluded trans or genderqueer people, they just ignored their gender. Or maybe I'm reading it wrong, but?
10% seems a bit more than was predicted, but would that account for those who don’t fit the peaks for the sexual dimorphism definitions, you think?
I think so. With a more diverse dataset and fewer binary assumptions baked into the analysis I think we'd start seeing the bimodal contours of a spectrum between the masculine and feminine peaks. The graphics included in the study seem to hint at this, showing nodes of similarity with a tapering tail toward the middle of the distribution for all three sets of data they analyzed:
IIRC Simon Baron Cohen's studies on his (IMHO poorly named) extreme male brain theory of autism found that only 70% of men had "male brains".