AINeutralarXiv – CS AI · 8h ago6/10
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The Variance Brain Foundation Models Forgot: Third-Order Statistics Predict Cognition Where Billion-Parameter Models Fail
Researchers demonstrate that brain foundation models (BFMs)—billion-parameter Transformers trained on fMRI data—paradoxically predict cognitive performance worse than simple linear regression on functional connectivity matrices. The study identifies a variance allocation problem where BFM pretraining captures dominant fMRI variance but destroys higher-order statistical structures (third-order co-skewness) that actually predict cognition, solved through a lightweight linear pipeline requiring no pretraining.