Why predicting brain activity isn't enough to understand AI vision

Researchers developed a framework that goes beyond prediction accuracy to identify which specific dimensions of brain activity a model actually captures. Using fMRI data from eight subjects viewing images, they show that early visual cortex activity has a low-dimensional structure, and that even models with identical prediction scores can recover different aspects of this structure. This matters because it reveals that current AI-brain comparisons may be fundamentally misleading.