Feature Visualization Recovers Known Cortical Selectivity from TRIBE v2
Brain encoder models predict cortical fMRI responses from the internal activations of pretrained vision and language networks, and are typically evaluated by held-out prediction accuracy. This is a useful signal for training but a poor one for interpretation: it tells us an encoder fits the data without telling us whether it has internalized the functional organization of the brain. We propose feature visualization -- gradient ascent on the encoder's predicted activation for a target region of interest (ROI) -- as a complementary interpretability technique, and apply it to TRIBE v2 composed with V-JEPA 2 (ViT-G, 40 layers), holding both frozen and synthesizing still images for seven regio...
| Authors | Stuart Bladon, Brinnae Bent |
|---|---|
| Primary category | q-bio.NC |
| Published | 13 May 2026 |
| Last updated at source | 13 May 2026 |
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