Cross-attention encoding models reveal dynamic spatiotemporal routing across human higher visual cortex
Understanding how the brain parses actions and events from time-varying natural inputs is a central challenge in neuroscience. Recent work has used deep neural network (DNN) models to build stimulus-computable fMRI encoding models that predict single-voxel responses to complex natural videos. However, the majority of video-computable encoding models predict responses using simple linear mappings from model tokens, overlooking the spatiotemporal structure shared by video representations and neural responses. Recent cross-attention encoding models address this limitation for static images, enabling flexible stimulus-dependent weighting of image content across space. Here, we extend this fra...
| Authors | Iishaan Inabathini, Margaret M. Henderson |
|---|---|
| Primary category | q-bio.NC |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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