MIRAGE: Robust multi-modal architectures translate fMRI-to-image models from vision to mental imagery
To be useful for downstream applications, vision decoding models that are trained to reconstruct seen images from human brain activity must be able to generalize to internally generated visual representations, i.e., mental images. In an analysis of the recently released NSD-Imagery dataset, we demonstrated that while some modern vision decoders can perform quite well on mental image reconstruction, some fail, and that state-of-the-art (SOTA) performance on seen image reconstruction is no guarantee of SOTA performance on mental image reconstruction. Motivated by these findings, we developed MIRAGE, a method explicitly designed to train on vision datasets and cross-decode mental images from...
| Authors | Reese Kneeland, Cesar Kadir Torrico Villanueva, Jordyn Ojeda et al. |
|---|---|
| Primary category | q-bio.NC |
| Published | 16 May 2026 |
| Last updated at source | 16 May 2026 |
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