Benchmarking Llama 3.2 in fMRI Decoding Reveals Language Prior Dominance

Jul 15, 2026

Researchers improved the Huth et al. fMRI encoding pipeline, achieving an 11% METEOR gain by expanding voxel selection and updating the proposal model. However, when using a new method that maps fMRI signals to a frozen Llama 3.2 language model, they found that decoding performance was almost entirely due to the language model's prior, as blind controls with zeroed fMRI input produced nearly identical results. This indicates that apparent decoding success may not reflect true neural decoding.

Why it matters: The study demonstrates that high-capacity language models can mask failures in fMRI decoding, emphasizing the need for rigorous blind-control evaluations in neural decoding research.

Full story at: arXiv Computation and Language ↗