Cross-lingual brain-language model alignment is robust but challenges hierarchical and computational accounts
Brain-language model alignment is often interpreted as evidence that transformer models implement computations similar to those of the human brain. This assumes that neural predictivity reflects internal computational properties of large language models (LLMs), such as hierarchical contextual processing, predictive coding, or representational compression. An alternative possibility is that brain scores primarily reflect stable lexical-semantic correspondences shared by language models and the brain. Here we tested these interpretations using whole-brain encoding models across Mandarin, English, and French. Across all three languages, transformer representations significantly predicted act...
| Authors | Ni Yang, Rui He, Philipp Homan et al. |
|---|---|
| Primary category | cs.CL |
| Published | 20 May 2026 |
| Last updated at source | 10 September 2026 |
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