arXiv Computation and Language September 4, 2026 By Elliot Murphy

No country for old linguists: LLM-brain alignment underdetermines neural computation

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Nastase et al. (2026) argue that large language models (LLMs) can shed light on language processing because both use distributed, context‑sensitive representations shaped by statistical learning, and they advocate for LLM‑brain alignment research. They reject simple cortical “boxology” but claim that representational alignment can constrain mechanistic hypotheses, though it does not itself identify a mechanism. The author critiques this position, pointing out logical, causal, and computational underdetermination and the tension between the authors’ methodological caveats and their conclusion that LLMs could serve as fully mechanistic models of language.

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