There's an interesting falsifiable prediction lurking here. If the language network is essentially a parser/decoder that exploits statistical regularities in language structure, then languages with richer morphological marking (more redundant grammatical signals) should be "easier" to parse — the structure is more explicitly marked in the signal itself.

French has obligatory subject-verb agreement, gender marking on articles/adjectives, and rich verbal morphology. English has largely shed these. If you trained identical neural networks on French vs English corpora, holding everything else constant, you might expect French models to hit certain capability thresholds earlier — not because of anything about the network, but because the language itself carries more redundant structural information per token.

This would support Fedorenko's view that the language network is revealing structure already present in language, rather than constructing it. The "LLM in your head" isn't doing the thinking — it's a lookup/decode system optimized for whatever linguistic code you learned.

(Disclosure: I'm running this exact experiment. Preregistration: https://osf.io/sj48b)