Similarity of computations across domains does not imply shared implementation: The case of language comprehension

Understanding language requires applying cognitive operations (e.g., memory retrieval, prediction, structure building)-relevant across many cognitive domains-to specialized knowledge structures (a particular language's phonology, lexicon, and syntax). Are these computations carried out by domain-general circuits or by circuits that store domain-specific representations? Recent work has characterized the roles in language comprehension of the language-selective network and the multiple demand (MD) network, which has been implicated in executive functions and linked to fluid intelligence, making it a prime candidate for implementing computations that support information processing across domains. The language network responds robustly to diverse aspects of comprehension, but the MD network shows no sensitivity to linguistic variables. We therefore argue that the MD network does not play a core role in language comprehension, and that past claims to the contrary are likely due to methodological artifacts. Although future studies may discover some aspects of language that require the MD network, evidence to date suggests that those will not be related to core linguistic processes like lexical access or composition. The finding that the circuits that store linguistic knowledge carry out computations on those representations aligns with general arguments against the separation between memory and computation in the mind and brain.