Similarity of Computations Across Domains Does Not Imply Shared Implementation: The Case of Language Comprehension
Name
nihms-1737327.pdf
Description
Accepted version
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678.67 KB
Format
Adobe PDF
Checksum (MD5)
b9759b21dc07ea7245592fbcac5bdf39
Author(s)
Fedorenko, Evelina
Shain, Cory
Date Issued
2021
Journal
Current Directions in Psychological Science
Publisher
SAGE Publications
Citation
Fedorenko, Evelina and Shain, Cory. 2021. "Similarity of Computations Across Domains Does Not Imply Shared Implementation: The Case of Language Comprehension." Current Directions in Psychological Science, 30 (6).
Version
Author's final manuscript
Abstract
Understanding language requires applying cognitive operations (e.g., memory retrieval, prediction, structure building) that are relevant across many cognitive domains to specialized knowledge structures (e.g., a particular language’s 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 network, which is selective for high-level language processing, and the multiple-demand (MD) network, which has been implicated in executive functions and linked to fluid intelligence and thus is 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 findings suggesting the contrary are likely due to methodological artifacts. Although future studies may reveal some aspects of language comprehension that require the MD network, evidence to date suggests that those will not be related to core linguistic processes such as 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 of memory and computation in the mind and brain.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1177/09637214211046955
