Similarity of Computations Across Domains Does Not Imply Shared Implementation: The Case of Language Comprehension
<jats:p> 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...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publications
2023
|
| Online Access: | https://hdl.handle.net/1721.1/148765 |
| _version_ | 1826191358804623360 |
|---|---|
| author | Fedorenko, Evelina Shain, Cory |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Fedorenko, Evelina Shain, Cory |
| author_sort | Fedorenko, Evelina |
| collection | MIT |
| description | <jats:p> 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. </jats:p> |
| first_indexed | 2024-09-23T08:54:42Z |
| format | Article |
| id | mit-1721.1/148765 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:54:42Z |
| publishDate | 2023 |
| publisher | SAGE Publications |
| record_format | dspace |
| spelling | mit-1721.1/1487652023-03-28T03:46:20Z Similarity of Computations Across Domains Does Not Imply Shared Implementation: The Case of Language Comprehension Fedorenko, Evelina Shain, Cory Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences <jats:p> 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. </jats:p> 2023-03-27T13:01:07Z 2023-03-27T13:01:07Z 2021 2023-03-27T12:55:22Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/148765 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). en 10.1177/09637214211046955 Current Directions in Psychological Science Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf SAGE Publications PMC |
| spellingShingle | Fedorenko, Evelina Shain, Cory Similarity of Computations Across Domains Does Not Imply Shared Implementation: The Case of Language Comprehension |
| title | Similarity of Computations Across Domains Does Not Imply Shared Implementation: The Case of Language Comprehension |
| title_full | Similarity of Computations Across Domains Does Not Imply Shared Implementation: The Case of Language Comprehension |
| title_fullStr | Similarity of Computations Across Domains Does Not Imply Shared Implementation: The Case of Language Comprehension |
| title_full_unstemmed | Similarity of Computations Across Domains Does Not Imply Shared Implementation: The Case of Language Comprehension |
| title_short | Similarity of Computations Across Domains Does Not Imply Shared Implementation: The Case of Language Comprehension |
| title_sort | similarity of computations across domains does not imply shared implementation the case of language comprehension |
| url | https://hdl.handle.net/1721.1/148765 |
| work_keys_str_mv | AT fedorenkoevelina similarityofcomputationsacrossdomainsdoesnotimplysharedimplementationthecaseoflanguagecomprehension AT shaincory similarityofcomputationsacrossdomainsdoesnotimplysharedimplementationthecaseoflanguagecomprehension |