4.7 Article

Robust Effects of Working Memory Demand during Naturalistic Language Comprehension in Language-Selective Cortex

Journal

JOURNAL OF NEUROSCIENCE
Volume 42, Issue 39, Pages 7412-7430

Publisher

SOC NEUROSCIENCE
DOI: 10.1523/JNEUROSCI.1894-21.2022

Keywords

domain specificity; fMRI; naturalistic; sentence processing; surprisal; working memory

Categories

Funding

  1. National Institutes of Health (NIH) [R00-HD-057522]
  2. National Science Foundation [1816891]
  3. Simons Center for the Social Brain at the Massachusetts Institute of Technology (MIT)
  4. NIH [R01-DC-016607, R01-DC-016950]
  5. Simons Foundation via the Simons Center for the Social Brain at MIT

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This study investigates the word-by-word working memory demand during natural language comprehension in humans and discusses the relationship between language-specialized working memory mechanisms and domain general working memory mechanisms.
To understand language, we must infer structured meanings from real-time auditory or visual signals. Researchers have long focused on word-by-word structure building in working memory as a mechanism that might enable this feat. However, some have argued that language processing does not typically involve rich word-by-word structure building, and/or that apparent working memory effects are underlyingly driven by surprisal (how predictable a word is in context). Consistent with this al-ternative, some recent behavioral studies of naturalistic language processing that control for surprisal have not shown clear working memory effects. In this fMRI study, we investigate a range of theory-driven predictors of word-by-word working memory demand during naturalistic language comprehension in humans of both sexes under rigorous surprisal controls. In addition, we address a related debate about whether the working memory mechanisms involved in language comprehension are language specialized or domain general. To do so, in each participant, we functionally localize (1) the language-selective network and (2) the multiple-demand network, which supports working memory across domains. Results show robust sur-prisal-independent effects of memory demand in the language network and no effect of memory demand in the multiple -demand network. Our findings thus support the view that language comprehension involves computationally demanding word-by-word structure building operations in working memory, in addition to any prediction-related mechanisms. Further, these memory operations appear to be primarily conducted by the same neural resources that store linguistic knowledge, with no evidence of involvement of brain regions known to support working memory across domains.

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