Schuler, W. | bioRxiv Science

bioRxiv2021

Robust effects of working memory demand during naturalistic language comprehension in language-selective cortex

A standard view of human language processing is that comprehenders build richly structured mental representations of natural language utterances, word by word, using computationally costly memory operations supported by domain-general working memory resources. However, three core claims of this view have been questioned, with some prior work arguing that (1) rich word-by-word structure building is not a core function of the language comprehension system, (2) apparent working memory costs are underlyingly driven by word predictability (surprisal), and/or (3) language comprehension relies primarily on domain-general rather than domain-specific working memory resources. In this work, we simultaneously evaluate all three of these claims using naturalistic comprehension in fMRI. In each participant, we functionally localize (a) a language-selective network and (b) a multiple-demand network that supports working memory across domains, and we analyze the responses in these two networks of interest during naturalistic story listening with respect to a range of theory-driven predictors of working memory demand under rigorous surprisal controls. Results show robust surprisal-independent effects of word-by-word memory demand in the language network and no effect of working memory demand in the multiple demand network. Our findings thus support the view that language comprehension (1) entails word-by-word structure building using (2) computationally intensive memory operations that are not explained by surprisal. However, these results challenge (3) the domain-generality of the resources that support these operations, instead indicating that working memory operations for language comprehension are carried out by the same neural resources that store linguistic knowledge. Significance StatementThis study uses fMRI to investigate signatures of working memory (WM) demand during naturalistic story listening, using a broad range of theoretically motivated estimates of WM demand. Results support a strong effect of WM demand in language-selective brain regions but no effect of WM demand in "multiple demand" regions that have previously been associated with WM in non-linguistic domains. We further show evidence that WM effects in language regions are distinct from effects of word predictability. Our findings support a core role for WM in incremental language processing, using WM resources that are specialized for language.

neuroscience↗

bioRxiv2019

fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

Much research in cognitive neuroscience supports prediction as a canonical computation of cognition across domains. Is such predictive coding implemented by feedback from higher-order domain-general circuits, or is it locally implemented in domain-specific circuits? What information sources are used to generate these predictions? This study addresses these two questions in the context of language processing. We present fMRI evidence from a naturalistic comprehension paradigm (1) that predictive coding in the brains response to language is domain-specific, and (2) that these predictions are sensitive both to local word co-occurrence patterns and to hierarchical structure. Using a recently developed continuous-time deconvolutional regression technique that supports data-driven hemodynamic response function discovery from continuous BOLD signal fluctuations in response to naturalistic stimuli, we found effects of prediction measures in the language network but not in the domain-general multiple-demand network, which supports executive control processes and has been previously implicated in language comprehension. Moreover, within the language network, surface-level and structural prediction effects were separable. The predictability effects in the language network were substantial, with the model capturing over 37% of explainable variance on held-out data. These findings indicate that human sentence processing mechanisms generate predictions about upcoming words using cognitive processes that are sensitive to hierarchical structure and specialized for language processing, rather than via feedback from high-level executive control mechanisms.

neuroscience↗