Frequency-specific brain dynamics related to prediction during language comprehension

Armeni, Kristijan; Willems, Roel M.; Bosch, Antal van den; Schoffelen, Jan‐Mathijs

doi:10.1101/399733

Cited by 6 publications

(6 citation statements)

References 92 publications

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“…The WM effects shown here furthermore fail to be accounted for by multiple strong measures of word predictability, which has repeatedly been shown in prior work to describe naturalistic human sentence processing responses across modalities, including behavioral (Demberg & Keller, 2008; Frank & Bod, 2011; Fossum & Levy, 2012; Smith & Levy, 2013; van Schijndel & Schuler, 2015; Aurnhammer & Frank, 2019; Shain, 2019), electrophysiological (Frank et al, 2015; Armeni et al, 2019), and fMRI (Brennan et al, 2016; Henderson et al, 2016; Lopopolo et al, 2017; Shain, Blank, et al, 2020; Willems et al, 2015). In its strong form, surprisal theory (Hale, 2001; Levy, 2008) equates sentence comprehension with allocating activation between (potentially infinite) possible interpretations of the unfolding sentence, in proportion to their probability given the currently observed string.…”

Section: Discussionsupporting

confidence: 66%

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

Shain

Blank

Fedorenko

et al. 2021

Preprint

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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.

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Section: Discussionsupporting

confidence: 66%

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

Shain

Blank

Fedorenko

et al. 2021

Preprint

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“…In line with previous literature (Armeni, Willems, van den Bosch, & Schoffelen, 2019; Hultén, Schoffelen, Uddén, Lam, & Hagoort, 2019; Schuster et al, 2020), the word-by-word association between the MEG signals and the increasingly constrained context (i.e. index), and metrics quantifying (the results of) prediction, presented itself with different spatiotemporal dynamics.…”

Section: Discussionsupporting

confidence: 82%

Lexical frequency and sentence context influence the brain’s response to single words

Huizeling

Arana

Hagoort

et al. 2020

Preprint

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Remarkably fast reading is facilitated by brain processes that are sensitive to both word frequency and contextual constraints. It is debated as to whether these attributes have additive or interactive effects on language processing in the brain. We investigated this issue by analysing existing magnetoencephalography data from 99 participants reading sentences and word-lists. Using a cross-validated model comparison scheme, we found that lexical frequency predicted the word-by-word elicited MEG signal in a widespread cortical network, irrespective of sentential context. In contrast, index (ordinal word position) was more strongly encoded in sentence words, in left front-temporal areas. This suggests that frequency influences word processing independently of predictability, and that contextual constraints affect word-by-word brain responses. Interestingly, an exploration of the index*frequency interaction revealed an effect (in left frontal and temporal cortex) that reversed in time and space for sentences compared to word-lists. These findings may improve future neuro-cognitive models of reading.

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“…Even something as simple as adjective-noun syntax (e.g., "red boat") is constructed predictively (Berwick & Stabler 2019). Finding evidence that some feature of cognition is concerned with surprise avoidance at once suggests that such a feature will comply with active inference (Da Costa et al 2020; see also Armeni et al 2018, Di Liberto et al 2018, Keshev & Meltzer-Asscher 2021, Wang et al 2018 and much other work for evidence for predictive processing in language, supporting the idea that prediction is a "canonical computation" implemented in domain-specific circuits; Keller & Mrsic-Flogel 2018).…”

Section: Ideal and The Real: Aligning Grammatical Knowledge With Performancementioning

confidence: 83%

Natural language syntax complies with the free-energy principle

Murphy¹,

Holmes²,

Friston³

2021

Preprint

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Natural language syntax yields an unbounded array of hierarchically structured expressions. We claim that these are used in the service of active inference in accord with the free-energy principle (FEP). While conceptual advances alongside modelling and simulation work have attempted to connect speech segmentation and linguistic communication with the FEP, we extend this program to the underlying computations responsible for generating elementary syntactic objects. We argue that recently proposed principles of economy in language design—such as “minimal search” and “least effort” criteria from theoretical syntax—adhere to the FEP. This permits a greater degree of explanatory power to the FEP—with respect to higher language functions—and presents linguists with a grounding in first principles of notions pertaining to computability. More generally, we explore the possibility of migrating certain topics in linguistics over to the domain of fields that investigate the FEP, such as complex polysemy. We aim to align concerns of linguists with the normative model for organic self-organisation associated with the FEP, marshalling evidence from theoretical linguistics and psycholinguistics to ground core principles of efficient syntactic computation within active inference.

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Frequency-specific brain dynamics related to prediction during language comprehension

Cited by 6 publications

References 92 publications

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

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

Lexical frequency and sentence context influence the brain’s response to single words

Natural language syntax complies with the free-energy principle

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