Grounding the neurobiology of language in first principles: The necessity of non-language-centric explanations for language comprehension

Hasson, Uri; Egidi, Giovanna; Marelli, Marco; Willems, Roel M.

doi:10.1016/j.cognition.2018.06.018

Cited by 134 publications

(101 citation statements)

References 264 publications

(329 reference statements)

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“…These results align with behavioral evidence for the role of working memory/cognitive control in language comprehension (e.g., King & Just, 1991;Gernsbacher, 1993;Waters and Caplan, 1996;Gibson, 1998;Gordon et al, 2002;Fedorenko et al, 2006Fedorenko et al, , 2007Lewis et al, 2006;Novick et al, 2009). Some have therefore proposed that domain-general executive resourcesimplemented in the MD network-support core aspects of linguistic interpretation related to lexical access, syntactic parsing, or semantic composition (Hasson et al, 2018), like inhibiting irrelevant meanings/parses (Novick et al, 2005), selecting the relevant representation from among alternatives (Thompson-Schill et al, 2002;Hirshorn & Thompson-Schill, 2006;Grindrod et al, 2008), supporting predictive coding for language processing (Strijkers et al, 2019), or keeping linguistic representations active in working memory (Moser et al, 2007).…”

Section: Introduction (699 Words)supporting

confidence: 82%

The domain-general multiple demand (MD) network does not support core aspects of language comprehension: a large-scale fMRI investigation

Diachek

Blank

Siegelman

et al. 2019

Preprint

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AbstractAside from the language-selective left-lateralized fronto-temporal network, language comprehension sometimes additionally recruits a domain-general bilateral fronto-parietal network implicated in executive functions: the multiple demand (MD) network. However, the nature of the MD network’s contributions to language comprehension remains debated. To illuminate the role of this network in language processing, we conducted a large-scale fMRI investigation using data from 30 diverse word and sentence comprehension experiments (481 unique participants, 678 scanning sessions). In line with prior findings, the MD network was active during many language tasks. Moreover, similar to the language-selective network, which is robustly lateralized to the left hemisphere, these responses were stronger in the left-hemisphere MD regions. However, in stark contrast with the language-selective network, the MD network responded more strongly (i) to lists of unconnected words than to sentences, and critically, (ii) in paradigms with an explicit task compared to passive comprehension paradigms. In fact, many passive comprehension tasks failed to elicit a response above the fixation baseline in the MD network, in contrast to strong responses in the language-selective network. In tandem, these results argue against a role for the MD network in core aspects of sentence comprehension like inhibiting irrelevant meanings or parses, keeping intermediate representations active in working memory, or predicting upcoming words or structures. These results align with recent evidence of relatively poor tracking of the linguistic signal by the MD regions during naturalistic comprehension, and instead suggest that the MD network’s engagement during language processing likely reflects effort associated with extraneous task demands.Significance StatementDomain-general executive processes, like working memory and cognitive control, have long been implicated in language comprehension, including in neuroimaging studies that have reported activation in domain-general multiple demand (MD) regions for linguistic manipulations. However, much prior evidence has come from paradigms where language interpretation is accompanied by extraneous tasks. Using a large fMRI dataset (30 experiments/481 participants/678 sessions), we demonstrate that MD regions are engaged during language comprehension in the presence of task demands, but not during passive reading/listening—conditions that strongly activate the fronto-temporal language network. These results present a fundamental challenge to proposals whereby linguistic computations, like inhibiting irrelevant meanings, keeping representations active in working memory, or predicting upcoming elements, draw on domain-general executive resources.

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Section: Introduction (699 Words)supporting

confidence: 82%

The domain-general multiple demand (MD) network does not support core aspects of language comprehension: a large-scale fMRI investigation

Diachek

Blank

Siegelman

et al. 2019

Preprint

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“…Ramus et al, 2003;Schulte-Körne and Bruder, 2010), however, mostly by utilizing unnatural, repetitive stimuli that barely resemble real-life speech. It has been argued that to truly understand the mechanisms of language processing in real-life situations, naturalistic stimuli should be used (Hasson et al, 2018). The core question of this study is whether the neural dynamics of processing natural speech are atypical in dyslexia.…”

Section: Introductionmentioning

confidence: 99%

Atypical MEG inter-subject correlation during listening to continuous natural speech in dyslexia

Thiede

Glerean

Kujala

et al. 2019

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AbstractListening to speech elicits brain activity time-locked to the speech sounds. This so-called neural entrainment to speech was found to be atypical in dyslexia, a reading impairment associated with neural speech processing deficits. We hypothesized that the brain responses of dyslexic vs. normal readers to real-life speech would be different, and thus the strength of inter-subject correlation (ISC) would differ from that of typical readers and be reflected in reading-related measures.We recorded magnetoencephalograms (MEG) of 23 dyslexic and 21 typically-reading adults during listening to ∼10 min of natural Finnish speech consisting of excerpts from radio news, a podcast, a self-recorded audiobook chapter and small talk. The amplitude envelopes of band-pass-filtered MEG source signals were correlated between subjects in a cortically-constrained source space in six frequency bands. The resulting ISCs of dyslexic and typical readers were compared with a permutation-based t-test. Neuropsychological measures of phonological processing, technical reading, and working memory were correlated with the ISCs utilizing the Mantel test.During listening to speech, ISCs were reduced in dyslexic compared to typical readers in delta (0.5–4 Hz), alpha (8–12 Hz), low gamma (25–45 Hz) and high gamma (55–90 Hz) frequency bands. In the beta (12–25 Hz) band, dyslexics had mainly enhanced ISC to speech compared to controls. Furthermore, we found that ISCs across both groups were associated with phonological processing, technical reading, and working memory.The atypical ISC to natural speech in dyslexics supports the temporal sampling deficit theory of dyslexia. It also suggests over-synchronization to phoneme-rate information in speech, which could indicate more effort-demanding sampling of phonemes from speech in dyslexia. These irregularities in parsing speech are likely some of the complex neural factors contributing to dyslexia. The associations between neural coupling and reading-related skills further support this notion.Research HighlightsMEG inter-subject correlation (ISC) of dyslexics was atypical while listening to speech.Depending on the frequency band, dyslexics had stronger or weaker ISC than controls.Reading-related measures correlated with the strength of ISC.

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“…And third, past studies that did employ neuroimaging tools with high spatial resolution and consequently reported linguistic prediction responsestypically neural response increases for violations of linguistic structurelocalized to executive control regions (e.g., Newman et al, 2001;Kuperberg et al, 2003;Nieuwland et al, 2012;Schuster et al, 2016) may have been influenced by task artifacts; indeed, some have argued that artificially constructed laboratory stimuli and tasks increase general cognitive load in comparison to naturalistic language comprehension (e.g., Blanco-Elorietta & Pylkkanen, 2017;Campbell & Tyler, 2018;Wehbe et al, submitted;Diachek et al, in prep.). To ensure that findings from the laboratory paradigms truly reflect the cognitive phenomenon of interest, it is important to validate them in more naturalistic experimental settings that better approximate the typical conditions of human sentence comprehension (Hasson & Honey, 2012;Hasson et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

Shain

Blank

Schijndel

et al. 2019

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Much research in cognitive neuroscience supports prediction as a canonical computation of cognition in many 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 brain's 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 deconvolutional time series regression technique that supports data-driven hemodynamic response function discovery from continuous BOLD signal fluctuations in response to naturalistic stimuli, we found we found effects of prediction measures in the language network but not in the domain-general, multiple-demand network. 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. 4 2016; Lopopolo et al., 2017; see Table 1 for summary), a well-established predictor of behavioral measures in naturalistic language comprehension (These previous naturalistic studies of linguistic prediction effects in the brainusing estimates of prediction effort such as surprisal, the negative log probability of a word given its context, or entropy, an information-theoretic measure of the degree of constraint placed by the context on upcoming words (Hale, 2001)have yielded mixed results on the existence, type, and functional location of such effects. For example, of the lexicalized and unlexicalized (part-of-speech) bigram and trigram models of word surprisal explored in Brennan et al. (2016), only part-of-speech bigrams positively modulated neural responses in most regions of the functionally localized language network. Lexicalized bi-and trigrams and part-of-speech trigrams yielded generally null or negative results (16 out of 18 comparisons). By contrast, Willems et al. (2015) found lexicalized trigram effects in regions typically associated with language processing (e.g., anterior and posterior temporal lobe). In addition, Willems et al. (2015) and Lopopolo et al. (2017) found prediction effects in regions that are unlikely to be specialized for language processing, including (aggregating across both studies) the brain stem, amygdala, putamen, and hippocampus, as well as in superior frontal areas more typically associated with domain-general executive functions like...

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Grounding the neurobiology of language in first principles: The necessity of non-language-centric explanations for language comprehension

Cited by 134 publications

References 264 publications

The domain-general multiple demand (MD) network does not support core aspects of language comprehension: a large-scale fMRI investigation

The domain-general multiple demand (MD) network does not support core aspects of language comprehension: a large-scale fMRI investigation

Atypical MEG inter-subject correlation during listening to continuous natural speech in dyslexia

fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

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