scholarly journals High-level language brain regions are sensitive to sub-lexical regularities

2021 ◽  
Author(s):  
Tamar I Regev ◽  
Josef Affourtit ◽  
Xuanyi Chen ◽  
Abigail E Schipper ◽  
Leon Bergen ◽  
...  
Keyword(s):  
Language Processing ◽  
Sentence Comprehension ◽  
Brain Regions ◽  
Linguistic Knowledge ◽  
High Level Language ◽  
Word Meanings ◽  
Sound Patterns ◽  
Core Language ◽  
High Level ◽  
Language Network

A network of left frontal and temporal brain regions supports 'high-level' language processing-including the processing of word meanings, as well as word-combinatorial processing-across presentation modalities. This 'core' language network has been argued to store our knowledge of words and constructions as well as constraints on how those combine to form sentences. However, our linguistic knowledge additionally includes information about sounds (phonemes) and how they combine to form clusters, syllables, and words. Is this knowledge of phoneme combinatorics also represented in these language regions? Across five fMRI experiments, we investigated the sensitivity of high-level language processing brain regions to sub-lexical linguistic sound patterns by examining responses to diverse nonwords-sequences of sounds/letters that do not constitute real words (e.g., punes, silory, flope). We establish robust responses in the language network to visually (Experiment 1a, n=605) and auditorily (Experiments 1b, n=12, and 1c, n=13) presented nonwords relative to baseline. In Experiment 2 (n=16), we find stronger responses to nonwords that obey the phoneme-combinatorial constraints of English. Finally, in Experiment 3 (n=14) and a post-hoc analysis of Experiment 2, we provide suggestive evidence that the responses in Experiments 1 and 2 are not due to the activation of real words that share some phonology with the nonwords. The results suggest that knowledge of phoneme combinatorics and representations of sub-lexical linguistic sound patterns are stored within the same fronto-temporal network that stores higher-level linguistic knowledge and supports word and sentence comprehension.

scholarly journals Lack of selectivity for syntax relative to word meanings throughout the language network

2018 ◽  
Author(s):  
Evelina Fedorenko ◽  
Idan Blank ◽  
Matthew Siegelman ◽  
Zachary Mineroff
Keyword(s):  
Semantic Processing ◽  
Sentence Comprehension ◽  
Syntactic Structure ◽  
Brain Regions ◽  
Linguistic Knowledge ◽  
Opposite Pattern ◽  
Word Meanings ◽  
Neural Architecture ◽  
Temporal Language ◽  
Language Network

AbstractTo understand what you are reading now, your mind retrieves the meanings of words and constructions from a linguistic knowledge store (lexico-semantic processing) and identifies the relationships among them to construct a complex meaning (syntactic or combinatorial processing). Do these two sets of processes rely on distinct, specialized mechanisms or, rather, share a common pool of resources? Linguistic theorizing, empirical evidence from language acquisition and processing, and computational modeling have jointly painted a picture whereby lexico-semantic and syntactic processing are deeply inter-connected and perhaps not separable. In contrast, many current proposals of the neural architecture of language continue to endorse a view whereby certain brain regions selectively support syntactic/combinatorial processing, although the locus of such “syntactic hub”, and its nature, vary across proposals. Here, we searched for selectivity for syntactic over lexico-semantic processing using a powerful individual-subjects fMRI approach across three sentence comprehension paradigms that have been used in prior work to argue for such selectivity: responses to lexico-semanticvs.morpho-syntactic violations (Experiment 1); recovery from neural suppression across pairs of sentences differing in only lexical itemsvs.only syntactic structure (Experiment 2); and same/different meaning judgments on such sentence pairs (Experiment 3). Across experiments, both lexico-semantic and syntactic conditions elicited robust responses throughout the left fronto-temporal language network. Critically, however, no regions were more strongly engaged by syntactic than lexico-semantic processing, although some regions showed the opposite pattern. Thus, contra many current proposals of the neural architecture of language, syntactic/combinatorial processing is not separable from lexico-semantic processing at the level of brain regions—or even voxel subsets—within the language network, in line with strong integration between these two processes that has been consistently observed in behavioral and computational language research. The results further suggest that the language network may be generally more strongly concerned with meaning than syntactic form, in line with the primary function of language—to share meanings across minds.

Similarity of Computations Across Domains Does Not Imply Shared Implementation: The Case of Language Comprehension

2021 ◽  
Vol 30 (6) ◽  
pp. 526-534
Author(s):  
Evelina Fedorenko ◽  
Cory Shain
Keyword(s):  
Language Processing ◽  
Language Comprehension ◽  
Fluid Intelligence ◽  
Linguistic Knowledge ◽  
Domain Specific ◽  
Cognitive Operations ◽  
High Level ◽  
General Circuits ◽  
The Mind ◽  
Language Network

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.

scholarly journals High-level language processing regions are not engaged in action observation or imitation

2018 ◽  
Vol 120 (5) ◽  
pp. 2555-2570 ◽  
Author(s):  
Brianna L. Pritchett ◽  
Caitlyn Hoeflin ◽  
Kami Koldewyn ◽  
Eyal Dechter ◽  
Evelina Fedorenko
Keyword(s):  
Language Processing ◽  
Language Comprehension ◽  
Music Perception ◽  
Action Observation ◽  
Inferior Frontal Gyrus ◽  
Brain Regions ◽  
Angular Gyrus ◽  
High Level Language ◽  
Left Frontal Lobe ◽  
High Level

A set of left frontal, temporal, and parietal brain regions respond robustly during language comprehension and production (e.g., Fedorenko E, Hsieh PJ, Nieto-Castañón A, Whitfield-Gabrieli S, Kanwisher N. J Neurophysiol 104: 1177–1194, 2010; Menenti L, Gierhan SM, Segaert K, Hagoort P. Psychol Sci 22: 1173–1182, 2011). These regions have been further shown to be selective for language relative to other cognitive processes, including arithmetic, aspects of executive function, and music perception (e.g., Fedorenko E, Behr MK, Kanwisher N. Proc Natl Acad Sci USA 108: 16428–16433, 2011; Monti MM, Osherson DN. Brain Res 1428: 33–42, 2012). However, one claim about overlap between language and nonlinguistic cognition remains prominent. In particular, some have argued that language processing shares computational demands with action observation and/or execution (e.g., Rizzolatti G, Arbib MA. Trends Neurosci 21: 188–194, 1998; Koechlin E, Jubault T. Neuron 50: 963–974, 2006; Tettamanti M, Weniger D. Cortex 42: 491–494, 2006). However, the evidence for these claims is indirect, based on observing activation for language and action tasks within the same broad anatomical areas (e.g., on the lateral surface of the left frontal lobe). To test whether language indeed shares machinery with action observation/execution, we examined the responses of language brain regions, defined functionally in each individual participant (Fedorenko E, Hsieh PJ, Nieto-Castañón A, Whitfield-Gabrieli S, Kanwisher N. J Neurophysiol 104: 1177–1194, 2010) to action observation ( experiments 1, 2, and 3a) and action imitation ( experiment 3b). With the exception of the language region in the angular gyrus, all language regions, including those in the inferior frontal gyrus (within “Broca’s area”), showed little or no response during action observation/imitation. These results add to the growing body of literature suggesting that high-level language regions are highly selective for language processing (see Fedorenko E, Varley R. Ann NY Acad Sci 1369: 132–153, 2016 for a review). NEW & NOTEWORTHY Many have argued for overlap in the machinery used to interpret language and others’ actions, either because action observation was a precursor to linguistic communication or because both require interpreting hierarchically-structured stimuli. However, existing evidence is indirect, relying on group analyses or reverse inference. We examined responses to action observation in language regions defined functionally in individual participants and found no response. Thus language comprehension and action observation recruit distinct circuits in the modern brain.

scholarly journals fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

2019 ◽  
Author(s):  
Cory Shain ◽  
Idan Asher Blank ◽  
Marten van Schijndel ◽  
William Schuler ◽  
Evelina Fedorenko
Keyword(s):  
Hierarchical Structure ◽  
Language Processing ◽  
Sentence Processing ◽  
Executive Control ◽  
Sentence Comprehension ◽  
Predictive Coding ◽  
Control Mechanisms ◽  
Hemodynamic Response Function ◽  
Domain Specific ◽  
Language Network

AbstractMuch 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 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 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.

scholarly journals A functional dissociation between language and multiple-demand systems revealed in patterns of BOLD signal fluctuations

2014 ◽  
Vol 112 (5) ◽  
pp. 1105-1118 ◽  
Author(s):  
Idan Blank ◽  
Nancy Kanwisher ◽  
Evelina Fedorenko
Keyword(s):  
Cognitive Processing ◽  
Language Processing ◽  
Cognitive Functions ◽  
Blood Oxygen Level Dependent ◽  
Brain Regions ◽  
Story Comprehension ◽  
Blood Oxygen Level ◽  
Two Systems ◽  
Dependent Signal ◽  
High Level

What is the relationship between language and other high-level cognitive functions? Neuroimaging studies have begun to illuminate this question, revealing that some brain regions are quite selectively engaged during language processing, whereas other “multiple-demand” (MD) regions are broadly engaged by diverse cognitive tasks. Nonetheless, the functional dissociation between the language and MD systems remains controversial. Here, we tackle this question with a synergistic combination of functional MRI methods: we first define candidate language-specific and MD regions in each subject individually (using functional localizers) and then measure blood oxygen level-dependent signal fluctuations in these regions during two naturalistic conditions (“rest” and story-comprehension). In both conditions, signal fluctuations strongly correlate among language regions as well as among MD regions, but correlations across systems are weak or negative. Moreover, data-driven clustering analyses based on these inter-region correlations consistently recover two clusters corresponding to the language and MD systems. Thus although each system forms an internally integrated whole, the two systems dissociate sharply from each other. This independent recruitment of the language and MD systems during cognitive processing is consistent with the hypothesis that these two systems support distinct cognitive functions.

scholarly journals The Functional Reorganization of Language Network Modules in Glioma Patients: New Insights From Resting State fMRI Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Lu Jin ◽  
Chuzhong Li ◽  
Yazhuo Zhang ◽  
Taoyang Yuan ◽  
Jianyou Ying ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Language Processing ◽  
Right Hemisphere ◽  
Preoperative Evaluation ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Distinct Pattern ◽  
Language Functions ◽  
Language Network

BackgroundPrior investigations of language functions have focused on the response profiles of particular brain regions. However, the specialized and static view of language processing does not explain numerous observations of functional recovery following brain surgery. To investigate the dynamic alterations of functional connectivity (FC) within language network (LN) in glioma patients, we explored a new flexible model based on the neuroscientific hypothesis of core-periphery organization in LN.MethodsGroup-level LN mapping was determined from 109 glioma patients and forty-two healthy controls (HCs) using independent component analysis (ICA). FC and mean network connectivity (mNC: l/rFCw, FCb, and FCg) were compared between patients and HCs. Correlations between mNC and tumor volume (TV) were calculated.ResultsWe identified ten separate LN modules from ICA. Compared to HCs, glioma patients showed a significant reduction in language network functional connectivity (LNFC), with a distinct pattern modulated by tumor position. Left hemisphere gliomas had a broader impact on FC than right hemisphere gliomas, with more reduced edges away from tumor sites (p=0.011). mNC analysis revealed a significant reduction in all indicators of FC except for lFCw in right hemisphere gliomas. These alterations were associated with TV in a double correlative relationship depending on the tumor position across hemispheres.ConclusionOur findings emphasize the importance of considering the modulatory effects of core-periphery mechanisms from a network perspective. Preoperative evaluation of changes in LN caused by gliomas could provide the surgeon a reference to optimize resection while maintaining functional balance.

Ontogeny of the Neural Language Network

2017 ◽  
Author(s):  
Angela D. Friederici ◽  
Noam Chomsky
Keyword(s):  
Language Acquisition ◽  
Language Processing ◽  
Inferior Frontal Gyrus ◽  
Temporal Cortex ◽  
Superior Temporal Gyrus ◽  
Brain Structures ◽  
Relevant Information ◽  
Brain Regions ◽  
Language Performance ◽  
Language Network

This chapter reviews the neural underpinning of normal language acquisition and asks not only at which age certain milestones in language acquisition are achieved, but moreover to what extent is this achievement dependent on the maturation of particular brain structures. In our recent model, the neural basis of the developing language system is described to reflect two major phases. The available data provide consistent evidence that very early on an infant is able to extract language-relevant information from the acoustic input. This first phase covers the first three years of life when language processing is largely input-driven and supported by the temporal cortex and the ventral part of the network. A second phase extends beyond age 3, when top-down processes come into play, and the left inferior frontal cortex and the dorsal part of the language network are recruited to a larger extent. Development towards full language performance beyond age 3 is dependent on maturational changes in the gray and white matter. An increased language ability is correlated with an increase in structural and functional connectivity between language-related brain regions in the left hemisphere, the inferior frontal gyrus and the posterior superior temporal gyrus/superior temporal sulcus.

Dissociation between the Language Network and the Multiple Demand (MD) Network, and Their Functional Roles in Language

2020 ◽  
Author(s):  
Weixi Kang ◽  
Sònia Pineda Hernández ◽  
Afshin Azadikhah
Keyword(s):  
Large Scale ◽  
Brain Regions ◽  
Functional Roles ◽  
Structural And Functional Properties ◽  
Language Recovery ◽  
Core Language ◽  
Large Scale Networks ◽  
Network Studies ◽  
Parietal Cortices ◽  
Language Network

Language operations rely on multiple mental activities that are supported by the frontal, temporal, and parietal cortices. Moreover, these cortices are not organized into individual isolated previous but rather consist of multiple large-scale networks: sets of brain regions that share structural and functional properties. Literature generally agrees with two well documented systems in the human brain, which are the language network, and the multiple demand (MD) network. Studies have reported both the participation of language network (e.g., Fedorenko et al., 2010, Vagharchakian et al., 2012; Fedorenko et al., 2016; Scott et al., 2017; Deniz et al., 2019) and MD network in language (e.g., Kuperberg et al., 2003; Rodd et al., 2005, Novais-Santos et al., 2007; January et al., 2009; Peelle et al., 2010; Nieuwland et al., 2012; McMillan et al., 2013), but their possible role in language is debated. In this paper, we review how the language-specific and domain-general MD systems support different aspects of cognition and how they can be dissociated from one another. We argue that core language operations are supported by the language network rather than the MD network, and the MD network does not contribute directly to language recovery after stroke, but plays a role in the recovery of other cognitive functions that are engaged in the same language task.

scholarly journals The Critical Role of Semantic Working Memory in Language Comprehension and Production

2021 ◽  
pp. 096372142199517
Author(s):  
Randi C. Martin
Keyword(s):  
Working Memory ◽  
Language Processing ◽  
Sentence Comprehension ◽  
Semantic Information ◽  
Critical Role ◽  
Verbal Working Memory ◽  
Word Meanings ◽  
Activated State ◽  
Limited Scope

Although research on the role of verbal working memory (WM) in language processing has focused on phonological maintenance, considerable evidence indicates that the maintenance of semantic information plays a more critical role. This article reviews studies of brain-damaged and healthy individuals demonstrating the contribution of semantic WM to language processing. On the sentence-comprehension side, semantic WM supports the retention of individual word meanings prior to their integration. It also serves to maintain semantic information in an activated state such that semantic interference between sentence constituents can be resolved. Phonological WM does not appear to support either of these functions, though it contributes to verbatim sentence recall. On the production side, evidence points to the phrase as the minimal scope of advance planning in sentence formulation, and to semantic WM as supporting the representation of the meanings of the content words within a phrase. Planning at the phonological level appears to have a very limited scope, making few demands on phonological WM. These findings imply that treatment of semantic but not phonological WM deficits should lead to improved sentence comprehension and production, and preliminary findings support that view.

scholarly journals The universal language network: A cross-linguistic investigation spanning 45 languages and 11 language families.

2021 ◽  
Author(s):  
Dima Ayyash ◽  
Saima Malik-Moraleda ◽  
Jeanne Gallee ◽  
Josef Affourtit ◽  
Malte Hoffman ◽  
...  
Keyword(s):  
Language Processing ◽  
Cognitive Neuroscience ◽  
Large Scale ◽  
Functional Integration ◽  
Brain Regions ◽  
European Languages ◽  
Universal Language ◽  
Neuroscience Research ◽  
Temporal Language ◽  
Language Network

To understand the architecture of human language, it is critical to examine diverse languages; yet most cognitive neuroscience research has focused on a handful of primarily Indo-European languages. Here, we report a large-scale investigation of the fronto-temporal language network across 45 languages and establish the cross-linguistic generality of its key functional properties, including general topography, left-lateralization, strong functional integration among its brain regions, and functional selectivity for language processing. 

Sign in / Sign up

Export Citation Format

Share Document