A temporal bottleneck in the language comprehension network

Engagement of the speech motor system in challenging speech perception: Activation likelihood estimation meta-analyses

The relationship between speech production and perception is a topic of ongoing debate. Some argue that there is little interaction between the two, while others claim they share representations and processes. One perspective suggests increased recruitment of the speech motor system in demanding listening situations to facilitate perception. However, uncertainties persist regarding the specific regions involved and the listening conditions influencing its engagement. This study used activation likelihood estimation in coordinate-based meta-analyses to investigate the neural overlap between speech production and three speech perception conditions: speech-in-noise, spectrally degraded speech and linguistically complex speech. Neural overlap was observed in the left frontal, insular and temporal regions. Key nodes included the left frontal operculum (FOC), left posterior lateral part of the inferior frontal gyrus (IFG), left planum temporale (PT), and left pre-supplementary motor area (pre-SMA). The left IFG activation was consistently observed during linguistic processing, suggesting sensitivity to the linguistic content of speech. In comparison, the left pre-SMA activation was observed when processing degraded and noisy signals, indicating sensitivity to signal quality. Activations of the left PT and FOC activation were noted in all conditions, with the posterior FOC area overlapping in all conditions. Our meta-analysis reveals context-independent (FOC, PT) and context-dependent (pre-SMA, posterior lateral IFG) regions within the speech motor system during challenging speech perception. These regions could contribute to sensorimotor integration and executive cognitive control for perception and production.

The Language Network Reliably "Tracks" Naturalistic Meaningful Nonverbal Stimuli

The language network, comprised of brain regions in the left frontal and temporal cortex, responds robustly and reliably during language comprehension but shows little or no response during many nonlinguistic cognitive tasks (e.g., Fedorenko & Blank, 2020). However, one domain whose relationship with language remains debated is semantics-our conceptual knowledge of the world. Given that the language network responds strongly to meaningful linguistic stimuli, could some of this response be driven by the presence of rich conceptual representations encoded in linguistic inputs? In this study, we used a naturalistic cognition paradigm to test whether the cognitive and neural resources that are responsible for language processing are also recruited for processing semantically rich nonverbal stimuli. To do so, we measured BOLD responses to a set of ∼5-minute-long video and audio clips that consisted of meaningful event sequences but did not contain any linguistic content. We then used the intersubject correlation (ISC) approach (Hasson et al., 2004) to examine the extent to which the language network "tracks" these stimuli, that is, exhibits stimulus-related variation. Across all the regions of the language network, meaningful nonverbal stimuli elicited reliable ISCs. These ISCs were higher than the ISCs elicited by semantically impoverished nonverbal stimuli (e.g., a music clip), but substantially lower than the ISCs elicited by linguistic stimuli. Our results complement earlier findings from controlled experiments (e.g., Ivanova et al., 2021) in providing further evidence that the language network shows some sensitivity to semantic content in nonverbal stimuli.

Language is primarily a tool for communication rather than thought

Language is a defining characteristic of our species, but the function, or functions, that it serves has been debated for centuries. Here we bring recent evidence from neuroscience and allied disciplines to argue that in modern humans, language is a tool for communication, contrary to a prominent view that we use language for thinking. We begin by introducing the brain network that supports linguistic ability in humans. We then review evidence for a double dissociation between language and thought, and discuss several properties of language that suggest that it is optimized for communication. We conclude that although the emergence of language has unquestionably transformed human culture, language does not appear to be a prerequisite for complex thought, including symbolic thought. Instead, language is a powerful tool for the transmission of cultural knowledge; it plausibly co-evolved with our thinking and reasoning capacities, and only reflects, rather than gives rise to, the signature sophistication of human cognition.

The language network as a natural kind within the broader landscape of the human brain

Language behaviour is complex, but neuroscientific evidence disentangles it into distinct components supported by dedicated brain areas or networks. In this Review, we describe the 'core' language network, which includes left-hemisphere frontal and temporal areas, and show that it is strongly interconnected, independent of input and output modalities, causally important for language and language-selective. We discuss evidence that this language network plausibly stores language knowledge and supports core linguistic computations related to accessing words and constructions from memory and combining them to interpret (decode) or generate (encode) linguistic messages. We emphasize that the language network works closely with, but is distinct from, both lower-level - perceptual and motor - mechanisms and higher-level systems of knowledge and reasoning. The perceptual and motor mechanisms process linguistic signals, but, in contrast to the language network, are sensitive only to these signals' surface properties, not their meanings; the systems of knowledge and reasoning (such as the system that supports social reasoning) are sometimes engaged during language use but are not language-selective. This Review lays a foundation both for in-depth investigations of these different components of the language processing pipeline and for probing inter-component interactions.

A neuro-cognitive model of comprehension based on prediction and unification

Most architectures and models of language processing have been built upon a restricted view of language, which is limited to sentence processing. These approaches fail to capture one primordial characteristic: efficiency. Many facilitation effects are known to be at play in natural situations such as conversation (shallow processing, no real access to the lexicon, etc.) without any impact on the comprehension. In this study, on the basis of a new model integrating into a unique architecture, we present these facilitation effects for accessing the meaning into the classical compositional architecture. This model relies on two mechanisms, prediction and unification, and provides a unique architecture for the description of language processing in its natural environment.

Tracking the neural codes for words and phrases during semantic composition, working-memory storage, and retrieval

The ability to compose successive words into a meaningful phrase is a characteristic feature of human cognition, yet its neural mechanisms remain incompletely understood. Here, we analyze the cortical mechanisms of semantic composition using magnetoencephalography (MEG) while participants read one-word, two-word, and five-word noun phrases and compared them with a subsequent image. Decoding of MEG signals revealed three processing stages. During phrase comprehension, the representation of individual words was sustained for a variable duration depending on phrasal context. During the delay period, the word code was replaced by a working-memory code whose activation increased with semantic complexity. Finally, the speed and accuracy of retrieval depended on semantic complexity and was faster for surface than for deep semantic properties. In conclusion, we propose that the brain initially encodes phrases using factorized dimensions for successive words but later compresses them in working memory and requires a period of decompression to access them.

High-level language brain regions process sublexical regularities

A network of left frontal and temporal brain regions supports language processing. This "core" language network stores 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 phonemes and how they combine to form phonemic clusters, syllables, and words. Are phoneme combinatorics also represented in these language regions? Across five functional magnetic resonance imaging experiments, we investigated the sensitivity of high-level language processing brain regions to sublexical linguistic regularities by examining responses to diverse nonwords-sequences of phonemes 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. In experiment 2 (n = 16), we find stronger responses to nonwords that are more well-formed, i.e. obey the phoneme-combinatorial constraints of English. Finally, in experiment 3 (n = 14), 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 sublexical regularities are stored and processed within the same fronto-temporal network that supports lexical and syntactic processes.

Spatiotemporally distributed frontotemporal networks for sentence reading

Reading a sentence entails integrating the meanings of individual words to infer more complex, higher-order meaning. This highly rapid and complex human behavior is known to engage the inferior frontal gyrus (IFG) and middle temporal gyrus (MTG) in the language-dominant hemisphere, yet whether there are distinct contributions of these regions to sentence reading is still unclear. To probe these neural spatiotemporal dynamics, we used direct intracranial recordings to measure neural activity while reading sentences, meaning-deficient Jabberwocky sentences, and lists of words or pseudowords. We isolated two functionally and spatiotemporally distinct frontotemporal networks, each sensitive to distinct aspects of word and sentence composition. The first distributed network engages the IFG and MTG, with IFG activity preceding MTG. Activity in this network ramps up over the duration of a sentence and is reduced or absent during Jabberwocky and word lists, implying its role in the derivation of sentence-level meaning. The second network engages the superior temporal gyrus and the IFG, with temporal responses leading those in frontal lobe, and shows greater activation for each word in a list than those in sentences, suggesting that sentential context enables greater efficiency in the lexical and/or phonological processing of individual words. These adjacent, yet spatiotemporally dissociable neural mechanisms for word- and sentence-level processes shed light on the richly layered semantic networks that enable us to fluently read. These results imply distributed, dynamic computation across the frontotemporal language network rather than a clear dichotomy between the contributions of frontal and temporal structures.

Precision fMRI reveals that the language-selective network supports both phrase-structure building and lexical access during language production

A fronto-temporal brain network has long been implicated in language comprehension. However, this network's role in language production remains debated. In particular, it remains unclear whether all or only some language regions contribute to production, and which aspects of production these regions support. Across 3 functional magnetic resonance imaging experiments that rely on robust individual-subject analyses, we characterize the language network's response to high-level production demands. We report 3 novel results. First, sentence production, spoken or typed, elicits a strong response throughout the language network. Second, the language network responds to both phrase-structure building and lexical access demands, although the response to phrase-structure building is stronger and more spatially extensive, present in every language region. Finally, contra some proposals, we find no evidence of brain regions-within or outside the language network-that selectively support phrase-structure building in production relative to comprehension. Instead, all language regions respond more strongly during production than comprehension, suggesting that production incurs a greater cost for the language network. Together, these results align with the idea that language comprehension and production draw on the same knowledge representations, which are stored in a distributed manner within the language-selective network and are used to both interpret and generate linguistic utterances.

The channel capacity of multilevel linguistic features constrains speech comprehension

Humans are expert at processing speech but how this feat is accomplished remains a major question in cognitive neuroscience. Capitalizing on the concept of channel capacity, we developed a unified measurement framework to investigate the respective influence of seven acoustic and linguistic features on speech comprehension, encompassing acoustic, sub-lexical, lexical and supra-lexical levels of description. We show that comprehension is independently impacted by all these features, but at varying degrees and with a clear dominance of the syllabic rate. Comparing comprehension of French words and sentences further reveals that when supra-lexical contextual information is present, the impact of all other features is dramatically reduced. Finally, we estimated the channel capacity associated with each linguistic feature and compared them with their generic distribution in natural speech. Our data reveal that while acoustic modulation, syllabic and phonemic rates unfold respectively at 5, 5, and 12 Hz in natural speech, they are associated with independent processing bottlenecks whose channel capacity are of 15, 15 and 35 Hz, respectively, as suggested by neurophysiological theories. They moreover point towards supra-lexical contextual information as the feature limiting the flow of natural speech. Overall, this study reveals how multilevel linguistic features constrain speech comprehension.

Intact reading ability despite lacking a canonical visual word form area in an individual born without the left superior temporal lobe

The visual word form area (VWFA), a region canonically located within left ventral temporal cortex (VTC), is specialized for orthography in literate adults presumbly due to its connectivity with frontotemporal language regions. But is a typical, left-lateralized language network critical for the VWFA's emergence? We investigated this question in an individual (EG) born without the left superior temporal lobe but who has normal reading ability. EG showed canonical typical face-selectivity bilateraly but no wordselectivity either in right VWFA or in the spared left VWFA. Moreover, in contrast with the idea that the VWFA is simply part of the language network, no part of EG's VTC showed selectivity to higher-level linguistic processing. Interestingly, EG's VWFA showed reliable multivariate patterns that distinguished words from other categories. These results suggest that a typical left-hemisphere language network is necessary for acanonical VWFA, and that orthographic processing can otherwise be supported by a distributed neural code.

The Stroop effect involves an excitatory-inhibitory fronto-cerebellar loop

The Stroop effect is a classical, well-known behavioral phenomenon in humans that refers to robust interference between language and color information. It remains unclear, however, when the interference occurs and how it is resolved in the brain. Here we show that the Stroop effect occurs during perception of color-word stimuli and involves a cross-hemispheric, excitatory-inhibitory loop functionally connecting the lateral prefrontal cortex and cerebellum. Participants performed a Stroop task and a non-verbal control task (which we term the Swimmy task), and made a response vocally or manually. The Stroop effect involved the lateral prefrontal cortex in the left hemisphere and the cerebellum in the right hemisphere, independently of the response type; such lateralization was absent during the Swimmy task, however. Moreover, the prefrontal cortex amplified cerebellar activity, whereas the cerebellum suppressed prefrontal activity. This fronto-cerebellar loop may implement language and cognitive systems that enable goal-directed behavior during perceptual conflicts.

Graph theoretical analysis reveals the functional role of the left ventral occipito-temporal cortex in speech processing

The left ventral occipito-temporal cortex (left-vOT) plays a key role in reading. Interestingly, the area also responds to speech input, suggesting that it may have other functions beyond written word recognition. Here, we adopt graph theoretical analysis to investigate the left-vOT's functional role in the whole-brain network while participants process spoken sentences in different contexts. Overall, different connectivity measures indicate that the left-vOT acts as an interface enabling the communication between distributed brain regions and sub-networks. During simple speech perception, the left-vOT is systematically part of the visual network and contributes to the communication between neighboring areas, remote areas, and sub-networks, by acting as a local bridge, a global bridge, and a connector, respectively. However, when speech comprehension is explicitly required, the specific functional role of the area and the sub-network to which the left-vOT belongs change and vary with the quality of speech signal and task difficulty. These connectivity patterns provide insightful information on the contribution of the left-vOT in various contexts of language processing beyond its role in reading. They advance our general understanding of the neural mechanisms underlying the flexibility of the language network that adjusts itself according to the processing context.

Hemispheric dissociations in regions supporting auditory sentence comprehension in older adults

We investigated how the aging brain copes with acoustic and syntactic challenges during spoken language comprehension. Thirty-eight healthy adults aged 54 - 80 years (M = 66 years) participated in an fMRI experiment wherein listeners indicated the gender of an agent in short spoken sentences that varied in syntactic complexity (object-relative vs subject-relative center-embedded clause structures) and acoustic richness (high vs low spectral detail, but all intelligible). We found widespread activity throughout a bilateral frontotemporal network during successful sentence comprehension. Consistent with prior reports, bilateral inferior frontal gyrus and left posterior superior temporal gyrus were more active in response to object-relative sentences than to subject-relative sentences. Moreover, several regions were significantly correlated with individual differences in task performance: Activity in right frontoparietal cortex and left cerebellum (Crus I & II) showed a negative correlation with overall comprehension. By contrast, left frontotemporal areas and right cerebellum (Lobule VII) showed a negative correlation with accuracy specifically for syntactically complex sentences. In addition, laterality analyses confirmed a lack of hemispheric lateralization in activity evoked by sentence stimuli in older adults. Importantly, we found different hemispheric roles, with a left-lateralized core language network supporting syntactic operations, and right-hemisphere regions coming into play to aid in general cognitive demands during spoken sentence processing. Together our findings support the view that high levels of language comprehension in older adults are maintained by a close interplay between a core left hemisphere language network and additional neural resources in the contralateral hemisphere.

EEG and fMRI coupling and decoupling based on joint independent component analysis (jICA)

BACKGROUND

Meaningful integration of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) requires knowing whether these measurements reflect the activity of the same neural sources, i.e., estimating the degree of coupling and decoupling between the neuroimaging modalities.

NEW METHOD

This paper proposes a method to quantify the coupling and decoupling of fMRI and EEG signals based on the mixing matrix produced by joint independent component analysis (jICA). The method is termed fMRI/EEG-jICA.

RESULTS

fMRI and EEG acquired during a syllable detection task with variable syllable presentation rates (0.25-3 Hz) were separated with jICA into two spatiotemporally distinct components, a primary component that increased nonlinearly in amplitude with syllable presentation rate, putatively reflecting an obligatory auditory response, and a secondary component that declined nonlinearly with syllable presentation rate, putatively reflecting an auditory attention orienting response. The two EEG subcomponents were of similar amplitude, but the secondary fMRI subcomponent was ten folds smaller than the primary one.

COMPARISON TO EXISTING METHOD

FMRI multiple regression analysis yielded a map more consistent with the primary than secondary fMRI subcomponent of jICA, as determined by a greater area under the curve (0.5 versus 0.38) in a sensitivity and specificity analysis of spatial overlap.

CONCLUSION

fMRI/EEG-jICA revealed spatiotemporally distinct brain networks with greater sensitivity than fMRI multiple regression analysis, demonstrating how this method can be used for leveraging EEG signals to inform the detection and functional characterization of fMRI signals. fMRI/EEG-jICA may be useful for studying neurovascular coupling at a macro-level, e.g., in neurovascular disorders.

Incremental Language Comprehension Difficulty Predicts Activity in the Language Network but Not the Multiple Demand Network

What role do domain-general executive functions play in human language comprehension? To address this question, we examine the relationship between behavioral measures of comprehension and neural activity in the domain-general "multiple demand" (MD) network, which has been linked to constructs like attention, working memory, inhibitory control, and selection, and implicated in diverse goal-directed behaviors. Specifically, functional magnetic resonance imaging data collected during naturalistic story listening are compared with theory-neutral measures of online comprehension difficulty and incremental processing load (reading times and eye-fixation durations). Critically, to ensure that variance in these measures is driven by features of the linguistic stimulus rather than reflecting participant- or trial-level variability, the neuroimaging and behavioral datasets were collected in nonoverlapping samples. We find no behavioral-neural link in functionally localized MD regions; instead, this link is found in the domain-specific, fronto-temporal "core language network," in both left-hemispheric areas and their right hemispheric homotopic areas. These results argue against strong involvement of domain-general executive circuits in language comprehension.

Incremental Language Comprehension Difficulty Predicts Activity in the Language Network but Not the Multiple Demand Network

What role do domain-general executive functions play in human language comprehension? To address this question, we examine the relationship between behavioral measures of comprehension and neural activity in the domain-general "multiple demand" (MD) network, which has been linked to constructs like attention, working memory, inhibitory control, and selection, and implicated in diverse goal-directed behaviors. Specifically, functional magnetic resonance imaging data collected during naturalistic story listening are compared with theory-neutral measures of online comprehension difficulty and incremental processing load (reading times and eye-fixation durations). Critically, to ensure that variance in these measures is driven by features of the linguistic stimulus rather than reflecting participant- or trial-level variability, the neuroimaging and behavioral datasets were collected in nonoverlapping samples. We find no behavioral-neural link in functionally localized MD regions; instead, this link is found in the domain-specific, fronto-temporal "core language network," in both left-hemispheric areas and their right hemispheric homotopic areas. These results argue against strong involvement of domain-general executive circuits in language comprehension.

Continuous-time deconvolutional regression for psycholinguistic modeling

The influence of stimuli in psycholinguistic experiments diffuses across time because the human response to language is not instantaneous. The linear models typically used to analyze psycholinguistic data are unable to account for this phenomenon due to strong temporal independence assumptions, while existing deconvolutional methods for estimating diffuse temporal structure model time discretely and therefore cannot be directly applied to natural language stimuli where events (words) have variable duration. In light of evidence that continuous-time deconvolutional regression (CDR) can address these issues (Shain & Schuler, 2018), this article motivates the use of CDR for many experimental settings, exposits some of its mathematical properties, and empirically evaluates the influence of various experimental confounds (noise, multicollinearity, and impulse response misspecification), hyperparameter settings, and response types (behavioral and fMRI). Results show that CDR (1) yields highly consistent estimates across a variety of hyperparameter configurations, (2) faithfully recovers the data-generating model on synthetic data, even under adverse training conditions, and (3) outperforms widely-used statistical approaches when applied to naturalistic reading and fMRI data. In addition, procedures for testing scientific hypotheses using CDR are defined and demonstrated, and empirically-motivated best-practices for CDR modeling are proposed. Results support the use of CDR for analyzing psycholinguistic time series, especially in a naturalistic experimental paradigm.

Neural integration underlying naturalistic prediction flexibly adapts to varying sensory input rate

Prediction of future sensory input based on past sensory information is essential for organisms to effectively adapt their behavior in dynamic environments. Humans successfully predict future stimuli in various natural settings. Yet, it remains elusive how the brain achieves effective prediction despite enormous variations in sensory input rate, which directly affect how fast sensory information can accumulate. We presented participants with acoustic sequences capturing temporal statistical regularities prevalent in nature and investigated neural mechanisms underlying predictive computation using MEG. By parametrically manipulating sequence presentation speed, we tested two hypotheses: neural prediction relies on integrating past sensory information over fixed time periods or fixed amounts of information. We demonstrate that across halved and doubled presentation speeds, predictive information in neural activity stems from integration over fixed amounts of information. Our findings reveal the neural mechanisms enabling humans to robustly predict dynamic stimuli in natural environments despite large sensory input rate variations.

Extracting representations of cognition across neuroimaging studies improves brain decoding

Cognitive brain imaging is accumulating datasets about the neural substrate of many different mental processes. Yet, most studies are based on few subjects and have low statistical power. Analyzing data across studies could bring more statistical power; yet the current brain-imaging analytic framework cannot be used at scale as it requires casting all cognitive tasks in a unified theoretical framework. We introduce a new methodology to analyze brain responses across tasks without a joint model of the psychological processes. The method boosts statistical power in small studies with specific cognitive focus by analyzing them jointly with large studies that probe less focal mental processes. Our approach improves decoding performance for 80% of 35 widely-different functional-imaging studies. It finds commonalities across tasks in a data-driven way, via common brain representations that predict mental processes. These are brain networks tuned to psychological manipulations. They outline interpretable and plausible brain structures. The extracted networks have been made available; they can be readily reused in new neuro-imaging studies. We provide a multi-study decoding tool to adapt to new data.

Brain activity predicts future learning success in intensive second language listening training

This study explores neural mechanisms underlying how prior knowledge gained from pre-listening transcript reading helps comprehend fast-rate speech in a second language (L2) and applies to L2 learning. Top-down predictive processing by prior knowledge may play an important role in L2 speech comprehension and improving listening skill. By manipulating the pre-listening transcript effect (pre-listening transcript reading [TR] vs. no transcript reading [NTR]) and type of languages (first language (L1) vs. L2), we measured brain activity in L2 learners, who performed fast-rate listening comprehension tasks during functional magnetic resonance imaging. Thereafter, we examined whether TR_L2-specific brain activity can predict individual learning success after an intensive listening training. The left angular and superior temporal gyri were key areas responsible for integrating prior knowledge to sensory input. Activity in these areas correlated significantly with gain scores on subsequent training, indicating that brain activity related to prior knowledge-sensory input integration predicts future learning success.

No evidence for differences among language regions in their temporal receptive windows

The "core language network" consists of left frontal and temporal regions that are selectively engaged in linguistic processing. Whereas functional differences among these regions have long been debated, many accounts propose distinctions in terms of representational grain-size-e.g., words vs. phrases/sentences-or processing time-scale, i.e., operating on local linguistic features vs. larger spans of input. Indeed, the topography of language regions appears to overlap with a cortical hierarchy reported by Lerner et al. (2011) wherein mid-posterior temporal regions are sensitive to low-level features of speech, surrounding areas-to word-level information, and inferior frontal areas-to sentence-level information and beyond. However, the correspondence between the language network and this hierarchy of "temporal receptive windows" (TRWs) is difficult to establish because the precise anatomical locations of language regions vary across individuals. To directly test this correspondence, we first identified language regions in each participant with a well-validated task-based localizer, which confers high functional resolution to the study of TRWs (traditionally based on stereotactic coordinates); then, we characterized regional TRWs with the naturalistic story listening paradigm of Lerner et al. (2011), which augments task-based characterizations of the language network by more closely resembling comprehension "in the wild". We find no region-by-TRW interactions across temporal and inferior frontal regions, which are all sensitive to both word-level and sentence-level information. Therefore, the language network as a whole constitutes a unique stage of information integration within a broader cortical hierarchy.

Syntax-Sensitive Regions of the Posterior Inferior Frontal Gyrus and the Posterior Temporal Lobe Are Differentially Recruited by Production and Perception

Matchin and Hickok (2020) proposed that the left posterior inferior frontal gyrus (PIFG) and the left posterior temporal lobe (PTL) both play a role in syntactic processing, broadly construed, attributing distinct functions to these regions with respect to production and perception. Consistent with this hypothesis, functional dissociations between these regions have been demonstrated with respect to lesion-symptom mapping in aphasia. However, neuroimaging studies of syntactic comprehension typically show similar activations in these regions. In order to identify whether these regions show distinct activation patterns with respect to syntactic perception and production, we performed an fMRI study contrasting the subvocal articulation and perception of structured jabberwocky phrases (syntactic), sequences of real words (lexical), and sequences of pseudowords (phonological). We defined two sets of language-selective regions of interest (ROIs) in individual subjects for the PIFG and the PTL using the contrasts [syntactic > lexical] and [syntactic > phonological]. We found robust significant interactions of comprehension and production between these 2 regions at the syntactic level, for both sets of language-selective ROIs. This suggests a core difference in the function of these regions with respect to production and perception, consistent with the lesion literature.

The Domain-General Multiple Demand (MD) Network Does Not Support Core Aspects of Language Comprehension: A Large-Scale fMRI Investigation

Aside from the language-selective left-lateralized frontotemporal network, language comprehension sometimes recruits a domain-general bilateral frontoparietal 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 in humans, we conducted a large-scale fMRI investigation using data from 30 diverse word and sentence comprehension experiments (481 unique participants [female and male], 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 contrast with the language-selective network, the MD network responded more strongly (1) to lists of unconnected words than to sentences, and (2) in paradigms with an explicit task compared with passive comprehension paradigms. Indeed, 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. Together, these results argue against a role for the MD network in core aspects of sentence comprehension, such as 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 reflects effort associated with extraneous task demands.SIGNIFICANCE STATEMENT Domain-general executive processes, such as 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 frontotemporal language network. These results present a fundamental challenge to proposals whereby linguistic computations, such as inhibiting irrelevant meanings, keeping representations active in working memory, or predicting upcoming elements, draw on domain-general executive resources.

Broca's Area Is Not a Natural Kind

Theories of human cognition prominently feature 'Broca's area', which causally contributes to a myriad of mental functions. However, Broca's area is not a monolithic, multipurpose unit - it is structurally and functionally heterogeneous. Some functions engaging (subsets of) this area share neurocognitive resources, whereas others rely on separable circuits. A decade of converging evidence has now illuminated a fundamental distinction between two subregions of Broca's area that likely play computationally distinct roles in cognition: one belongs to the domain-specific 'language network', the other to the domain-general 'multiple-demand (MD) network'. Claims about Broca's area should be (re)cast in terms of these (and other, as yet undetermined) functional components, to establish a cumulative research enterprise where empirical findings can be replicated and theoretical proposals can be meaningfully compared and falsified.

Composition is the Core Driver of the Language-selective Network

The frontotemporal language network responds robustly and selectively to sentences. But the features of linguistic input that drive this response and the computations that these language areas support remain debated. Two key features of sentences are typically confounded in natural linguistic input: words in sentences (a) are semantically and syntactically combinable into phrase- and clause-level meanings, and (b) occur in an order licensed by the language's grammar. Inspired by recent psycholinguistic work establishing that language processing is robust to word order violations, we hypothesized that the core linguistic computation is composition, and, thus, can take place even when the word order violates the grammatical constraints of the language. This hypothesis predicts that a linguistic string should elicit a sentence-level response in the language network provided that the words in that string can enter into dependency relationships as in typical sentences. We tested this prediction across two fMRI experiments (total N = 47) by introducing a varying number of local word swaps into naturalistic sentences, leading to progressively less syntactically well-formed strings. Critically, local dependency relationships were preserved because combinable words remained close to each other. As predicted, word order degradation did not decrease the magnitude of the blood oxygen level-dependent response in the language network, except when combinable words were so far apart that composition among nearby words was highly unlikely. This finding demonstrates that composition is robust to word order violations, and that the language regions respond as strongly as they do to naturalistic linguistic input, providing that composition can take place.

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 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.

Top-down activation of the visuo-orthographic system during spoken sentence processing

The left ventral occipitotemporal cortex (vOT) is considered the key area of the visuo-orthographic system. However, some studies reported that the area is also involved in speech processing tasks, especially those that require activation of orthographic knowledge. These findings suggest the existence of a top-down activation mechanism allowing such cross-modal activation. Yet, little is known about the involvement of the vOT in more natural speech processing situations like spoken sentence processing. Here, we addressed this issue in a functional Magnetic Resonance Imaging (fMRI) study while manipulating the impacts of two factors, i.e., task demands (semantic vs. low-level perceptual task) and the quality of speech signals (sentences presented against clear vs. noisy background). Analyses were performed at the levels of whole brain and region-of-interest (ROI) focusing on the vOT voxels individually identified through a reading task. Whole brain analysis showed that processing spoken sentences induced activity in a large network including the regions typically involved in phonological, articulatory, semantic and orthographic processing. ROI analysis further specified that a significant part of the vOT voxels that responded to written words also responded to spoken sentences, thus, suggesting that the same area within the left occipitotemporal pathway contributes to both reading and speech processing. Interestingly, both analyses provided converging evidence that vOT responses to speech were sensitive to both task demands and quality of speech signals: Compared to the low-level perceptual task, activity of the area increased when efforts on comprehension were required. The impact of background noise depended on task demands. It led to a decrease of vOT activity in the semantic task but not in the low-level perceptual task. Our results provide new insights into the function of this key area of the reading network, notably by showing that its speech-induced top-down activation also generalizes to ecological speech processing situations.

Common cortical architectures for phonological working memory identified in individual brains

Phonological working memory is the capacity to briefly maintain and recall representations of sounds important for speech and language and is believed to be critical for language and reading acquisition. Whether phonological working memory is supported by fronto-parietal brain regions associated with short-term memory storage or perisylvian brain structures implicated in speech perception and production is unclear, perhaps due to variability in stimuli, task demands, and individuals. We used fMRI to assess neurophysiological responses while individuals performed two tasks with closely matched stimuli but divergent task demands-nonword repetition and nonword discrimination-at two levels of phonological working memory load. Using analyses designed to address intersubject variability, we found significant neural responses to the critical contrast of high vs. low phonological working memory load in both tasks in a set of regions closely resembling those involved in speech perception and production. Moreover, within those regions, the voxel-wise patterns of load-related activation were highly correlated between the two tasks. These results suggest that brain regions in the temporal and frontal lobes encapsulate the core neurocomputational components of phonological working memory; an architecture that becomes increasingly evident as neural responses are examined in successively finer-grained detail in individual participants.

An Attempt to Conceptually Replicate the Dissociation between Syntax and Semantics during Sentence Comprehension

Is sentence structure processed by the same neural and cognitive resources that are recruited for processing word meanings, or do structure and meaning rely on distinct resources? Linguistic theorizing and much behavioral evidence suggest tight integration between lexico-semantic and syntactic representations and processing. However, most current proposals of the neural architecture of language continue to postulate a distinction between the two. One of the earlier and most cited pieces of neuroimaging evidence in favor of this dissociation comes from a paper by Dapretto and Bookheimer (1999). Using a sentence-meaning judgment task, Dapretto & Bookheimer observed two distinct peaks within the left inferior frontal gyrus (LIFG): one was more active during a lexico-semantic manipulation, and the other during a syntactic manipulation. Although the paper is highly cited, no attempt has been made, to our knowledge, to replicate the original finding. We report an fMRI study that attempts to do so. Using a combination of whole-brain, group-level ROI, and participant-specific functional ROI approaches, we fail to replicate the original dissociation. In particular, whereas parts of LIFG respond reliably more strongly during lexico-semantic than syntactic processing, no part of LIFG (including in the region defined around the peak reported by Dapretto & Bookheimer) shows the opposite pattern. We speculate that the original result was a false positive, possibly driven by a small subset of participants or items that biased a fixed-effects analysis with low power.

Phase-amplitude coupling between theta and gamma oscillations adapts to speech rate

Low- and high-frequency cortical oscillations play an important role in speech processing. Low-frequency neural oscillations in the delta (<4 Hz) and theta (4-8 Hz) bands entrain to the prosodic and syllabic rates of speech, respectively. Theta band neural oscillations modulate high-frequency neural oscillations in the gamma band (28-40 Hz), which have been hypothesized to be crucial for processing phonemes in natural speech. Since speech rate is known to vary considerably, both between and within talkers, it has yet to be determined whether this nested gamma response reflects an externally induced rhythm sensitive to the rate of the fine-grained structure of the input or a speech rate-independent endogenous response. Here, we recorded magnetoencephalography responses from participants listening to a speech delivered at different rates: decelerated, normal, and accelerated. We found that the phase of theta band oscillations in left and right auditory regions adjusts to speech rate variations. Importantly, we showed that the peak of the gamma response-coupled to the phase of theta-follows the speech rate. This indicates that gamma activity in auditory regions synchronizes with the fine-grain properties of speech, possibly reflecting detailed acoustic analysis of the input.

Functionally distinct language and Theory of Mind networks are synchronized at rest and during language comprehension

Communication requires the abilities to generate and interpret utterances and to infer the beliefs, desires, and goals of others ("Theory of Mind"; ToM). These two abilities have been shown to dissociate: individuals with aphasia retain the ability to think about others' mental states; and individuals with autism are impaired in social reasoning, but their basic language processing is often intact. In line with this evidence from brain disorders, functional MRI (fMRI) studies have shown that linguistic and ToM abilities recruit distinct sets of brain regions. And yet, language is a social tool that allows us to share thoughts with one another. Thus, the language and ToM brain networks must share information despite being implemented in distinct neural circuits. Here, we investigated potential interactions between these networks during naturalistic cognition using functional correlations in fMRI. The networks were functionally defined in individual participants, in terms of preference for sentences over nonwords for language, and for belief inference over physical-event processing for ToM, with both a verbal and a nonverbal paradigm. Although, across experiments, interregion correlations within each network were higher than between-network correlations, we also observed above-baseline synchronization of blood oxygenation level-dependent signal fluctuations between the two networks during rest and story comprehension. This synchronization was functionally specific: neither network was synchronized with the executive control network (functionally defined in terms of preference for a harder over easier version of an executive task). Thus, coordination between the language and ToM networks appears to be an inherent and specific characteristic of their functional architecture. NEW & NOTEWORTHY Humans differ from nonhuman primates in their abilities to communicate linguistically and to infer others' mental states. Although linguistic and social abilities appear to be interlinked onto- and phylogenetically, they are dissociated in the adult human brain. Yet successful communication requires language and social reasoning to work in concert. Using functional MRI, we show that language regions are synchronized with social regions during rest and language comprehension, pointing to a possible mechanism for internetwork interaction.

Independent Neural Activity Patterns for Sensory- and Confidence-Based Information Maintenance during Category-Selective Visual Processing

Several influential theories of consciousness attempt to explain how, when and where conscious perception arises in the brain. The extent of conscious perception of a stimulus is often probed by asking subjects to provide confidence estimations in their choices in challenging perceptual decision-making tasks. Here, we aimed to dissociate neural patterns of "cognitive" and "sensory" information maintenance by linking category selective visual processes to decision confidence using multivariate decoding techniques on human EEG data. Participants discriminated at-threshold masked face versus house stimuli and reported confidence in their discrimination performance. Three distinct types of category-selective neural activity patterns were observed, dissociable by their timing, scalp topography, relationship with decision confidence, and generalization profile. An early (∼150-200 ms) decoding profile was unrelated to confidence and quickly followed by two distinct decodable patterns of late neural activity (350-500 ms). One pattern was on-diagonal, global and highly related to decision confidence, likely indicating cognitive maintenance of consciously reportable stimulus representations. The other pattern however was off-diagonal, restricted to posterior electrode sites (local), and independent of decision confidence, and therefore may reflect sensory maintenance of category-specific information, possibly operating via recurrent processes within visual cortices. These results highlight that two functionally independent neural processes are operating in parallel, only one of which is related to decision confidence and conscious access.

Cortical phase locking to accelerated speech in blind and sighted listeners prior to and after training

Cross-correlation of magnetoencephalography (MEG) with time courses derived from the speech signal has shown differences in phase-locking between blind subjects able to comprehend accelerated speech and sighted controls. The present training study contributes to disentangle the effects of blindness and training. Both subject groups (baseline: n = 16 blind, 13 sighted; trained: 10 blind, 3 sighted) were able to enhance speech comprehension up to ca. 18 syllables per second. MEG responses phase-locked to syllable onsets were captured in five pre-defined source locations comprising left and right auditory cortex (A1), right visual cortex (V1), left inferior frontal gyrus (IFG) and left pre-supplementary motor area. Phase locking in A1 was consistently increased while V1 showed opposite training effects in blind and sighted subjects. Also the IFG showed some group differences indicating enhanced top-down strategies in sighted subjects while blind subjects may have a more fine-grained bottom-up resolution for accelerated speech.

The possible role of brain rhythms in perceiving fast speech: Evidence from adult aging

The rhythms of speech and the time scales of linguistic units (e.g., syllables) correspond remarkably to cortical oscillations. Previous research has demonstrated that in young adults, the intelligibility of time-compressed speech can be rescued by "repackaging" the speech signal through the regular insertion of silent gaps to restore correspondence to the theta oscillator. This experiment tested whether this same phenomenon can be demonstrated in older adults, who show age-related changes in cortical oscillations. The results demonstrated a similar phenomenon for older adults, but that the "rescue point" of repackaging is shifted, consistent with a slowing of theta oscillations.

Cortical circuits for mathematical knowledge: evidence for a major subdivision within the brain's semantic networks

Is mathematical language similar to natural language? Are language areas used by mathematicians when they do mathematics? And does the brain comprise a generic semantic system that stores mathematical knowledge alongside knowledge of history, geography or famous people? Here, we refute those views by reviewing three functional MRI studies of the representation and manipulation of high-level mathematical knowledge in professional mathematicians. The results reveal that brain activity during professional mathematical reflection spares perisylvian language-related brain regions as well as temporal lobe areas classically involved in general semantic knowledge. Instead, mathematical reflection recycles bilateral intraparietal and ventral temporal regions involved in elementary number sense. Even simple fact retrieval, such as remembering that 'the sine function is periodical' or that 'London buses are red', activates dissociated areas for math versus non-math knowledge. Together with other fMRI and recent intracranial studies, our results indicated a major separation between two brain networks for mathematical and non-mathematical semantics, which goes a long way to explain a variety of facts in neuroimaging, neuropsychology and developmental disorders.This article is part of a discussion meeting issue 'The origins of numerical abilities'.

Reduced Performance During a Sentence Repetition Task by Continuous Theta-Burst Magnetic Stimulation of the Pre-supplementary Motor Area

The pre-supplementary motor area (pre-SMA) is engaged in speech comprehension under difficult circumstances such as poor acoustic signal quality or time-critical conditions. Previous studies found that left pre-SMA is activated when subjects listen to accelerated speech. Here, the functional role of pre-SMA was tested for accelerated speech comprehension by inducing a transient “virtual lesion” using continuous theta-burst stimulation (cTBS). Participants were tested (1) prior to (pre-baseline), (2) 10 min after (test condition for the cTBS effect), and (3) 60 min after stimulation (post-baseline) using a sentence repetition task (formant-synthesized at rates of 8, 10, 12, 14, and 16 syllables/s). Speech comprehension was quantified by the percentage of correctly reproduced speech material. For high speech rates, subjects showed decreased performance after cTBS of pre-SMA. Regarding the error pattern, the number of incorrect words without any semantic or phonological similarity to the target context increased, while related words decreased. Thus, the transient impairment of pre-SMA seems to affect its inhibitory function that normally eliminates erroneous speech material prior to speaking or, in case of perception, prior to encoding into a semantically/pragmatically meaningful message.

Convergence of spoken and written language processing in the superior temporal sulcus

Spoken and written language processing streams converge in the superior temporal sulcus (STS), but the functional and anatomical nature of this convergence is not clear. We used functional MRI to quantify neural responses to spoken and written language, along with unintelligible stimuli in each modality, and employed several strategies to segregate activations on the dorsal and ventral banks of the STS. We found that intelligible and unintelligible inputs in both modalities activated the dorsal bank of the STS. The posterior dorsal bank was able to discriminate between modalities based on distributed patterns of activity, pointing to a role in encoding of phonological and orthographic word forms. The anterior dorsal bank was agnostic to input modality, suggesting that this region represents abstract lexical nodes. In the ventral bank of the STS, responses to unintelligible inputs in both modalities were attenuated, while intelligible inputs continued to drive activation, indicative of higher level semantic and syntactic processing. Our results suggest that the processing of spoken and written language converges on the posterior dorsal bank of the STS, which is the first of a heterogeneous set of language regions within the STS, with distinct functions spanning a broad range of linguistic processes.

Brain correlates of constituent structure in sign language comprehension

During sentence processing, areas of the left superior temporal sulcus, inferior frontal gyrus and left basal ganglia exhibit a systematic increase in brain activity as a function of constituent size, suggesting their involvement in the computation of syntactic and semantic structures. Here, we asked whether these areas play a universal role in language and therefore contribute to the processing of non-spoken sign language. Congenitally deaf adults who acquired French sign language as a first language and written French as a second language were scanned while watching sequences of signs in which the size of syntactic constituents was manipulated. An effect of constituent size was found in the basal ganglia, including the head of the caudate and the putamen. A smaller effect was also detected in temporal and frontal regions previously shown to be sensitive to constituent size in written language in hearing French subjects (Pallier et al., 2011). When the deaf participants read sentences versus word lists, the same network of language areas was observed. While reading and sign language processing yielded identical effects of linguistic structure in the basal ganglia, the effect of structure was stronger in all cortical language areas for written language relative to sign language. Furthermore, cortical activity was partially modulated by age of acquisition and reading proficiency. Our results stress the important role of the basal ganglia, within the language network, in the representation of the constituent structure of language, regardless of the input modality.

Scale-Free Amplitude Modulation of Neuronal Oscillations Tracks Comprehension of Accelerated Speech

Speech comprehension is preserved up to a threefold acceleration, but deteriorates rapidly at higher speeds. Current models posit that perceptual resilience to accelerated speech is limited by the brain's ability to parse speech into syllabic units using δ/θ oscillations. Here, we investigated whether the involvement of neuronal oscillations in processing accelerated speech also relates to their scale-free amplitude modulation as indexed by the strength of long-range temporal correlations (LRTC). We recorded MEG while 24 human subjects (12 females) listened to radio news uttered at different comprehensible rates, at a mostly unintelligible rate and at this same speed interleaved with silence gaps. δ, θ, and low-γ oscillations followed the nonlinear variation of comprehension, with LRTC rising only at the highest speed. In contrast, increasing the rate was associated with a monotonic increase in LRTC in high-γ activity. When intelligibility was restored with the insertion of silence gaps, LRTC in the δ, θ, and low-γ oscillations resumed the low levels observed for intelligible speech. Remarkably, the lower the individual subject scaling exponents of δ/θ oscillations, the greater the comprehension of the fastest speech rate. Moreover, the strength of LRTC of the speech envelope decreased at the maximal rate, suggesting an inverse relationship with the LRTC of brain dynamics when comprehension halts. Our findings show that scale-free amplitude modulation of cortical oscillations and speech signals are tightly coupled to speech uptake capacity.SIGNIFICANCE STATEMENT One may read this statement in 20-30 s, but reading it in less than five leaves us clueless. Our minds limit how much information we grasp in an instant. Understanding the neural constraints on our capacity for sensory uptake is a fundamental question in neuroscience. Here, MEG was used to investigate neuronal activity while subjects listened to radio news played faster and faster until becoming unintelligible. We found that speech comprehension is related to the scale-free dynamics of δ and θ bands, whereas this property in high-γ fluctuations mirrors speech rate. We propose that successful speech processing imposes constraints on the self-organization of synchronous cell assemblies and their scale-free dynamics adjusts to the temporal properties of spoken language.

Discrete and continuous mechanisms of temporal selection in rapid visual streams

Humans can reliably detect a target picture even when tens of images are flashed every second. Here we use magnetoencephalography to dissect the neural mechanisms underlying the dynamics of temporal selection during a rapid serial visual presentation task. Multivariate decoding algorithms allow us to track the overlapping brain responses induced by each image in a rapid visual stream. The results show that temporal selection involves a sequence of gradual followed by all-or-none stages: (i) all images first undergo the same parallel processing pipeline; (ii) starting around 150 ms, responses to multiple images surrounding the target are continuously amplified in ventral visual areas; (iii) only the images that are subsequently reported elicit late all-or-none activations in visual and parietal areas around 350 ms. Thus, multiple images can cohabit in the brain and undergo efficient parallel processing, but temporal selection also isolates a single one for amplification and report.

ICN_Atlas: Automated description and quantification of functional MRI activation patterns in the framework of intrinsic connectivity networks

Generally, the interpretation of functional MRI (fMRI) activation maps continues to rely on assessing their relationship to anatomical structures, mostly in a qualitative and often subjective way. Recently, the existence of persistent and stable brain networks of functional nature has been revealed; in particular these so-called intrinsic connectivity networks (ICNs) appear to link patterns of resting state and task-related state connectivity. These networks provide an opportunity of functionally-derived description and interpretation of fMRI maps, that may be especially important in cases where the maps are predominantly task-unrelated, such as studies of spontaneous brain activity e.g. in the case of seizure-related fMRI maps in epilepsy patients or sleep states. Here we present a new toolbox (ICN_Atlas) aimed at facilitating the interpretation of fMRI data in the context of ICN. More specifically, the new methodology was designed to describe fMRI maps in function-oriented, objective and quantitative way using a set of 15 metrics conceived to quantify the degree of 'engagement' of ICNs for any given fMRI-derived statistical map of interest. We demonstrate that the proposed framework provides a highly reliable quantification of fMRI activation maps using a publicly available longitudinal (test-retest) resting-state fMRI dataset. The utility of the ICN_Atlas is also illustrated on a parametric task-modulation fMRI dataset, and on a dataset of a patient who had repeated seizures during resting-state fMRI, confirmed on simultaneously recorded EEG. The proposed ICN_Atlas toolbox is freely available for download at http://icnatlas.com and at http://www.nitrc.org for researchers to use in their fMRI investigations.

Domain-General Brain Regions Do Not Track Linguistic Input as Closely as Language-Selective Regions

Language comprehension engages a cortical network of left frontal and temporal regions. Activity in this network is language-selective, showing virtually no modulation by nonlinguistic tasks. In addition, language comprehension engages a second network consisting of bilateral frontal, parietal, cingulate, and insular regions. Activity in this "multiple demand" (MD) network scales with comprehension difficulty, but also with cognitive effort across a wide range of nonlinguistic tasks in a domain-general fashion. Given the functional dissociation between the language and MD networks, their respective contributions to comprehension are likely distinct, yet such differences remain elusive. Prior neuroimaging studies have suggested that activity in each network covaries with some linguistic features that, behaviorally, influence on-line processing and comprehension. This sensitivity of the language and MD networks to local input characteristics has often been interpreted, implicitly or explicitly, as evidence that both networks track linguistic input closely, and in a manner consistent across individuals. Here, we used fMRI to directly test this assumption by comparing the BOLD signal time courses in each network across different people (n = 45, men and women) listening to the same story. Language network activity showed fewer individual differences, indicative of closer input tracking, whereas MD network activity was more idiosyncratic and, moreover, showed lower reliability within an individual across repetitions of a story. These findings constrain cognitive models of language comprehension by suggesting a novel distinction between the processes implemented in the language and MD networks.SIGNIFICANCE STATEMENT Language comprehension recruits both language-specific mechanisms and domain-general mechanisms that are engaged in many cognitive processes. In the human cortex, language-selective mechanisms are implemented in the left-lateralized "core language network", whereas domain-general mechanisms are implemented in the bilateral "multiple demand" (MD) network. Here, we report the first direct comparison of the respective contributions of these networks to naturalistic story comprehension. Using a novel combination of neuroimaging approaches we find that MD regions track stories less closely than language regions. This finding constrains the possible contributions of the MD network to comprehension, contrasts with accounts positing that this network has continuous access to linguistic input, and suggests a new typology of comprehension processes based on their extent of input tracking.

Neurophysiological dynamics of phrase-structure building during sentence processing

Although sentences unfold sequentially, one word at a time, most linguistic theories propose that their underlying syntactic structure involves a tree of nested phrases rather than a linear sequence of words. Whether and how the brain builds such structures, however, remains largely unknown. Here, we used human intracranial recordings and visual word-by-word presentation of sentences and word lists to investigate how left-hemispheric brain activity varies during the formation of phrase structures. In a broad set of language-related areas, comprising multiple superior temporal and inferior frontal sites, high-gamma power increased with each successive word in a sentence but decreased suddenly whenever words could be merged into a phrase. Regression analyses showed that each additional word or multiword phrase contributed a similar amount of additional brain activity, providing evidence for a merge operation that applies equally to linguistic objects of arbitrary complexity. More superficial models of language, based solely on sequential transition probability over lexical and syntactic categories, only captured activity in the posterior middle temporal gyrus. Formal model comparison indicated that the model of multiword phrase construction provided a better fit than probability-based models at most sites in superior temporal and inferior frontal cortices. Activity in those regions was consistent with a neural implementation of a bottom-up or left-corner parser of the incoming language stream. Our results provide initial intracranial evidence for the neurophysiological reality of the merge operation postulated by linguists and suggest that the brain compresses syntactically well-formed sequences of words into a hierarchy of nested phrases.

Automaticity of phonological and semantic processing during visual word recognition

Reading involves activation of phonological and semantic knowledge. Yet, the automaticity of the activation of these representations remains subject to debate. The present study addressed this issue by examining how different brain areas involved in language processing responded to a manipulation of bottom-up (level of visibility) and top-down information (task demands) applied to written words. The analyses showed that the same brain areas were activated in response to written words whether the task was symbol detection, rime detection, or semantic judgment. This network included posterior, temporal and prefrontal regions, which clearly suggests the involvement of orthographic, semantic and phonological/articulatory processing in all tasks. However, we also found interactions between task and stimulus visibility, which reflected the fact that the strength of the neural responses to written words in several high-level language areas varied across tasks. Together, our findings suggest that the involvement of phonological and semantic processing in reading is supported by two complementary mechanisms. First, an automatic mechanism that results from a task-independent spread of activation throughout a network in which orthography is linked to phonology and semantics. Second, a mechanism that further fine-tunes the sensitivity of high-level language areas to the sensory input in a task-dependent manner.

Adult-like processing of time-compressed speech by newborns: A NIRS study

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Newborns’ perception of time-compressed speech is similar to that of adults.

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Newborns adapt to moderately compressed speech, but not to highly compressed speech.

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Adaptation to time-compressed speech happens at an auditory level.

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Adaptation to time-compressed speech involves the left temporoparietal regions.

Humans can adapt to a wide range of variations in the speech signal, maintaining an invariant representation of the linguistic information it contains. Among them, adaptation to rapid or time-compressed speech has been well studied in adults, but the developmental origin of this capacity remains unknown. Does this ability depend on experience with speech (if yes, as heard in utero or as heard postnatally), with sounds in general or is it experience-independent? Using near-infrared spectroscopy, we show that the newborn brain can discriminate between three different compression rates: normal, i.e. 100% of the original duration, moderately compressed, i.e. 60% of original duration and highly compressed, i.e. 30% of original duration. Even more interestingly, responses to normal and moderately compressed speech are similar, showing a canonical hemodynamic response in the left temporoparietal, right frontal and right temporal cortex, while responses to highly compressed speech are inverted, showing a decrease in oxyhemoglobin concentration. These results mirror those found in adults, who readily adapt to moderately compressed, but not to highly compressed speech, showing that adaptation to time-compressed speech requires little or no experience with speech, and happens at an auditory, and not at a more abstract linguistic level.

The role of the supplementary motor area for speech and language processing

Apart from its function in speech motor control, the supplementary motor area (SMA) has largely been neglected in models of speech and language processing in the brain. The aim of this review paper is to summarize more recent work, suggesting that the SMA has various superordinate control functions during speech communication and language reception, which is particularly relevant in case of increased task demands. The SMA is subdivided into a posterior region serving predominantly motor-related functions (SMA proper) whereas the anterior part (pre-SMA) is involved in higher-order cognitive control mechanisms. In analogy to motor triggering functions of the SMA proper, the pre-SMA seems to manage procedural aspects of cognitive processing. These latter functions, among others, comprise attentional switching, ambiguity resolution, context integration, and coordination between procedural and declarative memory structures. Regarding language processing, this refers, for example, to the use of inner speech mechanisms during language encoding, but also to lexical disambiguation, syntax and prosody integration, and context-tracking.