Neurolinguistic accounts of sentence comprehension identify a network of relevant brain regions, but do not detail the information flowing through them. We investigate syntactic information. Does brain activity implicate a computation over hierarchical grammars or does it simply reflect linear order, as in a Markov chain? To address this question, we quantify the cognitive states implied by alternative parsing models. We compare processing-complexity predictions from these states against fMRI timecourses from regions that have been implicated in sentence comprehension. We find that hierarchical grammars independently predict timecourses from left anterior and posterior temporal lobe. Markov models are predictive in these regions and across a broader network that includes the inferior frontal gyrus. These results suggest that while linear effects are wide-spread across the language network, certain areas in the left temporal lobe deal with abstract, hierarchical syntactic representations.

The neural basis of syntax is a matter of substantial debate. In particular, the inferior frontal gyrus (IFG), or Broca s area, has been prominently linked to syntactic processing, but anterior temporal lobe has been reported to be activated instead of IFG when manipulating the presence of syntactic structure. These findings are difficult to reconcile because they rely on different laboratory tasks which tap into distinct computations, and may only indirectly relate to natural sentence processing. Here we assessed neural correlates of syntactic structure building in natural language comprehension, free from artificial task demands. Subjects passively listened to Alice in Wonderland during functional magnetic resonance imaging and we correlated brain activity with a word-byword measure of the amount syntactic structure analyzed. Syntactic structure building correlated with activity in the left anterior temporal lobe, but there was no evidence for a correlation between syntactic structure building and activity in inferior frontal areas. Our results suggest that the anterior temporal lobe computes syntactic structure under natural conditions.

Recurrent neural network grammars (RNNGs) are generative models of (tree, string) pairs that rely on neural networks to evaluate derivational choices. Parsing with them using beam search yields a variety of incremental complexity metrics such as word surprisal and parser action count. When used as regressors against human electrophysiological responses to naturalistic text, they derive two amplitude effects: an early peak and a P600-like later peak.By contrast, a non-syntactic neural language model yields no reliable effects. Model comparisons attribute the early peak to syntactic composition within the RNNG. This pattern of results recommends the RNNG+beam search combination as a mechanistic model of the syntactic processing that occurs during normal human language comprehension.

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The cognitive neuroscience of language relies largely on controlled experiments that are different from the everyday situations in which we use language. This review describes an approach that studies specific aspects of sentence comprehension in the brain using data collected while participants perform an everyday task, such as listening to a story. The approach uses ‘neuro‐computational’ models that are based on linguistic and psycholinguistic theories. These models quantify how a specific computation, such as identifying a syntactic constituent, might be carried out by a neural circuit word‐by‐word. Model predictions are tested for their statistical fit with measured brain data. The paper discusses three applications of this approach: (i) to probe the location and timing of linguistic processing in the brain without requiring unnatural tasks and stimuli, (ii) to test theoretical hypotheses by comparing the fits of different models to naturalistic data, and (iii) to study neural mechanisms for language processing in populations that are poorly served by traditional methods.

The grammar, or syntax, of human language is typically understood in terms of abstract hierarchical structures. However, theories of language processing that emphasize sequential information, not hierarchy, successfully model diverse phenomena. Recent work probing brain signals has shown mixed evidence for hierarchical information in some tasks. We ask whether sequential or hierarchical information guides the expectations that a human listener forms about a word’s part-of-speech when simply listening to every-day language. We compare the predictions of three computational models against electroencephalography signals recorded from human participants who listen passively to an audiobook story. We find that predictions based on hierarchical structure correlate with the human brain response above-and-beyond predictions based only on sequential information. This establishes a link between hierarchical linguistic structure and neural signals that generalizes across the range of syntactic structures found in every-day language.

BACKGROUND: 16p11.2 breakpoint 4 to 5 copy number variants (CNVs) increase the risk for developing autism spectrum disorder, schizophrenia, and language and cognitive impairment. In this multisite study, we aimed to quantify the effect of 16p11.2 CNVs on brain structure. METHODS: Using voxel-and surface-based brain morphometric methods, we analyzed structural magnetic resonance imaging collected at seven sites from 78 individuals with a deletion, 71 individuals with a duplication, and 212 individuals without a CNV. RESULTS: Beyond the 16p11.2-related mirror effect on global brain morphometry, we observe regional mirror differences in the insula (deletion . control . duplication). Other regions are preferentially affected by either the deletion or the duplication: the calcarine cortex and transverse temporal gyrus (deletion . control; Cohen's d . 1), the superior and middle temporal gyri (deletion , control; Cohen's d , 21), and the caudate and hippocampus (control . duplication; 20.5 . Cohen's d . 21). Measures of cognition, language, and social responsiveness and the presence of psychiatric diagnoses do not influence these results. CONCLUSIONS: The global and regional effects on brain morphometry due to 16p11.2 CNVs generalize across site, computational method, age, and sex. Effect sizes on neuroimaging and cognitive traits are comparable. Findings partially overlap with results of meta-analyses performed across psychiatric disorders. However, the lack of correlation between morphometric and clinical measures suggests that CNV-associated brain changes contribute to clinical manifestations but require additional factors for the development of the disorder. These findings highlight the power of genetic risk factors as a complement to studying groups defined by behavioral criteria. Autism spectrum disorder (ASD) and related neurodevelopmental disorders are defined behaviorally and characterized by a significant clinical and etiologic heterogeneity. As a consequence, investigating ASD under the assumption of an underlying homogeneous condition has resulted in controversial findings in the field of neuroimaging (1). Increased brain growth early in development (2-4) and alterations of many regional brain volumes (5) have been implicated in ASD, but results have proven difficult to replicate (1,(6)(7)(8).To mitigate some of these issues, cohorts of individuals with shared genetic risk factors have been assembled to minimize the noise introduced by etiologic and biological heterogeneity (9). Such a "genetic-first" study design provides the opportunity to investigate a given neurodevelopmental risk (and associated mechanism) shared by individuals who carry the same genetic etiology irrespective of the psychiatric diagnosis.Copy number variants (CNVs) at the 16p11.2 (breakpoints 4-5, 29.6-30.2 Mb-hg19) (10) are among the most frequent risk factors for neurodevelopmental and psychiatric conditions.