A recent hypothesis in empirical brain research on language is that the fundamental difference between animal and human communication systems is captured by the distinction between finitestate and more complex phrase-structure grammars, such as context-free and context-sensitive grammars. However, the relevance of this distinction for the study of language as a neurobiological system has been questioned and it has been suggested that a more relevant and partly analogous distinction is that between non-adjacent and adjacent dependencies. Online memory resources are central to the processing of non-adjacent dependencies as information has to be maintained across intervening material. One proposal is that an external memory device in the form of a limited push-down stack is used to process non-adjacent dependencies. We tested this hypothesis in an artificial grammar learning paradigm where subjects acquired non-adjacent dependencies implicitly. Generally, we found no qualitative differences between the acquisition of non-adjacent dependencies and adjacent dependencies. This suggests that although the acquisition of non-adjacent dependencies requires more exposure to the acquisition material, it utilizes the same mechanisms used for acquiring adjacent dependencies. We challenge the push-down stack model further by testing its processing predictions for nested and crossed multiple non-adjacent dependencies. The push-down stack model is partly supported by the results, and we suggest that stack-like properties are some among many natural properties characterizing the underlying neurophysiological mechanisms that implement the online memory resources used in language and structured sequence processing.Correspondence should be sent to Julia Uddén, Stockholm Brain Institute, A2:3, Retzius väg 8, 171 72

We used magnetoencephalography (MEG) to explore the spatiotemporal dynamics of neural oscillations associated with sentence processing in 102 participants. We quantified changes in oscillatory power as the sentence unfolded, and in response to individual words in the sentence. For words early in a sentence compared to those late in the same sentence, we observed differences in left temporal and frontal areas, and bilateral frontal and right parietal regions for the theta, alpha, and beta frequency bands. The neural response to words in a sentence differed from the response to words in scrambled sentences in left-lateralized theta, alpha, beta, and gamma. The theta band effects suggest that a sentential context facilitates lexical retrieval, and that this facilitation is stronger for words late in the sentence. Effects in the alpha and beta bands may reflect the unification of semantic and syntactic information, and are suggestive of easier unification late in a sentence. The gamma oscillations are indicative of predicting the upcoming word during sentence processing. In conclusion, changes in oscillatory neuronal activity capture aspects of sentence processing. Our results support earlier claims that language (sentence) processing recruits areas distributed across both hemispheres, and extends beyond the classical language regions.

The human capacity to implicitly acquire knowledge of structured sequences has recently been investigated in artificial grammar learning using functional magnetic resonance imaging. It was found that the left inferior frontal cortex (IFC; Brodmann's area (BA) 44/45) was related to classification performance. The objective of this study was to investigate whether the IFC (BA 44/45) is causally related to classification of artificial syntactic structures by means of an off-line repetitive transcranial magnetic stimulation (rTMS) paradigm. We manipulated the stimulus material in a 2 x 2 factorial design with grammaticality status and local substring familiarity as factors. The participants showed a reliable effect of grammaticality on classification of novel items after 5 days of exposure to grammatical exemplars without performance feedback in an implicit acquisition task. The results show that rTMS of BA 44/45 improves syntactic classification performance by increasing the rejection rate of non-grammatical items and by shortening reaction times of correct rejections specifically after left-sided stimulation. A similar pattern of results is observed in FMRI experiments on artificial syntactic classification. These results suggest that activity in the inferior frontal region is causally related to artificial syntax processing.

The brain's remarkable capacity for language requires bidirectional interactions between functionally specialized brain regions. We used magnetoencephalography to investigate interregional interactions in the brain network for language while 102 participants were reading sentences. Using Granger causality analysis, we identified inferior frontal cortex and anterior temporal regions to receive widespread input and middle temporal regions to send widespread output. This fits well with the notion that these regions play a central role in language processing. Characterization of the functional topology of this network, using data-driven matrix factorization, which allowed for partitioning into a set of subnetworks, revealed directed connections at distinct frequencies of interaction. Connections originating from temporal regions peaked at alpha frequency, whereas connections originating from frontal and parietal regions peaked at beta frequency. These findings indicate that the information flow between language-relevant brain areas, which is required for linguistic processing, may depend on the contributions of distinct brain rhythms.language | Granger causality | brain networks | magnetoencephalography T he human brain is capable of effortlessly extracting meaning from sequences of written or spoken words by means of a sophisticated interplay between dedicated neocortical regions. Neuroanatomical research has revealed a number of white-matter pathways that facilitate these interregional interactions (1). Electrophysiological research with electro-and magnetoencephalography (EEG/MEG) has revealed with high temporal precision the sequential activation of individual nodes embedded within the human brain network for language (2, 3). However, the nature of the functional interactions that enable the efficient flow of information between the nodes of this network has yet to be elucidated.One important feature of cortical interregional connections is that they are frequently reciprocal in nature (4), which implies that information can be exchanged in a bidirectional fashion. Moreover, the information flow between cortical regions may be facilitated by interregional rhythmic synchronization (5), where neuronal rhythms of specific different frequencies reflect the direction in which the information is flowing (6, 7). This bidirectional flow of information should also be a crucial feature of the neurobiological system that supports language processing. Linguistic processing is not a simple bottom-up process where incoming linguistic information (for instance, when reading a sentence) drives a sequence of activations of cortical areas that gradually transforms a string of letters into a representation of sentence and discourse meaning. Rather, contextual information, which is either already available, or built up while a sentence unfolds, can also provide top-down information, affecting the response in lower-order areas.Here, we show that interregional interactions in the human brain network for language are subserved by rhyt...

This dataset, colloquially known as the Mother Of Unification Studies (MOUS) dataset, contains multimodal neuroimaging data that has been acquired from 204 healthy human subjects. The neuroimaging protocol consisted of magnetic resonance imaging (MRI) to derive information at high spatial resolution about brain anatomy and structural connections, and functional data during task, and at rest. In addition, magnetoencephalography (MEG) was used to obtain high temporal resolution electrophysiological measurements during task, and at rest. All subjects performed a language task, during which they processed linguistic utterances that either consisted of normal or scrambled sentences. Half of the subjects were reading the stimuli, the other half listened to the stimuli. The resting state measurements consisted of 5 minutes eyes-open for the MEG and 7 minutes eyes-closed for fMRI. The neuroimaging data, as well as the information about the experimental events are shared according to the Brain Imaging Data Structure (BIDS) format. This unprecedented neuroimaging language data collection allows for the investigation of various aspects of the neurobiological correlates of language.

Several studies have reported an association between dyslexia and implicit learning deficits. It has been suggested that the weakness in implicit learning observed in dyslexic individuals may be related to sequential processing and implicit sequence learning. In the present article, we review the current literature on implicit learning and dyslexia. We describe a novel, forced-choice structural "mere exposure" artificial grammar learning paradigm and characterize this paradigm in normal readers in relation to the standard grammaticality classification paradigm. We argue that preference classification is a more optimal measure of the outcome of implicit acquisition since in the preference version participants are kept completely unaware of the underlying generative mechanism, while in the grammaticality version, the subjects have, at least in principle, been informed about the existence of an underlying complex set of rules at the point of classification (but not during acquisition). On the basis of the "mere exposure effect," we tested the prediction that the development of preference will correlate with the grammaticality status of the classification items. In addition, we examined the effects of grammaticality (grammatical/nongrammatical) and associative chunk strength (ACS; high/low) on the classification tasks (preference/grammaticality). Using a balanced ACS design in which the factors of grammaticality (grammatical/nongrammatical) and ACS (high/low) were independently controlled in a 2 x 2 factorial design, we confirmed our predictions. We discuss the suitability of this task for further investigation of the implicit learning characteristics in dyslexia.

In this paper, we present two novel perspectives on the function of the left inferior frontal gyrus (LIFG). First, a structured sequence processing perspective facilitates the search for functional segregation within the LIFG and provides a way to express common aspects across cognitive domains including language, music and action. Converging evidence from functional magnetic resonance imaging and transcranial magnetic stimulation studies suggests that the LIFG is engaged in sequential processing in artificial grammar learning, independently of particular stimulus features of the elements (whether letters, syllables or shapes are used to build up sequences). The LIFG has been repeatedly linked to processing of artificial grammars across all different grammars tested, whether they include non-adjacent dependencies or mere adjacent dependencies. Second, we apply the sequence processing perspective to understand how the functional segregation of semantics, syntax and phonology in the LIFG can be integrated in the general organization of the lateral prefrontal cortex (PFC). Recently, it was proposed that the functional organization of the lateral PFC follows a rostro-caudal gradient, such that more abstract processing in cognitive control is subserved by more rostral regions of the lateral PFC. We explore the literature from the viewpoint that functional segregation within the LIFG can be embedded in a general rostro-caudal abstraction gradient in the lateral PFC. If the lateral PFC follows a rostro-caudal abstraction gradient, then this predicts that the LIFG follows the same principles, but this prediction has not yet been tested or explored in the LIFG literature. Integration might provide further insights into the functional architecture of the LIFG and the lateral PFC.

This article briefly reviews some recent work on artificial language learning in children and adults. The final part of the article is devoted to a theoretical formulation of the language learning problem from a mechanistic neurobiological viewpoint and we show that it is logically possible to combine the notion of innate language constraints with, for example, the notion of domain general learning mechanisms. A growing body of empirical evidence suggests that the mechanisms involved in artificial language learning and in structured sequence processing are shared with those of natural language acquisition and natural language processing. Finally, by theoretically analyzing a formal learning model, we highlight Fodor's insight that it is logically possible to combine innate, domain‐specific constraints with domain‐general learning mechanisms.