A matter of time: Implicit acquisition of recursive sequence structures

A dominant hypothesis in empirical research on the evolution of language is the following: the fundamental difference between animal and human communication systems is captured by the distinction between regular and more complex non-regular grammars. Studies reporting successful artificial grammar learning of nested recursive structures and imaging studies of the same have methodological shortcomings since they typically allow explicit problem solving strategies and this has been shown to account for the learning effect in subsequent behavioral studies. The present study overcomes these shortcomings by using subtle violations of agreement structure in a preference classification task. In contrast to the studies conducted so far, we use an implicit learning paradigm, allowing the time needed for both abstraction processes and consolidation to take place. Our results demonstrate robust implicit learning of recursively embedded structures (context-free grammar) and recursive structures with cross-dependencies (context-sensitive grammar) in an artificial grammar learning task spanning 9 days. Keywords: Implicit artificial grammar learning; centre embedded; cross-dependency; implicit learning; context-sensitive grammar; context-free grammar; regular grammar; non-regular gramma

Processing multiple non-adjacent dependencies: evidence from sequence learning

Processing non-adjacent dependencies is considered to be one of the hallmarks of human language. Assuming that sequence-learning tasks provide a useful way to tap natural-language-processing mechanisms, we cross-modally combined serial reaction time and artificial-grammar learning paradigms to investigate the processing of multiple nested (A(1)A(2)A(3)B(3)B(2)B(1)) and crossed dependencies (A(1)A(2)A(3)B(1)B(2)B(3)), containing either three or two dependencies. Both reaction times and prediction errors highlighted problems with processing the middle dependency in nested structures (A(1)A(2)A(3)B(3-)B(1)), reminiscent of the 'missing-verb effect' observed in English and French, but not with crossed structures (A(1)A(2)A(3)B(1-)B(3)). Prior linguistic experience did not play a major role: native speakers of German and Dutch-which permit nested and crossed dependencies, respectively-showed a similar pattern of results for sequences with three dependencies. As for sequences with two dependencies, reaction times and prediction errors were similar for both nested and crossed dependencies. The results suggest that constraints on the processing of multiple non-adjacent dependencies are determined by the specific ordering of the non-adjacent dependencies (i.e. nested or crossed), as well as the number of non-adjacent dependencies to be resolved (i. e. two or three). Furthermore, these constraints may not be specific to language but instead derive from limitations on structured sequence learning.Netherlands Organisation of Scientific Research (NWO) [446-08-014]; Max Planck Institute for Psycholinguistics; Donders Institute for Brain, Cognition and Behaviour; Fundacao para a Ciencia e Tecnologia (IBB/CBME, LA, FEDER/POCI) [PTDC/PSI-PCO/110734/2009]; Stockholm Brain Institute; Vetenskapsradet; Swedish Dyslexia Foundation; Hedlunds Stiftelse; Stockholm County Council (ALF, FoUU)info:eu-repo/semantics/publishedVersio

Hierarchical structure in sequence processing: How to measure it and determine its neural implementation

In many domains of human cognition, hierarchically structured representations are thought to play a key role. In this paper, we start with some foundational definitions of key phenomena like “sequence” and “hierarchy," and then outline potential signatures of hierarchical structure that can be observed in behavioral and neuroimaging data. Appropriate behavioral methods include classic ones from psycholinguistics along with some from the more recent artificial grammar learning and sentence processing literature. We then turn to neuroimaging evidence for hierarchical structure with a focus on the functional MRI literature. We conclude that, although a broad consensus exists about a role for a neural circuit incorporating the inferior frontal gyrus, the superior temporal sulcus, and the arcuate fasciculus, considerable uncertainty remains about the precise computational function(s) of this circuitry. An explicit theoretical framework, combined with an empirical approach focusing on distinguishing between plausible alternative hypotheses, will be necessary for further progress

Frequency-specific directed interactions in the human brain network for language

Contains fulltext : 175222.pdf (publisher's version ) (Open Access)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.6 p

A common variant of the CNTNAP2 gene is associated with structural variation in the left superior occipital gyrus

Item does not contain fulltextThe CNTNAP2 gene encodes a cell-adhesion molecule that influences the properties of neural networks and the morphology and density of neurons and glial cells. Previous studies have shown association of CNTNAP2 variants with language-related phenotypes in health and disease. Here, we report associations of a common CNTNAP2 polymorphism (rs7794745) with variation in grey matter in a region in the dorsal visual stream. We tried to replicate an earlier study on 314 subjects by Tan et al. (2010), but now in a substantially larger group of more than 1700 subjects. Carriers of the T allele showed reduced grey matter volume in left superior occipital gyrus, while we did not replicate associations with grey matter volume in other regions identified by Tan et al. (2010). Our work illustrates the importance of independent replication in neuroimaging genetic studies of language-related candidate genes.6 p

Mindfulness reduces habitual responding based on implicit knowledge: Evidence from artificial grammar learning

Contains fulltext : 115730.pdf (publisher's version ) (Closed access)14 p

The Neuroscience of Implicit Learning

Over the past decades, research employing artificial grammar, sequence learning and statistical learning paradigms has flourished, not least because these methods appear to offer a window, albeit with a restricted view, on implicit learning processes underlying natural language learning. But these paradigms usually provide relatively little exposure, use meaningless stimuli, and do not even necessarily target natural language structures. So the question arises whether they engage the same brain regions as natural language. The aim of this review is to use data from brain imaging, brain stimulation, and the effects of brain damage to identify the main brain regions that show sensitivity to structural regularities in implicit learning paradigms and to consider their relationship to natural language processing and learning

Neural activity during sentence processing as reflected in theta, alpha, beta, and gamma oscillations

Item does not contain fulltextWe 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.12 p