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      BLISS: an Artificial Language for Learnability Studies

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      Cognitive Computation
      Springer Nature

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          On certain formal properties of grammars

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            Infant artificial language learning and language acquisition.

            The rapidity with which children acquire language is one of the mysteries of human cognition. A view held widely for the past 30 years is that children master language by means of a language-specific learning device. An earlier proposal, which has generated renewed interest, is that children make use of domain-general, associative learning mechanisms. However, our current lack of knowledge of the actual learning mechanisms involved during infancy makes it difficult to determine the relative contributions of innate and acquired knowledge. A recent approach to studying this problem exposes infants to artificial languages and assesses the resulting learning. In this article, we review studies using this paradigm that have led to a number of exciting discoveries regarding the learning mechanisms available during infancy. These studies raise important issues with respect to whether such mechanisms are general or specific to language, the extent to which they reflect statistical learning versus symbol manipulation, and the extent to which such mechanisms change with development. The fine-grained characterizations of infant learning mechanisms that this approach permits should result in a better understanding of the relative contributions of, and the dynamic between, innate and learned factors in language acquisition.
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              Hierarchical artificial grammar processing engages Broca's area.

              The present fMRI study investigates the neural basis of hierarchical processing using two types of artificial grammars: one governed by rules of adjacent dependencies and the other by rules of hierarchical dependencies. The adjacent dependency sequences followed the rule (AB)(n), at which simple transitions between two types of syllable categories were generated (e.g. A(1)B(1)A(2)B(2)). The hierarchical syllable sequences followed the rule A(n)B(n), generating a center-embedded structure (e.g. A(2)A(1)B(1)B(2)) the learning of which required the processing of hierarchical dependencies. When comparing the processing of hierarchical dependencies to adjacent dependencies, significantly higher activations were observed in Broca's area and the adjacent rim of the ventral premotor cortex (BA 44/6) in addition to some several other cortical and sub-cortical regions. These results indicate that Broca's area is part of a neural circuit that is responsible for the processing of hierarchical structures in an artificial grammar.
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                Author and article information

                Journal
                Cognitive Computation
                Cogn Comput
                Springer Nature
                1866-9956
                1866-9964
                December 2011
                October 2011
                : 3
                : 4
                : 539-553
                Article
                10.1007/s12559-011-9113-4
                c986528c-500b-4194-8fd7-57d0ff4fe7f3
                © 2011
                History

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