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Free keywords:
Artificial grammar; Associative processing; fMRI; Hierarchical syntactic processing; Language network; Syntactic category
Abstract:
Grammar is central to any natural language. In the past decades, the artificial grammar of the An Bn type in which a pair of associated elements can be nested in the other pair was considered as a desirable model to mimic human language syntax without semantic interference. However, such a grammar relies on mere associating mechanisms, thus insufficient to reflect the hierarchical nature of human syntax. Here, we test how the brain imposes syntactic hierarchies according to the category relations on linearized sequences by designing a novel artificial "Hierarchical syntactic structure-building Grammar" (HG), and compare this to the An Bn grammar as a "Nested associating Grammar" (NG) based on multilevel associations. Thirty-six healthy German native speakers were randomly assigned to one of the two grammars. Both groups performed a grammaticality judgment task on auditorily presented word sequences generated by the corresponding grammar in the scanner after a successful explicit behavioral learning session. Compared to the NG group, we found that the HG group showed a (a) significantly higher involvement of Brodmann area (BA) 44 in Broca's area and the posterior superior temporal gyrus (pSTG); and (b) qualitatively distinct connectivity between the two regions. Thus, the present study demonstrates that the build-up process of syntactic hierarchies on the basis of category relations critically relies on a distinctive left-hemispheric syntactic network involving BA 44 and pSTG. This indicates that our novel artificial grammar can constitute a suitable experimental tool to investigate syntax-specific processes in the human brain.
