A Cognitive Neural Model of Executive Functions in Natural Language Processing

Although extensive research has been devoted to cognitive models of human language, the role of executive functions in language processing has little been explored. In this work we present a neural-network-based cognitive architecture which models the development of the procedural knowledge that underpin language processing. The large scale organization of the architecture is based on a multi-component working memory model, with a central executive that controls the flow of information among the slave systems through neural gating mechanisms. The system was validated, starting from a tabula rasa condition, on a on a corpus of five datasets, each devoted to a thematic group, based on literature on early language assessment, at the level of a preschool child. The results show that the system is capable of learning different word classes, and to use them in expressive language, through an open-ended incremental learning process, expressing a broad range of language processing functionalities.