A Cognitive Neural Model of Executive Functions in Natural Language Processing

• Published in: Procedia computer science 2015, Vol.71, p.196-201
• Main Authors: Golosio, Bruno, Cangelosi, Angelo, Gamotina, Olesya, Masala, Giovanni Luca
• Format: Article
• Language: English
• Subjects: cognitive architectures; Hebbian learning rule; human language understanding; large-scale artificial neural networks; verbal working memory
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2015.12.200

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.