Abstract

This paper presents an Artificial Life and Neural Network (ALNN) model for the evolution of syntax. The simulation methodology provides a unifying approach for the study of the evolution of language and its interaction with other behavioral and neural factors. The model uses an object manipulation task to simulate the evolution of language based on a simple verb-noun rule. The analyses of results focus on the interaction between language and other non-linguistic abilities, and on the neural control of linguistic abilities. The model shows that the beneficial effects of language on non-linguistic behavior are explained by the emergence of distinct internal representation patterns for the processing of verbs and nouns. Modeling the Evolution of Language The recent development of computational evolutionary models (Wiles & Hallinan, in press) has contributed to the rebirth of interest in the origin and evolution of language. Computational models can directly simulate the evolution of communication and the emergence of language in populations of interacting organisms (Cangelosi & Parisi, in press; Dessalles & Ghadakpour, 2000; Steels, 1997). Various simulation approaches are used such as communication between rule-based agents (Kirby, 1999), recurrent neural networks (Batali, 1994;

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