In M.A. Arbib (Ed.), The handbook of brain theory and neural networks (2nd ed.). Cambridge, MA: MIT Press.


Language evolution and change.



Morten H. Christiansen, & Rick Dale


Introduction

Prior to the emergence of writing systems, no direct evidence remains to inform theories about the evolution of language. Only by amassing evidence from many different disciplines can theorizing about the evolution of language be sufficiently constrained to remove it from the realm of pure speculation and allow it to become an area of legitimate scientific inquiry. In order to go beyond existing data, rigorously controlled thought experiments can be used as crucial tests of competing theories. Computational modeling has become a valuable resource for such tests because it enables researchers to test hypotheses about specific aspects of language evolution under controlled circumstances (Cangelosi and Parisi, 2002; Turner, 2002). With the help of computational simulations, it is possible to study various processes that may have been involved in the evolution of language as well as the biological and cultural constraints that may have shaped language into its current form (see EVOLUTION AND LEARNING IN NEURAL NETWORKS).

Connectionist models have played an important role in the computational modeling of language evolution. In some cases, the networks are used as simulated agents to study how social transmission via learning may give rise to the evolution of structured communication systems. In other cases, the specific properties of neural network learning are enlisted to help illuminate the constraints and processes that may have been involved in the evolution of language. The remainder of this chapter surveys this connectionist research, starting from the emergence of early syntax, to the role of social interaction and constraints on network learning in subsequent evolution of language, and to linguistic change within existing languages.


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