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Christiansen, M. H. (2002) Language evolution and change. In M.A. Arbib, editor, Handbook of Brain Theory and Neural Networks (2nd Edition). Cambridge, MA: MIT Press.
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Language Evolution and Change
Morten H. Christiansen
Department of Psychology
Cornell University
mhc27@cornell.edu
Rick Dale
Department of Psychology
Cornell University
rad28@cornell.edu
Running title: Language Evolution and Change
Corresponding author: Morten H. Christiansen,
Department of Psychology,
240 Uris Hall
Cornell University
Ithaca, NY 14853
USA.
Email: mhc27@cornell.edu
Phone: (607) 255­3570 Fax: (607) 255­8433 Articles authored/co­authored by MHC: Connectionist models of speech processing; Constituency and recursion in language; Language evolution and change.
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
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BibTex
@incollection{christiansen_languageEvolution2,
  author={M. H. Christiansen},
  title={Language evolution and change},
  year={2002},
  month={November},
  address={Cambridge, MA},
  editor={M.A. Arbib},
  publisher={MIT Press},
  booktitle={Handbook of Brain Theory and Neural Networks (2nd Edition)},
  url={http://www.isrl.uiuc.edu/~amag/langev/paper/christiansen_languageEvolution2.html}
}


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