Advertisement

A Possible Role for Selective Masking in the Evolution of Complex, Learned Communication Systems

  • Graham R.S. Ritchie
  • Simon Kirby

Abstract

The human capacity for language is one of our most distinctive characteristics. While communication systems abound in the natural world, human language distinguishes itself in terms of its communicative power, flexibility and complexity. One of the most unusual features of human language, when compared to the communication systems of other species, is the degree to which it involves learning. Just how much of language is innate and how much is learned is an ongoing controversy, but it is undeniable that the specific details of any particular language must be learned anew every generation. We do, of course, bring a great deal of innate resources to bear on our language learning process, and the results these innate biases have on the development of languages may explain a great deal about the structure of the languages we see today. But still every child in every new generation must go through a lengthy process of language acquisition if they are to become normal language users.

Keywords

Human Language Iterate Learning Minimum Description Length Song Type Bird Song 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Brighton, H. (2002). Compositional syntax from cultural transmission. Artificial Life, 8(1):25–54.CrossRefGoogle Scholar
  2. Bullock, S. (1999). Are artificial mutation biases unnatural? In Floreano, D., Nicoud, J.-D., and Mondada, F., editors, Fifth European Conference on Artificial Life (ECAL99), pages 64–73. Springer-Verlag.Google Scholar
  3. Catchpole, C. K. and Slater, P. J. B. (1995). Bird Song: Biological themes and variations. Cambridge University Press.Google Scholar
  4. Darwin, C. (1879). The Descent of Man, and Selection in Relation to Sex. John Murray, London, 2nd edition. Reprinted in 2004 by Penguin.Google Scholar
  5. Deacon, T. (2003). Multilevel selection in a complex adaptive system: the problem of language origins. In Weber, B. and Depew, D., editors, Evolution and Learning: the Baldwin Effect Reconsidered, pages 81–106. MIT Press, Cambridge, MA.Google Scholar
  6. Doupe, A. J. and Kuhl, P. K. (1999). Birdsong and human speech: Common themes and mechanisms. Annual Reviews of Neuroscience, 22:567–631.CrossRefGoogle Scholar
  7. Jarvis, E. D. (2004). Learned birdsong and the neurobiology of human language. Annals of the New York Academy of Sciences, 1016:749–777.CrossRefGoogle Scholar
  8. Kirby, S. and Hurford, J. R. (2002). The emergence of linguistic structure: An overview of the iterated learning model. In Cangelosi, A. and Parisi, D., editors, Simulating the Evolution of Language. Springer Verlag, London.Google Scholar
  9. Kirby, S. (2001). Spontaneous evolution of linguistic structure: an iterated learning model of the emergence of regularity and irregularity. IEEE Journal of Evolutionary Computation, 5(2):102–110.MathSciNetCrossRefGoogle Scholar
  10. Lachlan, R. F. and Feldman, M. W. (2003). Evolution of cultural communication systems: the coevolution of cultural signals and genes encoding learning preferences. Journal of Evolutionary Biology, 16:1084–1095.CrossRefGoogle Scholar
  11. Lachlan, R. F. and Slater, P. J. B. (1999). The maintenance of vocal learning by gene-culture interaction: the cultural trap hypothesis. Proceedings of the Royal Society of London. B, 266:701–706.CrossRefGoogle Scholar
  12. Livingstone, D. (2002). The evolution of dialect diversity. In Cangelosi, A. and Parisi, D., editors, Simulating the Evolution of Language,  chapter 5, pages 99–118. Springer Verlag, London.Google Scholar
  13. Okanoya, K. (2002). Sexual display as a syntactic vehicle: The evolution of syntax in birdsong and human language through sexual selection. In Wray, A., editor, The Transition to Language,  chapter 3. Oxford University Press, Oxford.Google Scholar
  14. Okanoya, K. (2004). The bengalese finch: A window on the behavioral neurobiology of birdsong syntax. Annals of the New York Academy of Sciences, 1016:724–735.CrossRefGoogle Scholar
  15. Sasahara, K. and Ikegami, T. (2004). Song grammars as complex sexual displays. In Artificial Life 9.Google Scholar
  16. Shannon, C. E. (1948). A mathematical theory of communication. Bell Systems Technical Journal, 27:379–423 and 623–656. Reprinted in “Shannon Collected Papers”, ed. Sloane & Wyner, 1993, IEEE Press.MathSciNetGoogle Scholar
  17. Slater, P. J. B. (2003). Fifty years of bird song research: a case study in animal behaviour. Animal Behaviour, 65:633–639.CrossRefGoogle Scholar
  18. Teal, T. K. and Taylor, C. E. (2000). Effects of compression on language evolution. Artificial Life, 6:129–143.CrossRefGoogle Scholar
  19. Wiles, J., Watson, J., Tonkes, B., and Deacon, T. W. (2005). Transient phenomena in learning and evolution: Genetic assimilation and genetic redistribution. Artificial Life, (11):177–188.Google Scholar
  20. Zahavi, A. (1975). Mate selection – a selection for a handicap. Journal of Theoretical Biology, 53:205–214.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2007

Authors and Affiliations

  • Graham R.S. Ritchie
    • 1
  • Simon Kirby
    • 1
  1. 1.Language Evolution and Computation Research Unit, University of EdinburghUK

Personalised recommendations