Advertisement

Convergent Evolution of Behavioral Function

  • Mario NegrelloEmail author
Chapter
  • 479 Downloads
Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS, volume 1)

Abstract

The invention of behavioral function is often a punctuated event, whereas the development of function is a gradual process. The chapter includes examples from the evolutionary robotics tracking experiment introduced in the previous chapter and shows how both gradual and discontinuous improvement relate to the discovery of function. Attractor landscapes are a conceptual tool that show how invariants appear as agents converge to function. In nature, we find analogies in behavioral function across phyla and taxa, which also exhibit analogous solutions. Analogies in the form and behavior of organisms derive from the ideal implementations of function to which evolution may converge. Convergent evolution towards behavioral function underlies the appearance of instincts and analogous behavior of different organisms. Convergence and divergence also collaborate to resolve an old controversy about punctuated equilibria. In the interplay between the two, an answer can be given to Stephen Jay Gould’s question of what would happen if the evolutionary tape were replayed. The chapter ends with a list of the sources of constancy and variability in behavior.

Keywords

Motor Unit Chaotic Attractor Convergent Evolution Gradual Improvement Functional Behavior 
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. 1.
    Blount Z, Borland C, Lenski R (2008) Inaugural article: Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli. Proc Natl Acad Sci 105(23):7899PubMedCrossRefGoogle Scholar
  2. 2.
    Di Paolo E, Noble J, Bullock S (2000) Simulation models as opaque thought experiments. In: Artificial life VII: Proceedings of the seventh international conference on artificial life. MIT, Cambridge, CA, pp 497–506Google Scholar
  3. 3.
    Eldridge N, Gould S (1972) Punctuated equilibria: An alternative to phyletic gradualism. In: Models in paleobiology. Freeman Cooper & Company, San Francisco, CaliforniaGoogle Scholar
  4. 4.
    von Foerster H, von Glaserfeld E (2005) Einführung in den Konstruktivismus, 9th edn. Piper Press, MunichGoogle Scholar
  5. 5.
    Gould SJ (1990) Wonderful life: The burgess shale and the nature of history. WW Norton & Company, New YorkGoogle Scholar
  6. 6.
    Gould SJ, Lewontin RD (1979) The spandrels of San Marcos and the Panglossian paradigm: A critic of the adaptationist programme. Proc R Soc Lond 205:581–598PubMedCrossRefGoogle Scholar
  7. 7.
    Hochner B, Shomrat T, Fiorito G (2006) The octopus: A model for a comparative analysis of the evolution of learning and memory mechanisms. Biol Bull 210(3):308PubMedCrossRefGoogle Scholar
  8. 8.
    Mahn B (2003) Entwicklung von Neurokontrollern für eine holonome Roboterplatform. Diplomarbeit, Fachhochschule Oldenburg / Ostfriesland / WilhelmshavenGoogle Scholar
  9. 9.
    Mayr E (1954) Change of genetic environment and evolution. In: Evolution as a Process. Allen and Unwin, London, pp 157–180Google Scholar
  10. 10.
    Mayr E (1976) Evolution and the diversity of life. Harvard University Press, HarvardGoogle Scholar
  11. 11.
    Morris SC (2003) Life’s solution: Inevitable humans in a lonely universe. Cambridge University Press, Cambridge, UKCrossRefGoogle Scholar
  12. 12.
    Reidys C, Stadler P, Schuster P (1997) Generic properties of combinatory maps: neutral networks of RNA secondary structures. Bull Math Biol 59(2):339–397PubMedCrossRefGoogle Scholar
  13. 13.
    Schuster P (1997) Landscapes and molecular evolution. Physica D 107(2–4):351–365CrossRefGoogle Scholar
  14. 14.
    Simpson G (1953) The baldwin effect. Evolution 7:110–117CrossRefGoogle Scholar
  15. 15.
    Sporns O (2002) Graph theory methods for the analysis of neural connectivity patterns. In: Neuroscience databases A practical guide, pp 169–83Google Scholar
  16. 16.
    Varela F (1979) Principles of biological autonomy. North Holland, New YorkGoogle Scholar
  17. 17.
    Varela F, Maturana H (1987, 1998) The tree of knowledge, 1st edn. Shambala, Boston, MAGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Okinawa Institute of Science and TechnologyOkinawaJapan

Personalised recommendations