Minds and Machines

, Volume 15, Issue 1, pp 23–55 | Cite as

Attractor Spaces as Modules: A Semi-Eliminative Reduction of Symbolic AI to Dynamic Systems Theory

Article

Abstract

I propose a semi-eliminative reduction of Fodor’s concept of module to the concept of attractor basin which is used in Cognitive Dynamic Systems Theory (DST). I show how attractor basins perform the same explanatory function as modules in several DST based research program. Attractor basins in some organic dynamic systems have even been able to perform cognitive functions which are equivalent to the If/Then/Else loop in the computer language LISP. I suggest directions for future research programs which could find similar equivalencies between organic dynamic systems and other cognitive functions. This type of research could help us discover how (and/or if) it is possible to use Dynamic Systems Theory to more accurately model the cognitive functions that are now being modeled by subroutines in Symbolic AI computer models. If such a reduction of subroutines to basins of attraction is possible, it could free AI from the limitations that prompted Fodor to say that it was impossible to model certain higher level cognitive functions.

Keywords

animal locomotion attractor spaces bifurcations Central Pattern Generator collective variable connectionism distributed processing Dynamic Systems Theory Fodor GOFAI invariant sets Kelso Mezernich and Kaas modularity orbit Port symbolic systems hypothesis Thelen and Smith Van Gelder Walter Freeman 

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Copyright information

© Springer 2005

Authors and Affiliations

  1. 1.Teed RockwellBerkeleyUSA

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