Commentary on "the modularity of dynamic systems"
Graduate studies at Western
|Abstract||1. Throughout the paper, and especially in the section called "LISP vs. DST", I worried that there was not enough focus on EXPLANATION. For the real question, it seems to me, is not whether some dynamical system can implement human cognition, but whether the dynamical description of the system is more explanatorily potent than a computational/representational one. Thus we know, for example, that a purely physical specification can fix a system capable of computing any LISP function. But from this it doesn't follow that the physical description is the one we need to understand the power of the system considered as an information processing device. In the same way, I don't think your demonstration that bifurcating attractor sets can yield the same behavior as a LISP program goes any way towards showing that we should not PREFER the LISP description. To reduce symbolic stories to a subset of DST (as hinted in that section) requires MORE than showing this kind of equivalence: it requires showing that there is explanatory gain, or at the very least, no explanatory loss, at that level. I append an extract from a recent paper of mine that touches on these issues, in case it helps clarify what I am after here|
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
|External links||This entry has no external links. Add one.|
|Through your library||Only published papers are available at libraries|
Similar books and articles
W. Schonbein (2005). Cognition and the Power of Continuous Dynamical Systems. Minds and Machines 15 (1):57-71.
Vinod Goel (1991). Notationality and the Information Processing Mind. Minds and Machines 1 (2):129-166.
Marco Van Leeuwen (2005). Questions for the Dynamicist: The Use of Dynamical Systems Theory in the Philosophy of Cognition. [REVIEW] Minds and Machines 15 (3-4):271-333.
Stevan Harnad (1992). Virtual Symposium on Virtual Mind. Minds and Machines 2 (3):217-238.
Jan Treur (2005). States of Change: Explaining Dynamics by Anticipatory State Properties. Philosophical Psychology 18 (4):441-471.
Teed Rockwell (2005). Attractor Spaces as Modules: A Semi-Eliminative Reduction of Symbolic AI to Dynamic Systems Theory. [REVIEW] Minds and Machines 15 (1):23-55.
Added to index2009-01-28
Total downloads35 ( #39,328 of 738,687 )
Recent downloads (6 months)0
How can I increase my downloads?