by Christine Hardy, Ph.D. see
her new book
The mechanistic paradigm--with its focus on isolated, independent systems and linear
cause-effect relationships--is giving way to new paradigms that emphasize
self-organization, interdependence and complexity. Frameworks such as systems theory,
neural networks and chaos theory shed new light on complex relationships (such as the
relationship between individuals and their environment); in particular, these frameworks
account for the dynamical evolution of such complex relationships. Semantic Fields theory
blends a network approach with that of chaos theory (or complex dynamical systems theory).
It views learning as a process (Combs 1995), based on connective--rather than
computational--logic, and involving nonlinear dynamics (Guastello, 1995).
The computational (or symbolic) framework considers the mind to be a computer, executing
predefined logical operations on symbols. The connectionist framework, on the other hand,
views the mind as a network of elements and processes, organizing itself toward an optimal
state (vis-à-vis given inputs and/or objectives), on the basis of weighted connections
between the different elements. Chaos theory, in turn, can account for the interaction of
forces and the creation of novel organizational states. Both networks and dynamical
systems exhibit self-organizational properties, i.e., the capacity of a complex system to
reorganize itself internally. The combination of network and chaos theories is therefore a
particularly appealing framework for explaining the self-organizing and evolving features
of the mind. |
