SIT Versus Connectionism and Dynamic Systems Theory
On the one hand, a representational theory like SIT seems opposite to dynamic systems theory (DST). On the other hand, connectionism can be seen as something in between, that is, it flirts with DST when it comes to the usage of differential equations and it flirts with theories like SIT when it comes to the representation of information. In fact, the analyses provided by SIT, connectionism, and DST, correspond to what Marr called the computational, the algorithmic, and the implementational levels of description, respectively. According to Marr, such analyses are complementary rather than opposite.
What SIT, connectionism, and DST have in common is that they describe nonlinear system behavior, that is, a minor change in the input may yield a major change in the output. Their complementarity expresses itself in that they focus on different aspects:
- First, DST focuses primarily on how the state of a physical system as a whole (in this case, the brain) develops over time, whereas both SIT and connectionism focus primarily on what a system does in terms of information processing (which, in this case, can be said to constitute cognition).
- Second, according to both SIT and connectionism, this information processing relies on interactions between pieces of information in distributed representations, that is, in networks of connected pieces of information. In this respect, however, connectionism focuses on concrete interaction mechanisms (i.c., activation spreading) in a prefixed network that is assumed to be suited for any input, whereas SIT focuses on the nature of the outcome of the interactions which are assumed to take place in transient, input-dependent, networks.
Read more about this topic: Structural Information Theory
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