Mind, Body, World- Foundations of Cognitive Science, 2013a

classical models (Hurley, 2001)—a connectionist sandwich (Calvo & Gomila, 2008,
p. 5): “Cognitive sandwiches need not be Fodorian. A feed forward connectionist
network conforms equally to the sandwich metaphor. The input layer is identified
with a perception module, the output layer with an action one, and hidden space
serves to identify metrically, in terms of the distance relations among patterns of
activation, the structural relations that obtain among concepts. The hidden layer
this time contains the meat of the connectionist sandwich.”
A difference between classical and connectionist cognitive science is not that
the former is representational and the latter is not. Both are representational, but
they disagree about the nature of mental representations. “The major lesson of
neural network research, I believe, has been to thus expand our vision of the ways
a physical system like the brain might encode and exploit information and knowledge”
(Clark, 1997, p. 58).
4.5 Connectionist Computations: An Overview
In the preceding sections some of the basic characteristics of connectionist networks
were presented. These elements of connectionist cognitive science have emerged as
a reaction against key assumptions of classical cognitive science. Connectionist cognitive
scientists replace rationalism with empiricism, and recursion with chains of
associations.
Although connectionism reacts against many of the elements of classical cognitive
science, there are many similarities between the two. In particular, the multiple
levels of analysis described in Chapter 2 apply to connectionist cognitive science
just as well as they do to classical cognitive science (Dawson, 1998). The next two
sections of this chapter focus on connectionist research in terms of one of these, the
computational level of investigation.
Connectionism’s emphasis on both empiricism and associationism has raised
the spectre, at least in the eyes of many classical cognitive scientists, of a return to
the behaviourism that cognitivism itself revolted against. When cognitivism arose,
some of its early successes involved formal proofs that behaviourist and associationist
theories were incapable of accounting for fundamental properties of human languages
(Bever, Fodor, & Garrett, 1968; Chomsky, 1957, 1959b, 1965, 1966). With the
rise of modern connectionism, similar computational arguments have been made
against artificial neural networks, essentially claiming that they are not sophisticated
enough to belong to the class of universal machines (Fodor & Pylyshyn, 1988).
In Section 4.6, “Beyond the Terminal Meta-postulate,” we consider the in-principle
power of connectionist networks, beginning with two different types of tasks
that networks can be used to accomplish. One is pattern classification: assigning an
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