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

patterns for learning to be completed, they have sometimes been criticized as being
examples of “slow learning” (Carpenter, 1989).
Connectionism’s empiricist and associationist nature cast it close to the very
position that classical cognitivism reacted against: psychological behaviourism
(Miller, 2003). Modern classical arguments against connectionist cognitive science
(Fodor & Pylyshyn, 1988) cover much of the same ground as arguments against
behaviourist and associationist accounts of language (Bever, Fodor, & Garrett, 1968;
Chomsky, 1957, 1959a, 1959b, 1965). That is, classical cognitive scientists argue that
artificial neural networks, like their associationist cousins, do not have the computational
power to capture the kind of regularities modelled with recursive rule systems.
However, these arguments against connectionism are flawed. We see in later
sections that computational analyses of artificial neural networks have proven that
they too belong to the class “universal machine.” As a result, the kinds of inputoutput
mappings that have been realized in artificial neural networks are both vast
and diverse. One can find connectionist models in every research domain that has
also been explored by classical cognitive scientists. Even critics of connectionism
admit that “the study of connectionist machines has led to a number of striking and
unanticipated findings; it’s surprising how much computing can be done with a
uniform network of simple interconnected elements” (Fodor & Pylyshyn, 1988, p. 6).
That connectionist models can produce unanticipated results is a direct
result of their empiricist nature. Unlike their classical counterparts, connectionist
researchers do not require a fully specified theory of how a task is accomplished
before modelling begins (Hillis, 1988). Instead, they can let a learning rule discover
how to mediate a desired input-output mapping. Connectionist learning rules serve
as powerful methods for developing new algorithms of interest to cognitive science.
Hillis (1988, p. 176) has noted that artificial neural networks allow “for the possibility
of constructing intelligence without first understanding it.”
One problem with connectionist cognitive science is that the algorithms that
learning rules discover are extremely difficult to retrieve from a trained network
(Dawson, 1998, 2004, 2009; Dawson & Shamanski, 1994; McCloskey, 1991; Mozer &
Smolensky, 1989; Seidenberg, 1993). This is because these algorithms involve distributed,
parallel interactions amongst highly nonlinear elements. “One thing that connectionist
networks have in common with brains is that if you open them up and peer
inside, all you can see is a big pile of goo” (Mozer & Smolensky, 1989, p. 3).
In the early days of modern connectionist cognitive science, this was not a
concern. This was a period of what has been called “gee whiz” connectionism
(Dawson, 2009), in which connectionists modelled phenomena that were typically
described in terms of rule-governed symbol manipulation. In the mid-1980s
it was sufficiently interesting to show that such phenomena might be accounted
for by parallel distributed processing systems that did not propose explicit rules or
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