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

therefore of subsuming such theories under the umbrella of classical cognitive science
(Vera & Simon, 1993). This is because Vera and Simon (1993) argued that any
situation-action pairing can be represented either as a single production in a production
system or, for complicated situations, as a set of productions. “Productions
provide an essentially neutral language for describing the linkages between information
and action at any desired (sufficiently high) level of aggregation” (p. 42).
Other philosophers of cognitive science have endorsed similar positions. For
instance, von Eckardt (1995) suggested that if one considers distributed representations
in artificial neural networks as being “higher-level” representations, then
connectionist networks can be viewed as being analogous to classical architectures.
This is because when examined at this level, connectionist networks have the capacity
to input and output represented information, to store represented information,
and to manipulate represented information. In other words, the symbolic properties
of classical architectures may emerge from what are known as the subsymbolic
properties of networks (Smolensky, 1988).
However, the view that artificial neural networks are classical in general or
examples of production systems in particular is not accepted by all connectionists.
It has been claimed that connectionism represents a Kuhnian paradigm shift away
from classical cognitive science (Schneider, 1987). With respect to Vera and Simon’s
(1993) particular analysis, their definition of symbol has been deemed too liberal
by some neural network researchers (Touretzky & Pomerleau, 1994). Touretzky and
Pomerlau (1994) claimed of a particular neural network discussed by Vera and
Simon, ALVINN (Pomerleau, 1991), that its hidden unit “patterns are not arbitrarily
shaped symbols, and they are not combinatorial. Its hidden unit feature detectors
are tuned filters” (Touretzky & Pomerleau, 1994, p. 348). Others have viewed
ALVINN from a position of compromise, noting that “some of the processes are
symbolic and some are not” (Greeno & Moore, 1993, p. 54).
Are artificial neural networks equivalent to production systems? In the philosophy
of science, if two apparently different theories are in fact identical, then
one theory can be translated into the other. This is called intertheoretic reduction
(Churchland, 1985, 1988; Hooker, 1979, 1981). The widely accepted view that classical
and connectionist cognitive science are fundamentally different (Schneider, 1987)
amounts to the claim that intertheoretic reduction between a symbolic model and
a connectionist network is impossible. One research project (Dawson et al., 2000)
directly examined this issue by investigating whether a production system model
could be translated into an artificial neural network.
Dawson et al. (2000) investigated intertheoretic reduction using a benchmark
problem in the machine learning literature, classifying a very large number (8,124)
of mushrooms as being either edible or poisonous on the basis of 21 different features
(Schlimmer, 1987). Dawson et al. (2000) used a standard machine learning
178 Chapter 4