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

classical form. This result suggests that artificial neural networks might be more
symbolic in nature than connectionist cognitive scientists care to admit (Dawson,
Medler, & Berkeley, 1997).
Importantly, the Berkeley et al. (1995) analysis was successful because the definite
features that they identified were local. That is, by examining a single band in
a single jittered density plot, one could determine a semantically interpretable set
of features. However, activity bands are not always local. In some instances hidden
value units produce nicely banded jittered density plots that possess definite features,
but these features are difficult to interpret semantically (Dawson & Piercey, 2001).
This occurs when the semantic interpretation is itself distributed across different
bands for different hidden units; an interpretation of such a network requires definite
features from multiple bands to be considered in concert.
While the geometric argument provided earlier motivated a search for the
existence of bands in the hidden units of value unit networks, banding has been
observed in networks of integration devices as well (Berkeley & Gunay, 2004). That
being said, banding is not seen in every value unit network either. The existence of
banding is likely an interaction between network architecture and problem representation;
banding is useful when discovered, but it is only one tool available for
network interpretation.
The important point is that practical tools exist for interpreting the internal
structure of connectionist networks. Many of the technical issues concerning the
relationship between classical and connectionist cognitive science may hinge upon
network interpretations: “In our view, questions like ‘What is a classical rule?’ and
‘Can connectionist networks be classical in nature?’ are also hopelessly unconstrained.
Detailed analyses of the internal structure of particular connectionist
networks provide a specific framework in which these questions can be fruitfully
pursued” (Dawson, Medler, & Berkeley, 1997, p. 39).
4.12 A Parallel Distributed Production System
One of the prototypical architectures for classical cognitive science is the production
system (Anderson, 1983; Kieras & Meyer, 1997; Meyer et al., 2001; Meyer & Kieras,
1997a, 1997b; Newell, 1973, 1990; Newell & Simon, 1972). A production system is a
set of condition-action pairs. Each production works in parallel, scanning working
memory for a pattern that matches its condition. If a production finds such a match,
then it takes control, momentarily disabling the other productions, and performs
its action, which typically involves adding, deleting, copying, or moving symbols in
the working memory.
Production systems have been proposed as a lingua franca for cognitive science,
capable of describing any connectionist or embodied cognitive science theory and
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