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

to question explicit rules as a mark of the classical: classical models may not themselves
require explicit rules. For instance, classical cognitive scientists view an explicit
rule as an encoded representation that is part of the algorithmic level. Furthermore,
the reason that it is explicitly represented is that it is not part of the architecture
(Fodor & Pylyshyn, 1988). In short, classical theories posit a combination of explicit
(algorithmic, or stored program) and implicit (architectural) determinants of cognition.
As a result, classical debates about the cognitive architecture can be construed
as debates about the implicitness or explicitness of knowledge:
Not only is there no reason why Classical models are required to be ruleexplicit
but—as a matter of fact—arguments over which, if any, rules are explicitly
mentally represented have raged for decades within the Classicist camp.
(Fodor & Pylyshyn, p. 60)
To this point, the current section has tacitly employed the context that the distinction
between explicit rules and implicit knowledge parallels the distinction between
local and distributed representations. However, other contexts are also plausible.
For example, classical models may be characterized as employing explicit rules in
the sense that they employ a structure/process distinction. That is, classical systems
characteristically separate their symbol-holding memories from the rules that
modify stored contents.
For instance, the Turing machine explicitly distinguishes its ticker tape memory
structure from the rules that are executed by its machine head (Turing, 1936).
Similarly, production systems (Anderson, 1983; Newell, 1973) separate their symbolic
structures stored in working memory from the set of productions that scan
and manipulate expressions. The von Neumann (1958, 1993) architecture by definition
separates its memory organ from the other organs that act on stored contents,
such as its logical or arithmetical units.
To further establish this alternative context, some researchers have claimed
that PDP networks or other connectionist architectures do not exhibit the structure/process
distinction. For instance, a network can be considered to be an active
data structure that not only stores information, but at the same time manipulates
it (Hillis, 1985). From this perspective, the network is both structure and process.
However, it is still the case that the structure/process distinction fails to provide
a mark of the classical. The reason for this was detailed in this chapter’s earlier
discussion of control processes. That is, almost all PDP networks are controlled
by external processes—in particular, learning rules (Dawson & Schopflocher, 1992a;
Roy, 2008). This external control takes the form of rules that are as explicit as any to
be found in a classical model.
To bring this discussion to a close, I argue that a third context is possible for
distinguishing explicit rules from implicit knowledge. This context is the difference
between digital and analog processes. Classical rules may be explicit in the
Marks of the Classical? 347