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

determine which production will act at any given time” (Dawson, Dupuis, & Wilson,
2010, p. 76).
The discussion to this point has used the history of the automatic control of
computers to argue that characteristics of control cannot be used to provide a principled
distinction between classical and embodied cognitive science. Let us now
examine connectionist cognitive science in the context of cognitive control.
Connectionists have argued that the nature of cognitive control provides a principled
distinction between network models and models that belong to the classical
tradition (Rumelhart & McClelland, 1986b). In particular, connectionist cognitive
scientists claim that control in their networks is completely decentralized, and
that this property is advantageous because it is biologically plausible. “There is one
final aspect of our models which is vaguely derived from our understanding of brain
functioning. This is the notion that there is no central executive overseeing the general
flow of processing” (Rumelhart & McClelland, 1986b, p. 134).
However, the claim that connectionist networks are not under central control
is easily refuted; Dawson and Schopflocher (1992a) considered a very simple
connectionist system, the distributed memory or standard pattern associator
described in Chapter 4 (see Figure 4-1). They noted that connectionist researchers
typically describe such models as being autonomous, suggesting that the key
operations of such a memory (namely learning and recall) are explicitly defined in
its architecture, that is, in the connection weights and processors, as depicted in
Figure 4-1.
However, Dawson and Schopflocher (1992a) proceeded to show that even in
such a simple memory system, whether the network learns or recalls information
depends upon instructions provided by an external controller: the programmer
demonstrating the behaviour of the network. When instructed to learn, the components
of the standard pattern associator behave one way. However, when instructed
to recall, these same components behave in a very different fashion. The nature of
the network’s processing depends critically upon signals provided by a controller
that is not part of the network architecture.
For example, during learning the output units in a standard pattern associator
serve as a second bank of input units, but during recall they record the network’s
response to signals sent from the other input units. How the output units behave is
determined by whether the network is involved in either a learning phase or a recall
phase, which is signaled by the network’s user, not by any of its architectural components.
Similarly, during the learning phase connection weights are modified according
to a learning rule, but the weights are not modified during the recall phase. How
the weights behave is under the user’s control. Indeed, the learning rule is defined
outside the architecture of the network that is visible in Figure 4-1.
Dawson and Schopflocher (1992a) concluded that,
Marks of the Classical? 333