In this chapter we have focused on one particular theoretical framework, but there have been many other approaches described over the years. Probably the most influential model came from Alan Baddeley (1986), who especially focused on working memory, but also argued for a "central executive." In particular, he postulated two specific forms of working memory: 1) a phonological loop for maintaining verbal information and; 2) a visuospatial scratchpad for spatial information. Another highly influential theoretical approach came from Tim Shallice (e.g., 1988,2007) who described a supervisory attentional system (SAS) framework. Finally, there is also the very influential traditional AI approach, which we will discuss briefly below.
Motivated largely by the kinds of cognitive functions listed above, traditional AI has largely focused on a design-oriented approach using symbols that has focused on trying to figure out what it would take to solve a particular kind of problem, and then designing a model that does things that way. There is an irony in this approach in that researchers taking this approach are using the very higher-level cognitive functionality they are trying to explain in order to design a system that will reproduce it. A fundamental problem with this kind of approach is that it basically designs in the very functionality it aims to explain. This is not to say that these kinds of approaches are wholly without merit, only that they are fundamentally limited in what they can ultimately explain. Perhaps for obvious reasons, it has turned out that these kinds of models of cognitive function have been most successful in dealing with the kinds of cognitive function that we listed as being at the highest level - that is, in modeling systems able to do formal mathematics and logic. What they have done less well in has been in accounting for many of the kinds of things that might be considered less high-level, or even lower-level, things which we often take to be automatic. It is for these latter areas, that the biologically informed neural network approach has been most helpful. Thus, these two approaches can be nicely complementary and hybrid approaches are being pursued. For example, the Leabra approach is being hybridized with the ACT-R approach in an architecture called SAL.
All of these approaches are not mutually exclusive, but instead share many common ideas and can be complementary in many ways. In particular, the traditional AI approach, by going straight to solving a high level problem e.g., arithmetic. On the other hand, the goal of the neural network approach we advocate is to provide a more bottom-up model that tries to provide a reductionist account for the emergence of control-like processing based on underlying automatic mechanisms. This is the approach we take with the PBWM framework.