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

contents of these two types of sandwiches will differ. One can peer inside an artificial
neural network and find classical rules for logic (Berkeley et al., 1995) or even
an entire production system (Dawson et al., 2000).
At the architectural level of analysis, stronger differences between connectionist
and classical cognitive science can be established. Indeed, the debate between these
two approaches is in essence a debate about architecture. This is because many of
the dichotomies introduced earlier—rationalism vs. empiricism, digital computer
vs. analog brain, structure/process vs. dynamic data, serialism vs. parallelism—are
differences in opinion about cognitive architecture.
In spite of these differences, and in spite of connectionism’s search for biologically
plausible information processing, there is a key similarity at the architectural
level between connectionist and classical cognitive science: at this level, both propose
architectures that are functional, not physical. The connectionist architecture
consists of a set of building blocks: units and their activation functions, modifiable
connections, learning rules. But these building blocks are functional accounts of
the information processing properties of neurons; other brain-like properties are
ignored. Consider one response (Churchland & Churchland, 1990) to the claim that
the mind is the product of the causal powers of the brain (Searle, 1990):
We presume that Searle is not claiming that a successful artificial mind must have
all the causal powers of the brain, such as the power to smell bad when rotting, to
harbor slow viruses such as kuru, to stain yellow with horseradish peroxidase and
so forth. Requiring perfect parity would be like requiring that an artificial flying
device lay eggs. (Churchland & Churchland, 1990, p. 37)
It is the functional nature of the connectionist architecture that enables it to be
almost always studied by simulating it—on a digital computer!
The functional nature of the connectionist architecture raises some complications
when the implementational level of analysis is considered. On the one hand,
many researchers view connectionism as providing implementational-level theories
of cognitive phenomena. At this level, one finds researchers exploring relationships
between biological receptive fields and patterns of connectivity and similar properties
of artificial networks (Ballard, 1986; Bankes & Margoliash, 1993; Bowers, 2009;
Guzik, Eaton, & Mathis, 1999; Keith, Blohm, & Crawford, 2010; Moorhead, Haig, &
Clement, 1989; Poggio, Torre, & Koch, 1985; Zipser & Andersen, 1988). One also
encounters researchers finding biological mechanisms that map onto architectural
properties such as learning rules. For example, there is a great deal of interest in
relating the actions of certain neurotransmitters to Hebb learning (Brown, 1990;
Gerstner & Kistler, 2002; van Hemmen & Senn, 2002). Similarly, it has been argued
that connectionist networks provide an implementational account of associative
learning (Shanks, 1995), a position that ignores its potential contributions at other
levels of analysis (Dawson, 2008).
202 Chapter 4