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

that fail to materialize (Dreyfus, 1992, p. 85): “Despite predictions, press releases,
films, and warnings, artificial intelligence is a promise and not an accomplished
fact.” Connectionist cognitive science argues that this pattern of failure is due to the
fundamental assumptions of the classical approach that fail to capture the basic
principles of human cognition.
Connectionists propose a very different theory of information processing—
a potential paradigm shift (Schneider, 1987)—to remedy this situation. Even
staunch critics of artificial intelligence research have indicated a certain sympathy
with the connectionist view of information processing (Dreyfus & Dreyfus, 1988;
Searle, 1992). “The fan club includes the most unlikely collection of people. . . .
Almost everyone who is discontent with contemporary cognitive psychology and
current ‘information processing’ models of the mind has rushed to embrace the
‘connectionist alternative’” (Fodor & Pylyshyn, 1988, p. 4).
What are the key problems that connectionists see in classical models? Classical
models invoke serial processes, which make them far too slow to run on sluggish
componentry (Feldman & Ballard, 1982). They involve explicit, local, and digital
representations of both rules and symbols, making these models too brittle. “If in a
digital system of notations a single pulse is missing, absolute perversion of meaning,
i.e., nonsense, may result” (von Neumann, 1958, p. 78). Because of this brittleness,
the behaviour of classical models does not degrade gracefully when presented with
noisy inputs, and such models are not damage resistant. All of these issues arise
from one underlying theme: classical algorithms reflect the kind of information
processing carried out by electronic computers, not the kind that characterizes the
brain. In short, classical theories are not biologically plausible.
Connectionist cognitive science “offers a radically different conception of the
basic processing system of the mind-brain, one inspired by our knowledge of the
nervous system” (Bechtel & Abrahamsen, 2002, p. 2). The basic medium of connectionism
is a type of model called an artificial neural network, or a parallel distributed
processing (PDP) network (McClelland & Rumelhart, 1986; Rumelhart
& McClelland, 1986c). Artificial neural networks consist of a number of simple processors
that perform basic calculations and communicate the results to other processors
by sending signals through weighted connections. The processors operate
in parallel, permitting fast computing even when slow componentry is involved.
They exploit implicit, distributed, and redundant representations, making these
networks not brittle. Because networks are not brittle, their behaviour degrades
gracefully when presented with noisy inputs, and such models are damage resistant.
These advantages accrue because artificial neural networks are intentionally
biologically plausible or neuronally inspired.
Classical cognitive science develops models that are purely symbolic and
which can be described as asserting propositions or performing logic. In contrast,
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