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

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that can be applied to phrase markers. More modern theories <strong>of</strong> transformational<br />

grammars have reduced the number <strong>of</strong> transformations to one, and have described<br />

language learning as the setting <strong>of</strong> a finite number <strong>of</strong> parameters that determine<br />

grammatical structure (Cook & Newson, 1996). Again, these grammars can be identified<br />

in the limit on the basis <strong>of</strong> very simple input expressions (Lightfoot, 1989).<br />

Such pro<strong>of</strong>s are critical to cognitive science and to linguistics, because if a theory <strong>of</strong><br />

language is to be explanatorily adequate, then it must account for how language is<br />

acquired (Chomsky, 1965).<br />

Rationalist philosophers assumed that some human knowledge must be innate.<br />

This view was reacted against by empiricist philosophers who viewed experience as<br />

the only source <strong>of</strong> knowledge. For the empiricists, the mind was a tabula rasa, waiting<br />

to be written upon by the world. Classical cognitive scientists are comfortable<br />

with the notion <strong>of</strong> innate knowledge, and have used problems <strong>of</strong> underdetermination<br />

to argue against the modern tabula rasa assumed by connectionist cognitive<br />

scientists (Pinker, 2002, p. 78): “The connectionists, <strong>of</strong> course, do not believe in a<br />

blank slate, but they do believe in the closest mechanistic equivalent, a generalpurpose<br />

learning device.” The role <strong>of</strong> innateness is an issue that separates classical<br />

cognitive science from connectionism, and will be encountered again when connectionism<br />

is explored in Chapter 4.<br />

3.6 Physical Symbol Systems<br />

Special-purpose logic machines had been developed by philosophers in the late<br />

nineteenth century (Buck & Hunka, 1999; Jevons, 1870; Marquand, 1885). However,<br />

abstract descriptions <strong>of</strong> how devices could perform general-purpose symbol manipulation<br />

did not arise until the 1930s (Post, 1936; Turing, 1936). The basic properties<br />

laid out in these mathematical theories <strong>of</strong> computation define what is now known<br />

as a physical symbol system (Newell, 1980; Newell & Simon, 1976). The concept<br />

physical symbol system defines “a broad class <strong>of</strong> systems that is capable <strong>of</strong> having<br />

and manipulating symbols, yet is also realizable within our physical universe”<br />

(Newell, 1980, p. 136).<br />

A physical symbol system operates on a finite set <strong>of</strong> physical tokens called symbols.<br />

These are components <strong>of</strong> a larger physical entity called a symbol structure or<br />

a symbolic expression. It also consists <strong>of</strong> a set <strong>of</strong> operators that can create, modify,<br />

duplicate, or destroy symbols. Some sort <strong>of</strong> control is also required to select at any<br />

given time some operation to apply. A physical symbol system produces, over time,<br />

an evolving or changing collection <strong>of</strong> expressions. These expressions represent or<br />

designate entities in the world (Newell, 1980; Newell & Simon, 1976). As a result,<br />

the symbol manipulations performed by such a device permit new meanings to be<br />

Elements <strong>of</strong> Classical <strong>Cognitive</strong> <strong>Science</strong> 75

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