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

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found that informant learning permitted context-sensitive and context-free grammars<br />

to be identified in the limit.<br />

Gold’s (1967) research was conducted in a relatively obscure field <strong>of</strong> theoretical<br />

computer science. However, Steven Pinker brought it to the attention <strong>of</strong> cognitive<br />

science more than a decade later (Pinker, 1979), where it sparked a great deal <strong>of</strong><br />

interest and research. This is because Gold’s computational analysis revealed a paradox<br />

<strong>of</strong> particular interest to researchers who studied how human children acquire<br />

language.<br />

Gold’s (1967) pro<strong>of</strong>s indicated that informant learning was powerful enough<br />

that a complex grammar can be identified in the limit. Such learning was not possible<br />

with text learning. Gold’s paradox emerged because research strongly suggests<br />

that children are text learners, not informant learners (Pinker, 1979, 1994, 1999). It<br />

is estimated that 99.93 percent <strong>of</strong> the language to which children are exposed is<br />

grammatical (Newport, Gleitman, & Gleitman, 1977). Furthermore, whenever feedback<br />

about language grammaticality is provided to children, it is not systematic<br />

enough to be used to select a grammar (Marcus, 1993).<br />

Gold’s paradox is that while he proved that grammars complex enough to model<br />

human language could not be text learned, children learn such grammars—and do<br />

so via text learning! How is this possible?<br />

Gold’s paradox is an example <strong>of</strong> a problem <strong>of</strong> underdetermination. In a problem<br />

<strong>of</strong> underdetermination, the information available from the environment is<br />

not sufficient to support a unique interpretation or inference (Dawson, 1991). For<br />

instance, Gold (1967) proved that a finite number <strong>of</strong> expressions presented during<br />

text learning were not sufficient to uniquely determine the grammar from which<br />

these expressions were generated, provided that the grammar was more complicated<br />

than a regular grammar.<br />

There are many approaches available for solving problems <strong>of</strong> underdetermination.<br />

One that is most characteristic <strong>of</strong> classical cognitive science is to simplify<br />

the learning situation by assuming that some <strong>of</strong> the to-be-learned information<br />

is already present because it is innate. For instance, classical cognitive scientists<br />

assume that much <strong>of</strong> the grammar <strong>of</strong> a human language is innately available before<br />

language learning begins.<br />

The child has an innate theory <strong>of</strong> potential structural descriptions that is sufficiently<br />

rich and fully developed so that he is able to determine, from a real situation<br />

in which a signal occurs, which structural descriptions may be appropriate to this<br />

signal. (Chomsky, 1965, p. 32)<br />

If the existence <strong>of</strong> an innate, universal base grammar—a grammar used to create<br />

phrase markers—is assumed, then a generative grammar <strong>of</strong> the type proposed by<br />

Chomsky can be identified in the limit (Wexler & Culicover, 1980). This is because<br />

learning the language is simplified to the task <strong>of</strong> learning the set <strong>of</strong> transformations<br />

74 Chapter 3

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