Note: This paper was written for L645 - "Language and Cognition" - a class taught by Bob Port at Indiana University, Winter Semester, 1999. The entire text is available here in PDF format. Below is the introduction and the bibliography. If you prefer another format or have any questions or comments, feel free to E-mail me.

Schemata: Bootstrapping Language Acquisition

by Sean McLennan

April 28, 1999

1. Introduction

It has been demonstrated that Genetic Algorithms (GAs) can perform extremely efficient searches of “solution space” in order to find optimal solutions to complex problems. John Holland (1975) details and David Goldberg (1989) further develops) a theory of schemata to show how even poor solutions hold implicit information about the targeted good solutions. This accounts in part for how GAs can do what they do.

However, it may be that the principles of Schema Theorem extend beyond GAs. One of the arguments against statistical approaches to language acquisition, for example, is that the amount of input required to learn complex linguistic structures would greatly outweigh that which is exhibited in reality. Schema Theorem may hold a key to understanding why this argument is invalid.

Thus, the purpose of this paper is to explain Schema Theorem and explore its potential as a more general principle of information processing by examining language acquisition in particular. An effort is made to restrict the discussion as much as possible; however, inevitably, the implications and arguments presented here will apply to and draw on areas of cognition other than language. This should be interpreted as the possible pervasiveness and usefulness of Schema Theorem.

Should the generalization of the principles of schemata to language acquisition prove valid, the consequence will be that dynamic, statistical models of language and language learning may be more efficient than is currently believed, lending support to the growing argument against assumed innateness.

Section 2. discusses Schema Theorem in depth and how it is applied to genetic algorithms. Section 3. describes how the same fundamentals can be applied to language acquisition, and Section 4. summarizes and suggests areas for further study.


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