Document Detail


The evolutionary dynamics of grammar acquisition.
MedLine Citation:
PMID:  11237569     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Grammar is the computational system of language. It is a set of rules that specifies how to construct sentences out of words. Grammar is the basis of the unlimited expressibility of human language. Children acquire the grammar of their native language without formal education simply by hearing a number of sample sentences. Children could not solve this learning task if they did not have some pre-formed expectations. In other words, children have to evaluate the sample sentences and choose one grammar out of a limited set of candidate grammars. The restricted search space and the mechanism which allows to evaluate the sample sentences is called universal grammar. Universal grammar cannot be learned; it must be in place when the learning process starts. In this paper, we design a mathematical theory that places the problem of language acquisition into an evolutionary context. We formulate equations for the population dynamics of communication and grammar learning. We ask how accurate children have to learn the grammar of their parents' language for a population of individuals to evolve and maintain a coherent grammatical system. It turns out that there is a maximum error tolerance for which a predominant grammar is stable. We calculate the maximum size of the search space that is compatible with coherent communication in a population. Thus, we specify the conditions for the evolution of universal grammar.
Authors:
N L Komarova; P Niyogi; M A Nowak
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  Journal of theoretical biology     Volume:  209     ISSN:  0022-5193     ISO Abbreviation:  J. Theor. Biol.     Publication Date:  2001 Mar 
Date Detail:
Created Date:  2001-03-12     Completed Date:  2001-05-10     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  0376342     Medline TA:  J Theor Biol     Country:  England    
Other Details:
Languages:  eng     Pagination:  43-59     Citation Subset:  IM    
Copyright Information:
Copyright 2001 Academic Press.
Affiliation:
Institute for Advanced Study, Einstein Drive, Princeton, NJ 08540, USA.
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Child
Child, Preschool
Humans
Language Development*
Learning*
Models, Psychological*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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