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dc.contributor.authorDowman, Mikeen
dc.date.accessioned2006-03-27
dc.date.available2006-03-27
dc.date.issued2004-01-01
dc.identifier.urihttp://hdl.handle.net/2123/558
dc.description.abstractThis thesis investigates language acquisition and evolution, using the methodologies of Bayesian inference and expression-induction modelling, making specific reference to colour term typology, and syntactic acquisition. In order to test Berlin and Kay's (1969) hypothesis that the typological patterns observed in basic colour term systems are produced by a process of cultural evolution under the influence of universal aspects of human neurophysiology, an expression-induction model was created. Ten artificial people were simulated, each of which was a computational agent. These people could learn colour term denotations by generalizing from examples using Bayesian inference, and the resulting denotations had the prototype properties characteristic of basic colour terms. Conversations between these people, in which they learned from one-another, were simulated over several generations, and the languages emerging at the end of each simulation were investigated. The proportion of colour terms of each type correlated closely with the equivalent frequencies found in the World Colour Survey, and most of the emergent languages could be placed on one of the evolutionary trajectories proposed by Kay and Maffi (1999). The simulation therefore demonstrates how typological patterns can emerge as a result of learning biases acting over a period of time. Further work applied the minimum description length form of Bayesian inference to modelling syntactic acquisition. The particular problem investigated was the acquisition of the dative alternation in English. This alternation presents a learnability paradox, because only some verbs alternate, but children typically do not receive reliable evidence indicating which verbs do not participate in the alternation (Pinker, 1989). The model presented in this thesis took note of the frequency with which each verb occurred in each subcategorization, and so was able to infer which subcategorizations were conspicuously absent, and so presumably ungrammatical. Crucially, it also incorporated a measure of grammar complexity, and a preference for simpler grammars, so that more general grammars would be learned unless there was sufficient evidence to support the incorporation of some restriction. The model was able to learn the correct subcategorizations for both alternating and non-alternating verbs, and could generalise to allow novel verbs to appear in both constructions. When less data was observed, it also overgeneralized the alternation, which is a behaviour characteristic of children when they are learning verb subcategorizations. These results demonstrate that the dative alternation is learnable, and therefore that universal grammar may not be necessary to account for syntactic acquisition. Overall, these results suggest that the forms of languages may be determined to a much greater extent by learning, and by cumulative historical changes, than would be expected if the universal grammar hypothesis were correct.en
dc.format.extent1548239 bytes
dc.format.extent39571 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.languageenen
dc.language.isoen_AU
dc.rightsOtheren
dc.subjectcolour terms;syntax;Bayes Modellingen
dc.titleColour Terms, Syntax and Bayes Modelling Acquisition and Evolutionen
dc.typeThesisen
dc.date.valid2004-01-01en
dc.type.thesisDoctor of Philosophyen
dc.rights.otherCopyright Dowman, Mike;http://www.library.usyd.edu.au/copyright.htmlen
dc.rights.otherThe author retains copyright of this thesisen
usyd.facultyFaculty of Science, School of Information Technologiesen
usyd.degreeDoctor of Philosophy Ph.D.en
usyd.awardinginstThe University of Sydneyen


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