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Competing models of liaison acquisition: Evidence from corpus and experimental data

Competing models of liaison acquisition: Evidence from corpus and experimental data Abstract: Given that nouns rarely appear in isolation in French, infants acquiring the language must often retrieve the underlying representation of vowel-initial lexical forms from liaison contexts that provide conflicting information about the initial phoneme. Given this ambiguity, how do learners represent these nouns in their lexicons, and how do these representations change as learners’ knowledge of liaison and the lexicon become more adult-like? To explore this question, we analyze the types of errors children make, in both naturalistic and elicited speech, and how these are affected by input frequency. In doing so, we evaluate two major proposals for how children’s early representations of liaison develop. The first model, couched in a constructionist framework, predicts relatively late mastery of liaison (age five or older) and heavy dependence on the contexts in which a particular noun appears in the input. The second model takes an approach to liaison development that integrates it more closely with general phonological development and predicts relatively early mastery (by age three). The results of a corpus study reveal that by age three children are correctly producing liaison in the nominal domain and that their production errors are consistent with a phonological model of liaison acquisition. An elicitation task demonstrates that three-year-olds succeed at learning and correctly apply their knowledge of liaison to new nouns following brief exposure, though their productions continue to be influenced by nouns’ input distributions. Taken together, our findings suggest that by age three children are well on their way to adult-like representations of liaison. A phonologically based model, incorporating the effect of distributional context on early errors, provides a better overall fit to the data we present.* http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Language Linguistic Society of America

Competing models of liaison acquisition: Evidence from corpus and experimental data

Buerkin-Pontrelli, Angelica
Language , Volume 93 (1) – Mar 20, 2017
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Publisher
Linguistic Society of America
Copyright
Copyright © Linguistic Society of America.
ISSN
1535-0665
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Abstract

Abstract: Given that nouns rarely appear in isolation in French, infants acquiring the language must often retrieve the underlying representation of vowel-initial lexical forms from liaison contexts that provide conflicting information about the initial phoneme. Given this ambiguity, how do learners represent these nouns in their lexicons, and how do these representations change as learners’ knowledge of liaison and the lexicon become more adult-like? To explore this question, we analyze the types of errors children make, in both naturalistic and elicited speech, and how these are affected by input frequency. In doing so, we evaluate two major proposals for how children’s early representations of liaison develop. The first model, couched in a constructionist framework, predicts relatively late mastery of liaison (age five or older) and heavy dependence on the contexts in which a particular noun appears in the input. The second model takes an approach to liaison development that integrates it more closely with general phonological development and predicts relatively early mastery (by age three). The results of a corpus study reveal that by age three children are correctly producing liaison in the nominal domain and that their production errors are consistent with a phonological model of liaison acquisition. An elicitation task demonstrates that three-year-olds succeed at learning and correctly apply their knowledge of liaison to new nouns following brief exposure, though their productions continue to be influenced by nouns’ input distributions. Taken together, our findings suggest that by age three children are well on their way to adult-like representations of liaison. A phonologically based model, incorporating the effect of distributional context on early errors, provides a better overall fit to the data we present.*

Journal

Language – Linguistic Society of America

Published: Mar 20, 2017

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