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Children's use of information in word learning*
Published online by Cambridge University Press: 17 February 2009
Abstract
Whenever children hear a novel word, the context supplies information about its meaning. One way children may cope with so much information is to use whatever seems to make sense, given their prior knowledge and beliefs, while ignoring or quickly forgetting the rest. This work examined if and how children's beliefs about word meanings may affect their use of contrastive linguistic information in the input in word learning. In Study 1, some 3- and 4-year-olds were introduced to a novel material or shape name and heard it contrasted with familiar words. Others merely heard the novel word used for referring to an object. These children were then tested to determine what they had learned about their new word meaning. In Study 2, another group of 3-and 4-year-olds were asked to name the materials and shapes used for introducing these novel terms. Children made use of linguistic contrast only in some situations. They benefited more when the novel term did not overlap much in denotation with any terms commonly known by 3-and 4-year-olds. These results suggest that children can use information in the input very efficiently in learning a term for an as-yet-unnamed category, but not in learning a term similar in denotation to a word they already know. Thus, the results are consistent with the claim that children believe every word has a unique denotation.
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- Copyright © Cambridge University Press 1990
Footnotes
I am grateful to Eve Clark, Herbert Clark, John Flavell, Ellen Markman, Gregory Murphy, Karen Ravn, and two anonymous reviewers for their helpful comments on the manuscript. I thank the children, teachers, and parents of Abracadabra Child Care Center, Buttons & Bows Montessori Preschool, College Terrace Preschool, Darby's Day Care, Grace Lutheran Preschool, Palo Alto Montessori School, Sun Flower Nursery School, and Western Montessori Day School for their cooperation. This work was supported in part by an IBM fellowship and BRS Grant 5-27350 from Brown University.
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