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The influence of part-word phonotactic probability/neighborhood density on word learning by preschool children varying in expressive vocabulary*

Published online by Cambridge University Press:  08 July 2010

HOLLY L. STORKEL  and
JILL R. HOOVER
HOLLY L. STORKEL*
Affiliation:
University of Kansas
JILL R. HOOVER
Affiliation:
University of Kansas
*
[*]Address for correspondence: Holly Storkel, PhD, Associate Professor, Department of Speech-Language-Hearing: Sciences and Disorders, University of Kansas, 3001 Dole Human Development Center, 1000 Sunnyside Avenue, Lawrence, KS 66045-7555. e-mail: [email protected]. Jill R. Hoover is now at Indiana University.
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Abstract

The goal of this study was to examine the influence of part-word phonotactic probability/neighborhood density on word learning by preschool children with normal vocabularies that varied in size. Ninety-eight children (age 2 ; 11–6 ; 0) were taught consonant-vowel-consonant (CVC) nonwords orthogonally varying in the probability/density of the CV (i.e. body) and VC (i.e. rhyme). Learning was measured via picture naming. Children with the lowest expressive vocabulary scores showed no effect of either CV or VC probability/density, although floor effects could not be ruled out. In contrast, children with low or high expressive vocabulary scores demonstrated sensitivity to part-word probability/density with the nature of the effect varying by group. Children with the highest expressive vocabulary scores displayed yet a third pattern of part-word probability/density effects. Taken together, word learning by preschool children was influenced by part-word probability/density but the nature of this influence appeared to depend on the size of the lexicon.

Type
Brief Research Report
Information
Journal of Child Language , Volume 38 , Issue 3 , June 2011 , pp. 628 - 643
Copyright
Copyright © Cambridge University Press 2010

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Footnotes

[*]

This research was supported by NIH Grants DC 08095, DC 00052, DC009135, DC 05803 and HD02528. The following individuals contributed to data collection, data processing or analysis: Andrew Aschenbrenner, Teresa Brown, Jennie Fox, Andrea Giles, Nicole Hayes, Jennica Kilwein, Su-Yeon Lee, Junko Maekawa, Shannon Rogers, Mariza Rosales, Josie Row, Katie Shatzer, Maki Sueto, Courtney Winn.

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