What Can Graph Theory Tell Us About Word Learning and Lexical Retrieval? Purpose Graph theory and the new science of networks provide a mathematically rigorous approach to examine the development and organization of complex systems. These tools were applied to the mental lexicon to examine the organization of words in the lexicon and to explore how that structure might influence the acquisition ... Research Article
Research Article  |   April 01, 2008
What Can Graph Theory Tell Us About Word Learning and Lexical Retrieval?
 
Author Affiliations & Notes
  • Michael S. Vitevitch
    University of Kansas, Lawrence
  • Contact author: Michael S. Vitevitch, Spoken Language Laboratory, Department of Psychology, 1415 Jayhawk Boulevard, University of Kansas, Lawrence, KS 66045. E-mail: mvitevit@ku.edu.
Article Information
Development / Normal Language Processing / Speech, Voice & Prosody / Language / Research Articles
Research Article   |   April 01, 2008
What Can Graph Theory Tell Us About Word Learning and Lexical Retrieval?
Journal of Speech, Language, and Hearing Research, April 2008, Vol. 51, 408-422. doi:10.1044/1092-4388(2008/030)
History: Received June 20, 2006 , Revised February 2, 2007 , Accepted August 6, 2007
 
Journal of Speech, Language, and Hearing Research, April 2008, Vol. 51, 408-422. doi:10.1044/1092-4388(2008/030)
History: Received June 20, 2006; Revised February 2, 2007; Accepted August 6, 2007
Web of Science® Times Cited: 67

Purpose Graph theory and the new science of networks provide a mathematically rigorous approach to examine the development and organization of complex systems. These tools were applied to the mental lexicon to examine the organization of words in the lexicon and to explore how that structure might influence the acquisition and retrieval of phonological word-forms.

Method Pajek, a program for large network analysis and visualization (V. Batagelj & A. Mvrar, 1998), was used to examine several characteristics of a network derived from a computerized database of the adult lexicon. Nodes in the network represented words, and a link connected two nodes if the words were phonological neighbors.

Results The average path length and clustering coefficient suggest that the phonological network exhibits small-world characteristics. The degree distribution was fit better by an exponential rather than a power-law function. Finally, the network exhibited assortative mixing by degree. Some of these structural characteristics were also found in graphs that were formed by 2 simple stochastic processes suggesting that similar processes might influence the development of the lexicon.

Conclusions The graph theoretic perspective may provide novel insights about the mental lexicon and lead to future studies that help us better understand language development and processing.

Acknowledgments
This work was supported in part by grants from the National Institutes of Health to the University of Kansas through the Schiefelbusch Institute for Life Span Studies (R01 DC 006472), the Mental Retardation and Developmental Disabilities Research Center (P30 HD002528), and the Center for Biobehavioral Neurosciences in Communication Disorders (P30 DC005803). I would like to thank Ed Auer, Albert-László Barabási, Steven B. Chin, John Colombo, Mark Steyvers, Daniel B. Stouffer, Steven Strogatz, and Holly Storkel for helpful comments, suggestions, and discussions. I would also like to thank Douglas Kieweg, Mircea Sauciuc, and Brad Torgler for their assistance with several analyses.
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