The Lexical Restructuring Hypothesis and Graph Theoretic Analyses of Networks Based on Random Lexicons Purpose The mental lexicon of words used for spoken word recognition has been modeled as a complex network or graph. Do the characteristics of that graph reflect processes involved in its growth (M. S. Vitevitch, 2008) or simply the phonetic overlap between similar-sounding words? Method Three pseudolexicons were ... Research Article
Research Article  |   June 01, 2009
The Lexical Restructuring Hypothesis and Graph Theoretic Analyses of Networks Based on Random Lexicons
 
Author Affiliations & Notes
  • Thomas M. Gruenenfelder
    Indiana University, Bloomington
  • David B. Pisoni
    Indiana University, Bloomington
  • Contact author: Thomas M. Gruenenfelder, Department of Psychological and Brain Sciences, Indiana University, 1101 East 10th Street, Bloomington, IN 47405. E-mail: tgruenen@indiana.edu.
Article Information
Development / Regulatory, Legislative & Advocacy / Speech, Voice & Prosody / Language / Research Articles
Research Article   |   June 01, 2009
The Lexical Restructuring Hypothesis and Graph Theoretic Analyses of Networks Based on Random Lexicons
Journal of Speech, Language, and Hearing Research, June 2009, Vol. 52, 596-609. doi:10.1044/1092-4388(2009/08-0004)
History: Received January 3, 2008 , Revised August 12, 2008 , Accepted September 19, 2008
 
Journal of Speech, Language, and Hearing Research, June 2009, Vol. 52, 596-609. doi:10.1044/1092-4388(2009/08-0004)
History: Received January 3, 2008; Revised August 12, 2008; Accepted September 19, 2008
Web of Science® Times Cited: 10

Purpose The mental lexicon of words used for spoken word recognition has been modeled as a complex network or graph. Do the characteristics of that graph reflect processes involved in its growth (M. S. Vitevitch, 2008) or simply the phonetic overlap between similar-sounding words?

Method Three pseudolexicons were generated by randomly selecting phonological segments from a fixed set. Each lexicon was then modeled as a graph, linking words differing by one segment. The properties of those graphs were compared with those of a graph based on real English words.

Results The properties of the graphs built from the pseudolexicons matched the properties of the graph based on English words. Each graph consisted of a single large island and a number of smaller islands and hermits. The degree distribution of each graph was better fit by an exponential than by a power function. Each graph showed short path lengths, large clustering coefficients, and positive assortative mixing.

Conclusion The results suggest that there is no need to appeal to processes of growth or language acquisition to explain the formal properties of the network structure of the mental lexicon. These properties emerged because the network was built based on the phonetic overlap of words.

Acknowledgments
This work was supported by National Institutes of Health Grant DC00111. We thank Luis Hernandez for technical assistance and Rob Felty and K. L. Mueller for comments on an earlier draft of this article.
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