%A Vitevitch,Michael
%A Ercal,Gunes
%A Adagarla,Bhargav
%D 2011
%J Frontiers in Psychology
%C
%F
%G English
%K clustering coefficient,diffusion dynamics,mental Lexicon,Network Science,simulation,word recognition
%Q
%R 10.3389/fpsyg.2011.00369
%W
%L
%M
%P
%7
%8 2011-December-14
%9 Original Research
%+ Dr Michael Vitevitch,University of Kansas,Psychology College of Liberal Arts & Sciences,Spoken Language Laboratory,Lawrence, Kansas,United States,mvitevit@ku.edu
%#
%! Simulation of lexical retrieval
%*
%<
%T Simulating Retrieval from a Highly Clustered Network: Implications for Spoken Word Recognition
%U https://www.frontiersin.org/articles/10.3389/fpsyg.2011.00369
%V 2
%0 JOURNAL ARTICLE
%@ 1664-1078
%X Network science describes how entities in complex systems interact, and argues that the structure of the network influences processing. Clustering coefficient, C – one measure of network structure – refers to the extent to which neighbors of a node are also neighbors of each other. Previous simulations suggest that networks with low C dissipate information (or disease) to a large portion of the network, whereas in networks with high C information (or disease) tends to be constrained to a smaller portion of the network (Newman, 2003). In the present simulation we examined how C influenced the spread of activation to a specific node, simulating retrieval of a specific lexical item in a phonological network. The results of the network simulation showed that words with lower C had higher activation values (indicating faster or more accurate retrieval from the lexicon) than words with higher C. These results suggest that a simple mechanism for lexical retrieval can account for the observations made in Chan and Vitevitch (2009), and have implications for diffusion dynamics in other fields.