%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.