AUTHOR=Vitevitch Michael S., Ercal Gunes , Adagarla Bhargav 

TITLE=Simulating Retrieval from a Highly Clustered Network: Implications for Spoken Word Recognition

JOURNAL=Frontiers in Psychology

VOLUME=2

YEAR=2011

URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2011.00369

DOI=10.3389/fpsyg.2011.00369

ISSN=1664-1078

ABSTRACT=<p>Network science describes how entities in complex systems interact, and argues that the structure of the network influences processing. Clustering coefficient, <italic>C</italic> – 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 <italic>C</italic> dissipate information (or disease) to a large portion of the network, whereas in networks with high <italic>C</italic> information (or disease) tends to be constrained to a smaller portion of the network (Newman, <xref ref-type="bibr" rid="B33">2003</xref>). In the present simulation we examined how <italic>C</italic> 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 <italic>C</italic> had higher activation values (indicating faster or more accurate retrieval from the lexicon) than words with higher <italic>C</italic>. These results suggest that a simple mechanism for lexical retrieval can account for the observations made in Chan and Vitevitch (<xref ref-type="bibr" rid="B10">2009</xref>), and have implications for diffusion dynamics in other fields.</p>