title: Simulating retrieval from a highly clustered network: implications for spoken word recognition.
abstract: 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.
authors.affiliation: Department of Psychology, University of Kansas Lawrence, KS, USA.
authors.forename: Michael S
authors.initials: MS
authors.lastname: Vitevitch
authors.forename: Gunes
authors.initials: G
authors.lastname: Ercal
authors.forename: Bhargav
authors.initials: B
authors.lastname: Adagarla