Simulating Retrieval From A Highly Clustered Network: Implications For Spoken Word Recognition - Vitevitch, Michael S.; Ercal, Güneş; Adagarla, Bhargav (Frontiers Research Foundation, Po Box 110, Lausanne, 1015, Switzerland, 2011)
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.
Keywords: network science; simulation; clustering coefficient; mental lexicon; word recognition; ağ bilimi; simülasyon; kümeleme katsayısı; zihinsel sözlüğü; kelime tanıma