Simulating retrieval from a highly clustered network: implications for spoken word recognition


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Vitevitch M. S., Ercal G., Adagarla B.

FRONTIERS IN PSYCHOLOGY, vol.2, 2011 (SSCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 2
  • Publication Date: 2011
  • Doi Number: 10.3389/fpsyg.2011.00369
  • Journal Name: FRONTIERS IN PSYCHOLOGY
  • Journal Indexes: Social Sciences Citation Index (SSCI), Scopus
  • Istanbul Kültür University Affiliated: Yes

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.