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

Frontiers in Psychology

Michael S. Vitevitch¹ 

关键词: network science;    simulation;    clustering coefficient;    mental lexicon;    word recognition;   

DOI  :  10.3389/fpsyg.2011.00369

学科分类：心理学（综合）

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY