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Simulating retrieval from a highly clustered network: implications for spoken word recognition
Michael S Vitevitch1, Gunes Ercal, Bhargav Adagarla
1Department of Psychology, University of Kansas Lawrence, KS, USA.
Frontiers in Psychology
|December 17, 2011
Summary
Network structure impacts information flow. This study found that lower clustering coefficients (C) in phonological networks facilitate faster lexical retrieval, suggesting network properties influence cognitive processes.
Area of Science:
- Cognitive Science
- Network Science
- Computational Linguistics
Background:
- Network science posits that system structure influences processing.
- Clustering coefficient (C) measures how interconnected a node's neighbors are.
- Previous research suggests low C networks spread information widely, while high C networks contain it.
Purpose of the Study:
- To investigate how clustering coefficient (C) affects activation spread in phonological networks.
- To simulate the retrieval of specific lexical items and its relation to network structure.
Main Methods:
- Network simulation was employed to model activation spread.
- The study focused on the influence of clustering coefficient (C) on activation values.
- Lexical retrieval was simulated within a phonological network context.
Main Results:
- Networks with lower clustering coefficients (C) exhibited higher activation values.
- This suggests that lower C facilitates faster or more accurate lexical retrieval.
- The findings align with previous observations on information diffusion in networks.
Conclusions:
- A simple network mechanism can explain lexical retrieval dynamics.
- Network structure, specifically clustering coefficient, plays a crucial role in cognitive processes like word retrieval.
- The findings have broader implications for understanding diffusion dynamics across various fields.
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