Simulating retrieval from a highly clustered network: Implications for spoken word recognition
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2011-12-01
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| Series: | Frontiers in Psychology |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fpsyg.2011.00369/full |
| _version_ | 1818277932629491712 |
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| author | Michael S Vitevitch Gunes eErcal Bhargav eAdagarla |
| author_facet | Michael S Vitevitch Gunes eErcal Bhargav eAdagarla |
| author_sort | Michael S Vitevitch |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T23:09:24Z |
| format | Article |
| id | doaj.art-cc9ad89eb4dd4bfe87aaa0f2a64061dc |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-12-12T23:09:24Z |
| publishDate | 2011-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-cc9ad89eb4dd4bfe87aaa0f2a64061dc2022-12-22T00:08:38ZengFrontiers Media S.A.Frontiers in Psychology1664-10782011-12-01210.3389/fpsyg.2011.0036915018Simulating retrieval from a highly clustered network: Implications for spoken word recognitionMichael S Vitevitch0Gunes eErcal1Bhargav eAdagarla2University of KansasIstanbul Kultur UniversityUniversity of Kansas Medical CenterNetwork 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.http://journal.frontiersin.org/Journal/10.3389/fpsyg.2011.00369/fullsimulationword recognitionmental LexiconNetwork Scienceclustering coefficientdiffusion dynamics |
| spellingShingle | Michael S Vitevitch Gunes eErcal Bhargav eAdagarla Simulating retrieval from a highly clustered network: Implications for spoken word recognition Frontiers in Psychology simulation word recognition mental Lexicon Network Science clustering coefficient diffusion dynamics |
| title | Simulating retrieval from a highly clustered network: Implications for spoken word recognition |
| title_full | Simulating retrieval from a highly clustered network: Implications for spoken word recognition |
| title_fullStr | Simulating retrieval from a highly clustered network: Implications for spoken word recognition |
| title_full_unstemmed | Simulating retrieval from a highly clustered network: Implications for spoken word recognition |
| title_short | Simulating retrieval from a highly clustered network: Implications for spoken word recognition |
| title_sort | simulating retrieval from a highly clustered network implications for spoken word recognition |
| topic | simulation word recognition mental Lexicon Network Science clustering coefficient diffusion dynamics |
| url | http://journal.frontiersin.org/Journal/10.3389/fpsyg.2011.00369/full |
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