Jump to ContentJump to Main Navigation
Show Summary Details

Using complex networks to understand the mental lexicon

Michael S. Vitevitch
  • Corresponding author
  • Department of Psychology, University of Kansas
  • Email:
/ Rutherford Goldstein
  • Department of Psychology, University of Kansas
/ Cynthia S.Q. Siew
  • Department of Psychology, University of Kansas
/ Nichol Castro
  • Department of Psychology, University of Kansas
Published Online: 2015-03-03 | DOI: https://doi.org/10.1515/yplm-2015-0007


Network science is an emerging discipline drawing from sociology, computer science, physics and a number of other fields to examine complex systems in economical, biological, social, and technological domains. To examine these complex systems, nodes are used to represent individual entities, and links are used to represent relationships between entities, forming a web-like structure, or network, of the entire system. The structure that emerges in these complex networks influences the dynamics of that system. We provide a short review of how this mathematical approach has been used to examine the structure found in the phonological lexicon, and of how subsequent psycholinguistic investigations demonstrate that several of the structural characteristics of the phonological network influence various language-related processes, including word retrieval during the recognition and production of spoken words, recovery from instances of failed lexical retrieval, and the acquisition of word-forms. This approach allows researchers to examine the lexicon at the micro-, meso-, and macro-levels, holding much promise for increasing our understanding of language-related processes and representations.

Keywords : mental lexicon; network science; word recognition; word production


  • Albert, R. and A.L. Barabasi. 2002. “Statistical mechanics of complex networks”. Review of Modern Physics 74. 47-97.

  • Albert, R., H. Jeong and A.L. Barabasi. 2000. “Error and attack tolerance of complex networks”. Nature 406. 378-382.

  • Arbesman, S., S.H. Strogatz and M.S. Vitevitch. 2010. “The structure of phonological networks across multiple languages”. International Journal of Bifurcation and Chaos 20. 679-685. [Web of Science] [Crossref]

  • Barabasi, A.L. 2009. “Scale-free networks: A decade and beyond”. Science 325. 412-413. [Web of Science]

  • Bond, Z.S. 1999. Slips of the ear: Errors in the perception of casual conversation. New York: Academic Press.

  • Borgatti, S.P. 2006. “Identifying sets of key players in a network”. Computational, Mathematical and Organizational Theory 12. 21-34.

  • Borsboom, D. and A.O.J. Cramer. 2013. “Network analysis: An integrative approach to the structure of psychopathology”. Annual Review of Clinical Psychology 9. 91-121. [Web of Science] [Crossref]

  • Brandes, U., G. Robins, A. McCranie and S. Wasserman. 2013. “Editorial: What is network science?” Network Science 1. 1-15.

  • Chan, K.Y. and M.S. Vitevitch. 2009. “The influence of the phonological neighborhood clustering-coefficient on spoken word recognition”. Journal of Experimental Psychology: Human Perception and Performance 35. 1934-1949. [Web of Science] [Crossref]

  • Chan, K.Y. and M.S. Vitevitch. 2010. “Network structure influences speech production”. Cognitive Science 34. 685-697. [Web of Science] [Crossref]

  • Charles-Luce, J. and P.A. Luce. 1990. “Similarity neighborhoods of words in young children’s lexicons”. Journal of Child Language 17. 205-215. [Crossref]

  • Girvan, M. and M.E.J. Newman. 2002. “Community structure in social and biological networks”. Proceedings of the National Academy of Sciences 99. 7821-7826.

  • Goldinger, S.D., P.A. Luce, D.B. Pisoni and J.K. Marcario. 1992. “Form-based priming in spoken word recognition: The roles of competition and bias”. Journal of Experimental Psychology: Learning, Memory and Cognition 18. 1211-1238.

  • Goldstein, R. and M.S. Vitevitch. 2014. “The influence of clustering coefficient on word‐learning: How groups of similar sounding words facilitate acquisition”. Frontiers in Psychology 5. 1307. [Web of Science]

  • Griffiths, T.L., M. Steyvers and A. Firl. 2007. “Google and the mind: Predicting fluency with PageRank”. Psychological Science 18. 1069-1076. [Crossref]

  • Hills, T.T., M. Maouene, J. Maouene, A. Sheya and L. Smith. 2009. “Longitudinal analysis of early semantic networks: Preferential attachment or preferential acquisition?” Psychological Science 20. 729-739. [Web of Science] [Crossref]

  • Iyengar, S.R.S., C.E.V. Madhavan, K.A. Zweig and A. Natarajan. 2012. “Understanding human navigation using network analysis”. Topics in Cognitive Science 4. 121-134.

  • Luce, P.A. and N.R. Large. 2001. “Phonotactics, density and entropy in spoken word recognition”. Language and Cognitive Processes 16. 565-581. [Crossref] [Web of Science]

  • Luce, P.A. and D.B. Pisoni. 1998. “Recognizing spoken words: The neighborhood activation model”. Ear and Hearing 19. 1-36. [Crossref]

  • Newman, M.E.J. 2001. “The structure of scientific collaboration networks”. Proceedings of the National Academy of Sciences 98. 404-409.

  • Newman, M.E.J. 2002. “Assortative mixing in networks”. Physical Review Letters, 89. 20889701.

  • Norris, D. 1994. “Shortlist: A connectionist model of continuous speech recognition”. Cognition 52. 189-234. [Crossref]

  • Otake, T. and A. Cutler. 2013. “Lexical selection in action: Evidence from spontaneous punning”. Language and Speech 56. 555-573 [Crossref] [Web of Science]

  • Porter, M.A., J. P. Onnela and P.J. Mucha. 2009. “Communities in networks”. Notices of the American Mathematical Society 56. 1082-1166.

  • Roediger, H. L. and K.B. McDermott. 1995. “Creating false memories: Remembering words not presented in lists”. Journal of Experimental Psychology: Learning, Memory, and Cognition 21. 803-814. [Web of Science]

  • Roodenrys, S., C. Hulme, A. Lethbridge, M. Hinton and L.M. Nimmo. 2002. “Wordfrequency and phonological-neighborhood effects on verbal short-term memory”. Journal of Experimental Psychology: Learning, Memory, and Cognition 28. 1019-1034.

  • Siew, C.S.Q. 2013. “Community structure in the phonological network”. Frontiers in Psychology 4. 553. [Web of Science]

  • Sole, R.V., B. Corominas-Murtra, S. Valverde and L. Steels. 2010. “Language networks: Their structure, function and evolution”. Complexity 15. 20-26. [Web of Science]

  • Sommers, M.S. and B.P. Lewis. 1999. “Who really lives next door: Creating false memories with phonological neighbors”. Journal of Memory and Language 40. 83-108.

  • Sporns, O. 2010. Networks of the brain. MIT Press.

  • Steyvers, M. and J. Tenenbaum. 2005. “The large-scale structure of semantic networks: Statistical analyses and a model of semantic growth”. Cognitive Science 29. 41-78. [Crossref]

  • Storkel, H.L. 2004. “Do children acquire dense neighborhoods? An investigation of similarity neighborhoods in lexical acquisition”. Applied Psycholinguistics 25. 201-221.

  • Strauss, T.J., H.D. Harris and J.S. Magnuson. 2007. “jTRACE: A reimplementation and extension of the TRACE model of speech perception and spoken word recognition”. Behavior Research Methods 39. 19-30. [Crossref]

  • Suarez, L., S.H. Tan, M.J. Yap and W.D. Goh. 2011. “Observing neighborhood effects without neighbors”. Psychonomic Bulletin and Review 18. 605-611. [Crossref] [Web of Science]

  • Valente, T.W. 2012. “Network interventions”. Science 337. 49-53. [Web of Science]

  • Vitevitch, M.S. 2002. “The influence of phonological similarity neighborhoods on speech production”. Journal of Experimental Psychology: Learning, Memory and Cognition 28. 735-747. [Web of Science]

  • Vitevitch, M.S. 2003. “The influence of sublexical and lexical representations on the processing of spoken words in English”. Clinical Linguistics and Phonetics 17. 487-499. [Crossref]

  • Vitevitch, M.S. 2008. “What can graph theory tell us about word learning and lexical retrieval?” Journal of Speech Language Hearing Research 51. 408-422. [Crossref]

  • Vitevitch, M.S., K.Y. Chan and R. Goldstein. 2014. “Insights into failed lexical retrieval from network science”. Cognitive Psychology 68. 1-32. [Crossref] [Web of Science]

  • Vitevitch, M.S., G. Ercal and B. Adagarla. 2011. “Simulating retrieval from a highly clustered network: Implications for spoken word recognition”. Frontiers in Psychology 2. 369. [Web of Science]

  • Vitevitch, M.S., R. Goldstein and E. Johnson. In press. “Path-length and the misperception of speech: Insights from Network Science and Psycholinguistics”. In: Mehler, A., P. Blanchard, B. Job and S. Banish (eds.), Towards a theoretical framework for analyzing complex linguistic networks. (Understanding Complex Systems series.) New York: Springer.

  • Vitevitch, M.S. and P.A. Luce. 2005. “Increases in phonotactic probability facilitate spoken nonword repetition”. Journal of Memory and Language 52. 193-204.

  • Vitevitch, M.S. and E. Rodriguez. 2005. “Neighborhood density effects in spoken word recognition in Spanish”. Journal of Multilingual Communication Disorders 3. 64-73.

  • Vitevitch, M.S. and M.K. Stamer. 2006. “The curious case of competition in Spanish speech production”. Language and Cognitive Processes 21. 760-770. [Crossref]

  • Watts, D.J. and S.H. Strogatz. 1998. “Collective dynamics of ‘small-world’ networks”. Nature 393. 409-410. [Web of Science]

  • Yarkoni, T., D. Balota and M.J. Yap. 2008. “Moving beyond Coltheart’s N: A new measure of orthographic similarity”. Psychonomic Bulletin and Review 15. 971-979. [Web of Science] [Crossref]

  • Yates, M. 2013. “How the clustering of phonological neighbors affects visual word recognition”. Journal of Experimental Psychology: Learning, Memory, and Cognition 39. 1649-1656. [Web of Science]

About the article

Published Online: 2015-03-03

Published in Print: 2014-12-01

Citation Information: Yearbook of the Poznan Linguistic Meeting, ISSN (Online) 2449-7525, DOI: https://doi.org/10.1515/yplm-2015-0007. Export Citation

© 2015. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)

Comments (0)

Please log in or register to comment.
Log in