Language Networks as Models of Cognition: Understanding Cognition through Language

  • Nicole M. BeckageEmail author
  • Eliana Colunga
Part of the Understanding Complex Systems book series (UCS)


Language is inherently cognitive and distinctly human. Separating the object of language from the human mind that processes and creates language fails to capture the full language system. Linguistics traditionally has focused on the study of language as a static representation, removed from the human mind. Network analysis has traditionally been focused on the properties and structure that emerge from network representations. Both disciplines could gain from looking at language as a cognitive process. In contrast, psycholinguistic research has focused on the process of language without committing to a representation. However, by considering language networks as approximations of the cognitive system we can take the strength of each of these approaches to study human performance and cognition as related to language. This paper reviews research showcasing the contributions of network science to the study of language. Specifically, we focus on the interplay of cognition and language as captured by a network representation. To this end, we review different types of language network representations before considering the influence of global level network features. We continue by considering human performance in relation to network structure and conclude with theoretical network models that offer potential and testable explanations of cognitive and linguistic phenomena.


Target Word Word Frequency Semantic Memory Lexical Access Edit Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [Albert et al.(1999)]
    Albert, R., Jeong, H., Barabási, A.L.: Internet: Diameter of the worldwide web. Nature 401(130) (1999)Google Scholar
  2. [Arbesman et al.(2010a)]
    Arbesman, S., Strogatz, S.H., Vitevitch, M.S.: Comparative Analysis of Networks of Phonologically Similar Words in English and Spanish. Entropy 12(3), 327–337 (2010a)CrossRefGoogle Scholar
  3. [Arbesman et al.(2010b)]
    Arbesman, S., Strogatz, S.H., Vitevitch, M.S.: The structure of phonological networks across multiple languages. International Journal of Bifurcation and Chaos 20, 679–685 (2010b)CrossRefGoogle Scholar
  4. [Balota and Spieler(1999)]
    Balota, D., Spieler, D.H.: Word Frequency, Repetition, and Lexicality Effects in Word Recognition Tasks: Beyond Measures of Central Tendency. Journal of Experimental Psychology: General (1999)Google Scholar
  5. [Barabási and Albert(1999)]
    Barabási, A.L., Albert, R.: Emergence of Scaling in Random Networks. Science 286(5439), 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  6. [Baronchelli et al.(2013)]
    Baronchelli, A., Ferrer-I-Cancho, R., Pastor-Satorras, R., Chater, N., Christiansen, M.H.: Networks in cognitive science. Trends in Cognitive Sciences 17(7), 348–360 (2013)CrossRefGoogle Scholar
  7. [Beckage et al.(2011)]
    Beckage, N., Smith, L., Hills, T.: Small worlds and semantic network growth in typical and late talkers. PloS One 6(5), e19348 (2011)Google Scholar
  8. [Beckage et al.(2012)]
    Beckage, N., Steyvers, M., Butts, C.T.: Route choice in individuals– semantic network navigation. In: Miyake, N., Peebles, D., Cooper, R.D. (eds.) Proceedings of the 34th Annual Conference of the Cognitive Science Society, pp. 108–113. Cognitive Science Society, Austin (2012)Google Scholar
  9. [Borge-Holthoefer and Arenas(2010)]
    Borge-Holthoefer, J., Arenas, A.: Semantic Networks: Structure and Dynamics. Entropy 12(5), 1264–1302 (2010)CrossRefGoogle Scholar
  10. [Borge-Holthoefer et al.(2011)]
    Borge-Holthoefer, J., Moreno, Y., Arenas, A.: Modeling abnormal priming in Alzheimer’s patients with a free association network. PloS One 6(8), 22651 (2011)CrossRefGoogle Scholar
  11. [Brin and Page(1998)]
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems 30, 107–117 (1998)CrossRefGoogle Scholar
  12. [Callaway et al.(2001)]
    Callaway, D.S., Hopcroft, J.E., Kleinberg, J.M., Newman, M.E.J., Strogatz, S.H.: Are randomly grown graphs really random? Physical Review E: Statistical, Nonlinear, and Soft Matter Physics 64, 041902 (2001)Google Scholar
  13. [Chan and Vitevitch(2010)]
    Chan, K.Y., Vitevitch, M.S.: Network structure influences speech production. Cognitive Science 34(4), 685–697 (2010)CrossRefGoogle Scholar
  14. [Chan and Vitevitch(2009)]
    Chan, K.Y., Vitevitch, M.S.: The Influence of the Phonological Neighborhood Clustering-Coefficient on Spoken Word Recognition. Journal of Experimental Psychology: Human Perception and Performance 35(6), 1934–1949 (2009)Google Scholar
  15. [Clauset et al.(2009)]
    Clauset, A., Shalizi, C.R., Newman, M.E.J.: Power-Law Distributions in Empirical Data. SIAM Review 51(4), 661–703 (2009)MathSciNetCrossRefGoogle Scholar
  16. [Cluff and Luce(1990)]
    Cluff, M.S., Luce, P.A.: Similarity neighborhoods of spoken two-syllable words: Retroactive effects on multiple activation. Journal of Experimental Psychology: Human Perception and Performance 16, 551–563 (1990)Google Scholar
  17. [Collins and Loftus(1975)]
    Collins, A.M., Loftus, E.F.: A Spreading-Activation Theory of Semantic Processing. Psychological Review 82(6), 407–428 (1975)CrossRefGoogle Scholar
  18. [Collins and Quillian(1969)]
    Collins, A.M., Quillian, M.R.: Retrieval Time from Semantic Memory. Journal of Verbal Learning and Verbal Behavior 247, 240–247 (1969)CrossRefGoogle Scholar
  19. [Dale and Fenson(1996)]
    Dale, P.S., Fenson, L.: Lexical development norms for young children. Behavior Research Methods, Instruments and Computers 28, 125–127 (1996)CrossRefGoogle Scholar
  20. [Ellis and Morrison(1998)]
    Ellis, A.W., Morrison, C.M.: Real age of acquisition effects in lexical retrieval. Journal of Experimental Psychology: Learning Memory and Cognition 24, 515–523 (1998)Google Scholar
  21. [Goñi et al.(2011)]
    Goñi, J., Arrondo, G., Sepulcre, J., Martincorena, I., de Mendizábal, N.V., Corominas-Murtra, B., Bejarano, B., Ardanza-Trevijano, S., Peraita, H., Wall, D.P., Villoslada, P.: The semantic organization of the animal category: evidence from semantic verbal fluency and network theory. Cognitive Processing 12(2), 183–196 (2011)CrossRefGoogle Scholar
  22. [Griffiths et al.(2007)]
    Griffiths, T.L., Steyvers, M., Firl, A.: Google and the mind: predicting fluency with PageRank. Psychological Science 18(12), 1069–1076 (2007)CrossRefGoogle Scholar
  23. [Gruenenfelder and Pisoni(2009)]
    Gruenenfelder, T.M., Pisoni, D.B.: The Lexical Restructuring Hypothesis and Graph Theoretic Analyses of Networks Based on Random Lexicons. Journal of Speech, Language and Hearing Research 52(3), 596–609 (2009)CrossRefGoogle Scholar
  24. [Harley(1993)]
    Harley, T.A.: Phonological activation of semantic competitors during lexical acces in speech production. Language and Cognitive Processes 8, 291–309 (1993)CrossRefGoogle Scholar
  25. [Hills et al.(2010)]
    Hills, T.T., Maouene, J., Riordan, B., Smith, L.B.: The Associative Structure of Language: Contextual Diversity in Early Word Learning. Journal of Memory and Language 63(3), 259–273 (2010)CrossRefGoogle Scholar
  26. [Hills et al.(2009a)]
    Hills, T.T., Maouene, M., Maouene, J., Sheya, A., Smith, L.: Categorical structure among shared features in networks of earlylearned nouns. Cognition 112(3), 381–396 (2009a)CrossRefGoogle Scholar
  27. [Hills et al.(2009b)]
    Hills, T.T., Maouene, M., Maouene, J., Sheya, A., Smith, L.: Longitudinal analysis of early semantic networks: preferential attachment or preferential acquisition? Psychological Science 20(6), 729–739 (2009b)CrossRefGoogle Scholar
  28. [Ke and Yao(2008)]
    Ke, J., Yao, Y.: Analyzing language development from a network approach. Journal of Quantitative Linguistics, 1–22 (2008)Google Scholar
  29. [Lerner et al.(2009)]
    Lerner, A.J., Ogrocki, P.K., Thomas, P.J.: Network graph analysis of category fluency testing. Cognitive and Behavioral Neurology 22(1), 45–52 (2009)CrossRefGoogle Scholar
  30. [Luce and Pisoni(1998)]
    Luce, P.A., Pisoni, D.B.: Recognizing Spoken Words: The Neighborhood Activation Model. Ear and Hearing 19(1), 1–36 (1998)CrossRefGoogle Scholar
  31. [Metsala and Walley(1998)]
    Metsala, J.L., Walley, A.C.: Spoken vocabulary growth and segmental restructuring of lexical representations: Precursors to phonemic awareness and early reading ability. In: Word Recognition in Beginning Literacy, vol. 4, pp. 89–120. Erlbaum, Mahwah (1998)Google Scholar
  32. [Milgram(1967)]
    Milgram, S.: The small-world problem. Psychology Today 2, 60–67 (1967)Google Scholar
  33. [Montoya and Solé(2002)]
    Montoya, J.M., Solé, R.V.: Small world patterns in food webs. Journal of Theoretical Biology 214, 405–412 (2002)CrossRefGoogle Scholar
  34. [Moore and Golledge(1967)]
    Moore, G.T., Golledge, R.G.: Environmental knowing. Hutchinson and Ross, Strodsberg (1967)Google Scholar
  35. [Nelson et al.(1999)]
    Nelson, D.L., McEvoy, C.L., Schrieber, T.A.: The University of South Florida Word Association Norms (1999)Google Scholar
  36. [Quillian(1967)]
    Quillian, M.R.: Word concepts: A theory and simulation of some basic semantic capabilities. Behavioral Science 12, 410–430 (1967)CrossRefGoogle Scholar
  37. [Quillian(1969)]
    Quillian, M.R.: The teachable language comprehender: a simulation program and theory of language. Communications of the ACM (1969)Google Scholar
  38. [Seidenberg and McClelland(1989)]
    Seidenberg, M.S., McClelland, J.L.: A Distributed, Developmental Model of Word Recognition and Naming. Psychological Review (1989)Google Scholar
  39. [Solé et al.(2010)]
    Solé, R.V., Corominas-Murtra, B., Valverde, S., Steels, L.: Language networks: Their structure, function, and evolution. Complexity 22, 1–9 (2010)Google Scholar
  40. [Stamer and Vitevitch(2011)]
    Stamer, M.K., Vitevitch, M.S.: Phonological similarity influences word learning in adults learning Spanish as a foreign language. Bilingualism: Language and Cognition 15(03), 490–502 (2011)CrossRefGoogle Scholar
  41. [Steyvers and Tenenbaum(2005)]
    Steyvers, M., Tenenbaum, J.B.: The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth. Cognitive Science, 3–27 (2005)Google Scholar
  42. [Storkel(2009)]
    Storkel, H.L.: Developmental differences in the effects of phonological, lexical and semantic variables on word learning by infants. Journal of Child Language 36, 291–321 (2009)CrossRefGoogle Scholar
  43. [Storkel(2004)]
    Storkel, H.L.: Do children acquire dense neighborhoods? An investigation of similarity neighborhoods in lexical acquisition. Applied Psycholinguistics 25(2), 201–221 (2004)CrossRefGoogle Scholar
  44. [Sudarshan Iyengar et al.(2012)]
    Sudarshan Iyengar, S.R., Veni Madhavan, C.E., Zweig, K.A., Natarajan, A.: Understanding human navigation using network analysis. Topics in Cognitive Science 4(1), 121–134 (2012)CrossRefGoogle Scholar
  45. [Theakston et al.(2001)]
    Theakston, A.L., Lieven, E.V.M., Pine, J.M., Rowland, C.F.: The role of performance limitations in acquisition of verb-argument structure: an alternative account. Journal of Child Language 28, 127–152 (2001)CrossRefGoogle Scholar
  46. [Vitevitch(2008)]
    Vitevitch, M.S.: What Can Graph Theory Tell Us AboutWord Learning and Lexical Retrieval. Journal of Speech, Language and Hearing Research 51, 408–422 (2008)CrossRefGoogle Scholar
  47. [Watts and Strogatz(1998)]
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998)CrossRefGoogle Scholar
  48. [Zipf(1949)]
    Zipf, G.K.: Human Behavior and the Principle of Least Effort. An Introduction to Human Ecology. Addison Wesley, Cambridge (1949)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Colorado BoulderBoulderUSA
  2. 2.Department of Psychology and NeuroscienceUniversity of Colorado BoulderBoulderUSA

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