Modeling Language Change: The Pitfall of Grammaticalization

  • Quentin Feltgen
  • Benjamin Fagard
  • Jean-Pierre Nadal
Part of the Lecture Notes in Morphogenesis book series (LECTMORPH)


Language evolution is the subject of various theoretical studies, following two main paths: one, where language is viewed as a code between meanings and forms to express them, with a focus on language as a social convention; and the other defining language as a set of grammatical rules governing the production of utterances, evolution being the outcome of mistakes in the acquisition process. We claim that none of the current models provides a satisfactory account of the grammaticalization phenomenon, a linguistic process by which words acquire a grammatical status. We argue that this limitation is mainly due to the way these models represent language and communication mechanisms. We therefore introduce a new framework, the “grammatheme,” as a tool which formalizes in an unambiguous way different concepts and mechanisms involved in grammaticalization. The model especially includes an inference mechanism triggering new grammaticalization processes. We present promising preliminary results of a numerical implementation and discuss a possible research program based on this framework.


Language Evolution Naming Game Conceptual Link Language Change Linguistic Phenomenon 
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.


  1. ATILF-CNRS, Université de Lorraine. (2014). Base textuelle FRANTEXT [WWW Document]. URL
  2. Baronchelli, A., Felici, M., Loreto, V., Caglioti, E., Steels, L. (2006). Sharp transition towards shared vocabularies in multi-agent systems. Journal of Statistical Mechanics: Theory and Experiment, 2006, P06014.Google Scholar
  3. Baronchelli, A., Loreto, V., & Steels, L. (2008). In-depth analysis of the naming game dynamics: The homogeneous mixing case. International Journal of Modern Physics C, 19, 785–812.CrossRefzbMATHGoogle Scholar
  4. Baxter, G. J., Blythe, R. A., Croft, W., & McKane, A. J. (2006). Utterance selection model of language change. Physical Review E, 73, 046118.CrossRefGoogle Scholar
  5. Biber, D., Conrad, S., & Reppen, R. (1994). Corpus-based approaches to issues in applied linguistics. Applied Linguistics, 15, 169–189.CrossRefGoogle Scholar
  6. Bybee, J. L. (2003). Phonology and language use. Cambridge: Cambridge University Press.Google Scholar
  7. Bybee, J. L. (2006). From usage to grammar: The mind’s response to repetition. Language, 82, 711–733.CrossRefGoogle Scholar
  8. Bybee, J. L., & Dahl, O. (1989). The creation of tense and aspect systems in the languages of the world. Studies in Language, 13, 51–103.CrossRefGoogle Scholar
  9. Carlier, A. (2007). From preposition to article: The grammaticalization of the French partitive. Studies in Language, 31, 1–49.CrossRefGoogle Scholar
  10. Castellano, C., Fortunato, S., & Loreto, V. (2009). Statistical physics of social dynamics. Reviews of Modern Physics, 81, 591–646.CrossRefGoogle Scholar
  11. Castelló, X., Baronchelli, A., & Loreto, V. (2009). Consensus and ordering in language dynamics. European Physical Journal B: Condensed Matter and Complex Systems, 71, 557–564.CrossRefzbMATHGoogle Scholar
  12. Chomsky, N. (1981). Lectures on government and binding. Dordrecht: Foris Publications.Google Scholar
  13. Christiansen, M. H., & Kirby, S. (2003). Language evolution: Consensus and controversies. Trends in Cognitive Sciences, 7, 300–307.CrossRefGoogle Scholar
  14. Croft, W. (2013). Evolution: Language use and the evolution of languages. In P.-M. Binder & K. Smith (Eds.), The language phenomenon, The Frontiers Collection (pp. 93–120). Berlin: Springer.CrossRefGoogle Scholar
  15. Cucker, F., Smale, S., & Zhou, D.-X. (2004). Modeling language evolution. Foundations of Computational Mathematics, 4, 315–343.MathSciNetCrossRefzbMATHGoogle Scholar
  16. Diessel, H. (2007). Frequency effects in language acquisition, language use, and diachronic change. New Ideas Psychology Modern Approaches to Language, 25, 108–127.CrossRefGoogle Scholar
  17. Ellis, N. C. (2008). The periphery and the heart of language. In S. Granger & F. Meunier (Eds.), Phraseology: An interdisciplinary perspective (pp. 1–13).Google Scholar
  18. Fagard, B., & Combettes, B. (2013). De en à dans, un simple remplacement? Une étude diachronique. Langue Française, 178, 93.CrossRefGoogle Scholar
  19. Feltgen, Q., Fagard, B., & Nadal, J.-P. (2016). Représentation du langage et modèles d’évolution linguistique: la grammaticalisation comme perspective. TAL, 55(3), 47–71.Google Scholar
  20. Feltgen, Q., Fagard, B., & Nadal, J.-P. (in preparation).Google Scholar
  21. Fitch, W. T. (2005). The evolution of language: A comparative review. Biology and Philosophy, 20, 193–203.CrossRefGoogle Scholar
  22. Fortuny, J., & Corominas-Murtra, B. (2013). On the origin of ambiguity in efficient communication. Journal of Logic, Language and Information, 22, 249–267.MathSciNetCrossRefzbMATHGoogle Scholar
  23. Gómez, J.-C. (2007). Pointing behaviors in apes and human infants: A balanced interpretation. Child Development, 78, 729–734.CrossRefGoogle Scholar
  24. Heine, B. (2003). Grammaticalization. In Joseph, B. D. & Janda, R. D. (Eds.), The handbook of historical linguistics (pp. 573–601). Oxford: Blackwell Publishing Ltd.Google Scholar
  25. Hopper, P. J., & Traugott, E. C. (2003). Grammaticalization. Cambridge: Cambridge University Press.Google Scholar
  26. John, T., & Bennett, A. (1982). Language as a self-organizing system. Cybernetics and System, 13, 201–212.CrossRefGoogle Scholar
  27. Keller, R. (1989). Invisible-hand theory and language evolution. Lingua, 77, 113–127.CrossRefGoogle Scholar
  28. Keller, R. (2005). On language change: The invisible hand in language. New York: Routledge.Google Scholar
  29. Kirby, S. (2001). Spontaneous evolution of linguistic structure-an iterated learning model of the emergence of regularity and irregularity. IEEE Transactions on Evolutionary Computation, 5, 102–110.CrossRefGoogle Scholar
  30. Kirby, S., & Hurford, J. R. (2002). The Emergence of Linguistic Structure: An Overview of the Iterated Learning Model. In A. C. Laurea & D. P. L. Laurea (Eds.), Simulating the Evolution of Language (pp. 121–147). London: Springer.Google Scholar
  31. Komarova, N. L., & Jameson, K. A. (2008). Population heterogeneity and color stimulus heterogeneity in agent-based color categorization. Journal of Theoretical Biology, 253, 680–700.MathSciNetCrossRefGoogle Scholar
  32. Kroch, A. S. (1989). Reflexes of grammar in patterns of language change. Language Variation and Change, 1, 199–244.CrossRefGoogle Scholar
  33. Leavens, D. A., Hopkins, W. D., & Bard, K. A. (1996). Indexical and referential pointing in chimpanzees (Pan troglodytes). Journal of Comparative Psychology, 110, 346–353.CrossRefGoogle Scholar
  34. Loreto, V., Baronchelli, A., Mukherjee, A., Puglisi, A., & Tria, F. (2011). Statistical physics of language dynamics. Journal of Statistical Mechanics: Theory and Experiment, 2011, P04006.CrossRefGoogle Scholar
  35. Meillet, A. (1912). L’évolution des formes grammaticales, Zanichelli.Google Scholar
  36. Mukherjee, A., Tria, F., Baronchelli, A., Puglisi, A., Loreto, V. (2011). Aging in language dynamics. PLoS ONE, 6, e16677.Google Scholar
  37. Nicolle, S. (1998). A relevance theory perspective on grammaticalization. Cognitive Linguistics, 9, 1–36.CrossRefGoogle Scholar
  38. Niyogi, P. (2009). The computational nature of language learning and evolution. Cambridge, Massachusetts: MIT Press.Google Scholar
  39. Niyogi, P., & Berwick, R. C. (1996). A language learning model for finite parameter spaces. Cognition, Compositional Language Acquisition, 61, 161–193.Google Scholar
  40. Nowak, M. A., Komarova, N. L., & Niyogi, P. (2001). Evolution of universal grammar. Science, 291, 114–118.MathSciNetCrossRefzbMATHGoogle Scholar
  41. Nowak, M. A., & Krakauer, D. C. (1999). The evolution of language. Proceedings of National Academy of Sciences, 96, 8028–8033.CrossRefGoogle Scholar
  42. Ogura, M., & Wang, W. S.-Y. (1996). Snowball Effect in Lexical Diffusion, English Historical Linguistics 1994: Papers from the 8th International Conference on English Historical Linguistic (8. ICEHL, Edinburgh, 19–23 September 1994).Google Scholar
  43. Omodei, E., & Fagard, B. (2013). Cases, Prepositions, and In-Betweens: Sketching a Model of Grammatical Evolution. In European Conference on Complex Systems (ECCS, Barcelona, 16–20 September 2013).Google Scholar
  44. Nadal, J.-P. & Pierrehumbert, J. B. (unpublished).Google Scholar
  45. Pawlowitsch, C. (2007). Finite populations choose an optimal language. Journal of Theoretical Biology, 249, 606–616.MathSciNetCrossRefGoogle Scholar
  46. Pawlowitsch, C., Mertikopoulos, P., & Ritt, N. (2011). Neutral stability, drift, and the diversification of languages. Journal of Theoretical Biology, 287, 1–12.MathSciNetCrossRefGoogle Scholar
  47. Peyraube, A. (2002). L’évolution des structures grammaticales. Langages, 1, 46–58.Google Scholar
  48. Pierrehumbert, J. B. (2001), Exemplar dynamics: Word frequency, lenition and contrast. In Bybee, J. L. & Hopper, P. J. (Eds.), Frequency and the emergence of linguistic structure (pp. 153–157). Amsterdam: John Benjamins.Google Scholar
  49. Prévost, S. (2003). La grammaticalisation: unidirectionnalité et statut. Français moderne, 71(2), 144–166.Google Scholar
  50. Puglisi, A., Baronchelli, A., & Loreto, V. (2008). Cultural route to the emergence of linguistic categories. Proceedings of National Academy of Sciences, 105, 7936–7940.CrossRefGoogle Scholar
  51. Reali, F., & Griffiths, T. L. (2010). Words as alleles: connecting language evolution with Bayesian learners to models of genetic drift. Proceedings of the Royal Society of London B: Biological Sciences, 277, 429–436.CrossRefGoogle Scholar
  52. Schulze, C., & Stauffer, D. (2005). Monte Carlo simulation and the rise and fall of languages. International Journal of Modern Physics C, 16, 781–787.CrossRefGoogle Scholar
  53. Scott-Phillips, T. C., & Kirby, S. (2010). Language evolution in the laboratory. Trends in Cognitive Sciences, 14, 411–417.CrossRefGoogle Scholar
  54. Sperber, D., & Wilson, D. (1996). Relevance: Communication and Cognition, (2 ed.). Oxford ; Cambridge, MA: Wiley-Blackwell.Google Scholar
  55. Steels, L. (1995). A self-organizing spatial vocabulary. Artificial Life, 2, 319–332.CrossRefGoogle Scholar
  56. Steels, L. (1999). The talking heads experiment, words and meanings. Google Scholar
  57. Steels, L. (2011). Modeling the cultural evolution of language. Physics of Life Reviews, 8(339–356), 4.Google Scholar
  58. Steels, L., & Belpaeme, T. (2005). Coordinating perceptually grounded categories through language: A case study for colour. Behavioral and Brain Sciences, 28, 469–529.Google Scholar
  59. Traugott, E. C., & Dasher, R. B. (2001). Regularity in semantic change. Cambridge: Cambridge University Press.Google Scholar
  60. Victorri, B., & Fuchs, C. (1996). La polysémie - construction dynamique du sens, Langue, raisonnement, calcul. Paris: Hermès Science Publications.Google Scholar
  61. Vogt, P., & De Boer, B. (2010). Language evolution: Computer models for empirical data. Adaptive Behavior: Animals, Animats, Software Agents, Robots, Adaptive Systems, 18, 5–11.CrossRefGoogle Scholar
  62. Wedel, A. B. (2006). Exemplar models, evolution and language change. The Linguistic Review, 23, 247–274.CrossRefGoogle Scholar
  63. Zanette, D. H. (2008). Analytical approaches to bit-string models of language evolution. International Journal of Modern Physics C, 19, 569–581.CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Quentin Feltgen
    • 1
  • Benjamin Fagard
    • 2
  • Jean-Pierre Nadal
    • 1
    • 3
  1. 1.Laboratoire de Physique Statistique, CNRS - Ecole Normale Supérieure - UPMC - Univ.Paris Diderot, PSL Research UniversityParisFrance
  2. 2.Laboratoire Lattice, CNRS, Ecole Normale SupérieureUniversité Sorbonne Nouvelle, PSL Research University, Sorbonne Paris-CitéMontrougeFrance
  3. 3.Centre d’Analyse et de Mathématique Sociales, CNRS-Ecole des Hautes Etudes en Sciences SocialesPSL Research UniversityParisFrance

Personalised recommendations