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Abstract

Abstract

As a usage-based approach to the study language, cognitive linguistics is theoretically well poised to apply quantitative methods to the analysis of corpus and experimental data. In this article, I review the historical circumstances that led to the quantitative turn in cognitive linguistics and give an overview of statistical models used by cognitive linguists, including chi-square test, Fisher test, Binomial test, t-test, ANOVA, correlation, regression, classification and regression trees, naïve discriminative learning, cluster analysis, multi-dimensional scaling, and correspondence analysis. I stress the essential role of introspection in the design and interpretation of linguistic studies, and assess the pros and cons of the quantitative turn. I also make a case for open access science and appropriate archiving of linguistic data.

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2019-08-20
2023-09-21

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References

  1. Ambridge, B. & Goldberg, A. E.
    (2008) The island status of clausal complements: Evidence in favor of an information structure explanation. Cognitive Linguistics, 19, 357–389. 10.1515/COGL.2008.014
     https://doi.org/10.1515/COGL.2008.014 [Google Scholar]
  2. Baayen, R. H.
    (2008) Analyzing linguistic data: A practical introduction to statistics using R. Cambridge: Cambridge University Press. 10.1017/CBO9780511801686
     https://doi.org/10.1017/CBO9780511801686 [Google Scholar]
  3. (2011) Corpus linguistics and naive discriminative learning. Brazilian Journal of Applied Linguistics, 11, 295–328.
     [Google Scholar]
  4. Baayen, R. H., Milin, P., Filipovic Durdjevic, D., Hendrix, P., & Marelli, M.
    (2011) An amorphous model for morphological processing in visual comprehension based on naive discriminative learning. Psychological Review, 118, 438–482. 10.1037/a0023851
     https://doi.org/10.1037/a0023851 [Google Scholar]
  5. Baayen, R. H., Endresen, A., Janda, L. A., Makarova, A., & Nesset, T.
    (2013) Making choices in Russian: Pros and cons of statistical methods for rival forms. Russian Linguistics, 37(3), 253–291. doi:  10.1007/s11185‑013‑9118‑6
     https://doi.org/10.1007/s11185-013-9118-6 [Google Scholar]
  6. Barth, D., & Kapatsinski, V.
    (2017) A multimodel inference approach to categorical variant choice: Construction, priming and frequency effects on the choice between full and contracted forms of am, are and is. Corpus Linguistics & Linguistic Theory, 13(2), 213–260. doi:  10.1515/cllt‑2014‑0022
     https://doi.org/10.1515/cllt-2014-0022 [Google Scholar]
  7. Butler, C.
    (1985) Statistics in Linguistics. Oxford: Blackwell.
     [Google Scholar]
  8. Bybee, J.
    (2010) Language, usage and cognition. Cambridge: Cambridge University Press. 10.1017/CBO9780511750526
     https://doi.org/10.1017/CBO9780511750526 [Google Scholar]
  9. Carden, G., & Dieterich, T. G.
    (1980) Introspection, observation and experiment: An example where experiment pays off. Journal of the Philosophy of Science Association, 2, 583–597.
     [Google Scholar]
  10. Clancy, S. J.
    (2006) The topology of Slavic case: Semantic maps and multidimensional scaling. Glossos, 7, 1–28. www.seelrc.org/glossos/issues/7/
     [Google Scholar]
  11. Cowart, W.
    (1997) Experimental syntax: Applying objective methods to sentence judgements. Thousand Oaks, CA: Sage Publications.
     [Google Scholar]
  12. Croft, W., & Poole, K. T.
    (2008) Inferring universals from grammatical variation: Multidimensional scaling for typological analysis. Theoretical Linguistics, 34, 1–37. 10.1515/THLI.2008.001
     https://doi.org/10.1515/THLI.2008.001 [Google Scholar]
  13. Dąbrowska, E.
    (2010) Naive v. expert competence: An empirical study of speaker intuitions. The Linguistic Review, 27, 1–23. 10.1515/tlir.2010.001
     https://doi.org/10.1515/tlir.2010.001 [Google Scholar]
  14. (2012) Different speakers, different grammars: Individual differences in native language attainment. Linguistic Approaches to Bilingualism, 2, 219–253. 10.1075/lab.2.3.01dab
     https://doi.org/10.1075/lab.2.3.01dab [Google Scholar]
  15. Dąbrowska, E., Rowland, C., & Theakston, A.
    (2009) The acquisition of questions with long-distance dependencies. Cognitive Linguistics, 20, 571–597. 10.1515/COGL.2009.025
     https://doi.org/10.1515/COGL.2009.025 [Google Scholar]
  16. Delbecque, N.
    (1990) Word order as a reflection of alternate conceptual construals in French and Spanish: Similarities and divergences in adjective position. Cognitive Linguistics, 1, 349–416. 10.1515/cogl.1990.1.4.349
     https://doi.org/10.1515/cogl.1990.1.4.349 [Google Scholar]
  17. Diessel, H.
    (2008) Iconicity of sequence: A corpus-based analysis of the positioning of temporal adverbial clauses in English. Cognitive Linguistics, 19, 465–490. 10.1515/COGL.2008.018
     https://doi.org/10.1515/COGL.2008.018 [Google Scholar]
  18. Eckhoff, H. M., & Janda, L. A.
    (2014) Grammatical profiles and aspect in Old Church Slavonic. Transactions of the Philological Society, 112(2), 231–258. doi:  10.1111/1467‑968X.12012
     https://doi.org/10.1111/1467-968X.12012 [Google Scholar]
  19. Falck, M. J., & Gibbs, R. W.
    (2012) Embodied motivations for metaphorical meanings. Cognitive Linguistics, 23, 251–272.
     [Google Scholar]
  20. Feynman, R.
    (1992) Surely you’re joking, Mr. Feynman!London: Vintage.
     [Google Scholar]
  21. Gibbs, R. W.
    (1990) Psycholinguistic studies on the conceptual basis of idiomaticity. Cognitive Linguistics, 1, 417–452. 10.1515/cogl.1990.1.4.417
     https://doi.org/10.1515/cogl.1990.1.4.417 [Google Scholar]
  22. (2006) Introspection and cognitive linguistics: Should we trust our own intuitions?Annual Review of Cognitive Linguistics, 4, 135–151. 10.1075/arcl.4.06gib
     https://doi.org/10.1075/arcl.4.06gib [Google Scholar]
  23. Glynn, D.
    (2010) Corpus-driven Cognitive Semantics: Introduction to the field. InD. Glynn & K. Fischer (Eds.), Quantitative methods in Cognitive Semantics: Corpus-driven approaches (pp.1–42). Berlin: Mouton de Gruyter. 10.1515/9783110226423.1
     https://doi.org/10.1515/9783110226423.1 [Google Scholar]
  24. Goldberg, A. E.
    (2011) Corpus evidence of the viability of statistical preemption. Cognitive Linguistics, 22, 131–153. 10.1515/cogl.2011.006
     https://doi.org/10.1515/cogl.2011.006 [Google Scholar]
  25. Goossens, L.
    (1990) Metaphtonymy: The interaction of metaphor and metonymy in expressions for linguistic action. Cognitive Linguistics, 1, 323–342. 10.1515/cogl.1990.1.3.323
     https://doi.org/10.1515/cogl.1990.1.3.323 [Google Scholar]
  26. Gries, S. T.
    (2009) What is corpus linguistics?Language and Linguistics Compass, 3, 1–17. 10.1111/j.1749‑818X.2009.00149.x
     https://doi.org/10.1111/j.1749-818X.2009.00149.x [Google Scholar]
  27. (2011) Phonological similarity in multi-word units. Cognitive Linguistics, 22, 491–510. 10.1515/cogl.2011.019
     https://doi.org/10.1515/cogl.2011.019 [Google Scholar]
  28. (2013) Statistics for Linguistics with R. Berlin: Mouton de Gruyter. 10.1515/9783110307474
     https://doi.org/10.1515/9783110307474 [Google Scholar]
  29. (2014) Frequencies, probabilities, and association measures in usage-/exemplar-based linguistics: Some necessary clarifications. InN. B. Gisborne & W. Hollmann (Eds.), Theory and data in Cognitive Linguistics (pp.15–49). Amsterdam: John Benjamins. 10.1075/bct.67.02gri
     https://doi.org/10.1075/bct.67.02gri [Google Scholar]
  30. (2015) More (old and new) misunderstandings of collostructional analysis: On Schmid & Küchenhoff (2013). Cognitive Linguistics, 26, 505–536. 10.1515/cog‑2014‑0092
     https://doi.org/10.1515/cog-2014-0092 [Google Scholar]
  31. Günter, F.
    (2014) Form, meaning and cognition: Language- and speaker-specific variation in linguistic and non-linguistic forms of interaction with spatial scenes. PhD Dissertation, Ludwig-Maximilians-University.
  32. Janda, L. A.
    (2009) What is the role of semantic maps in cognitive linguistics?InP. Stalmaszczyk & W. Oleksy (Eds.), Cognitive approaches to language and linguistic data: Studies in honor of Barbara Lewandowska-Tomaszczyk (pp.105–124). Frankfurt am Main: Peter Lang.
     [Google Scholar]
  33. (2013) Quantitative methods in Cognitive Linguistics. InL. A. Janda (Ed.), Cognitive Linguistics: The quantitative turn. The Essential Reader (pp.1–32). Berlin: De Gruyter Mouton. 10.1515/9783110335255.1
     https://doi.org/10.1515/9783110335255.1 [Google Scholar]
  34. (2017) The quantitative turn. InB. Dancygier (Ed.), The Cambridge handbook of Cognitive Linguistics (pp.498–514). Cambridge: Cambridge University Press. 10.1017/9781316339732.032
     https://doi.org/10.1017/9781316339732.032 [Google Scholar]
  35. Janda, L. A., Nesset, T., & Baayen, R. H.
    (2010) Capturing correlational structure in Russian paradigms: A case study in logistic mixed-effects modeling. Corpus Linguistics and Linguistic Theory, 6, 29–48. 10.1515/cllt.2010.002
     https://doi.org/10.1515/cllt.2010.002 [Google Scholar]
  36. Janda, L. A., & Solovyev, V. D.
    (2009) What constructional profiles reveal about synonymy: A case study of Russian words for sadness and happiness. Cognitive Linguistics, 20, 367–393. 10.1515/COGL.2009.018
     https://doi.org/10.1515/COGL.2009.018 [Google Scholar]
  37. Johnson, K.
    (2008) Quantitative methods in linguistics. Malden, MA: Blackwell.
     [Google Scholar]
  38. Joseph, B.
    (2004) On change in Language and change in language. Language, 80, 381–383. 10.1353/lan.2004.0132
     https://doi.org/10.1353/lan.2004.0132 [Google Scholar]
  39. Kapatsinski, V.
    (2013) Conspiring to mean: Experimental and computational evidence for a usage-based harmonic approach to morphophonology. Language, 89, 110–148. 10.1353/lan.2013.0003
     https://doi.org/10.1353/lan.2013.0003 [Google Scholar]
  40. Kelly, K.
    (2010) What technology wants. New York: Viking Press.
     [Google Scholar]
  41. Kilgarriff, A.
    (2005) Language is never, ever, ever random. Corpus Linguistics and Linguistic Theory, 1, 263–276. 10.1515/cllt.2005.1.2.263
     https://doi.org/10.1515/cllt.2005.1.2.263 [Google Scholar]
  42. Kraska-Szlenk, I., & Żygis, M.
    (2012) Phonetic and lexical gradience in Polish prefixed words. Cognitive Linguistics, 23, 317–366. 10.1515/cog‑2012‑0010
     https://doi.org/10.1515/cog-2012-0010 [Google Scholar]
  43. Küchenhoff, H., & Schmid, H. J.
    (2015) Reply to “More (old and new) misunderstandings of collostructional analysis: On Schmid & Küchenhoff” by S. T. Gries. Cognitive Linguistics, 26, 537–547. 10.1515/cog‑2015‑0053
     https://doi.org/10.1515/cog-2015-0053 [Google Scholar]
  44. Ladd, D. R., Roberts, S. G., & Dediu, D.
    (2015) Correlational studies in typological and historical linguistics. Annual Review of Linguistics, 1, 221–241. 10.1146/annurev‑linguist‑030514‑124819
     https://doi.org/10.1146/annurev-linguist-030514-124819 [Google Scholar]
  45. Langacker, R. W.
    (1987) Foundations of cognitive grammar. vol.1: Theoretical prerequisites. Stanford: Stanford University Press.
     [Google Scholar]
  46. (2013) Essentials of cognitive grammar: A basic introduction. Oxford: Oxford University Press.
     [Google Scholar]
  47. Larson-Hall, J.
    (2010) A guide to doing statistics in second language research using SPSS. New York: Routledge.
     [Google Scholar]
  48. Levshina, N.
    (2015) How to do linguistics with R. Amsterdam: John Benjamins. 10.1075/z.195
     https://doi.org/10.1075/z.195 [Google Scholar]
  49. Nesset, T., & Janda, L. A.
    (2010) Paradigm structure: Evidence from Russian suffix shift. Cognitive Linguistics, 21, 699–725. 10.1515/cogl.2010.022
     https://doi.org/10.1515/cogl.2010.022 [Google Scholar]
  50. R Development Core Team
    R Development Core Team (2010) R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.
     [Google Scholar]
  51. Roberts, S., & Winters, J.
    (2013) Linguistic diversity and traffic accidents: Lessons from statistical studies of cultural traits. PLOSone, 8(8), e70902. 10.1371/journal.pone.0070902
     https://doi.org/10.1371/journal.pone.0070902 [Google Scholar]
  52. Schmid, H. J., & Küchenhoff, H.
    (2013) Collostructional analysis and other ways of measuring lexicogrammatical attraction: Theoretical premises, practical problems and cognitive underpinnings. Cognitive Linguistics, 24, 531–577. 10.1515/cog‑2013‑0018
     https://doi.org/10.1515/cog-2013-0018 [Google Scholar]
  53. Stefanowitsch, A.
    (2011a) Constructional preemption by contextual mismatch: A corpus-linguistic investigation. Cognitive Linguistics, 22, 107–129. 10.1515/cogl.2011.005
     https://doi.org/10.1515/cogl.2011.005 [Google Scholar]
  54. (2011b) Cognitive linguistics as a cognitive science. InM. Callies, W. R. Keller, & A. Lohöfer (Eds.), Bi-directionality in the cognitive sciences (pp.296–309). Amsterdam/Philadelphia: John Benjamins. 10.1075/hcp.30.18ste
     https://doi.org/10.1075/hcp.30.18ste [Google Scholar]
  55. (2013) Collostructional analysis. InT. Hoffmann & G. Trousdale (Eds.), The Oxford handbook of Construction Grammar (pp.290–306). Oxford: Oxford University Press.
     [Google Scholar]
  56. Stefanowitsch, A., & Gries, S. T.
    (2003) Collostructions: Investigating the interaction between words and constructions. International Journal of Corpus Linguistics, 8, 209–243. 10.1075/ijcl.8.2.03ste
     https://doi.org/10.1075/ijcl.8.2.03ste [Google Scholar]
  57. (2005) Covarying collexemes. Corpus Linguistics and Linguistic Theory, 1, 1–43. 10.1515/cllt.2005.1.1.1
     https://doi.org/10.1515/cllt.2005.1.1.1 [Google Scholar]
  58. Strobl, C., Tutz, G., & Malley, J.
    (2009) An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychological Methods, 14, 323–348. 10.1037/a0016973
     https://doi.org/10.1037/a0016973 [Google Scholar]
  59. Theakston, A. L., Maslen, R., Lieven, E. V. M., & Tomasello, M.
    (2012) The acquisition of the active transitive construction in English: A detailed jyoti case study. Cognitive Linguistics, 23, 91–128. 10.1515/cog‑2012‑0004
     https://doi.org/10.1515/cog-2012-0004 [Google Scholar]
  60. Zenner, E., Speelman, D., & Geeraerts, D.
    (2012) Cognitive Sociolinguistics meets loanword research: Measuring variation in the success of anglicisms in Dutch. Cognitive Linguistics, 23, 749–792. 10.1515/cog‑2012‑0023
     https://doi.org/10.1515/cog-2012-0023 [Google Scholar]
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Quantitative perspectives in Cognitive Linguistics
Review of Cognitive Linguistics. Published under the auspices of the Spanish Cognitive Linguistics Association 17, 7 (2019); https://doi.org/10.1075/rcl.00024.jan
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  • Article Type: Research Article
Keyword(s): corpus data; experimental data; quantitative methods; statistical models; usage-based approach

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