Visual World Paradigm Data: From Preprocessing to Nonlinear Time-Course Analysis

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 73)


The Visual World Paradigm (VWP) is used to study online spoken language processing and produces time-series data. The data present challenges for analysis and they require significant preprocessing and are by nature nonlinear. Here, we discuss VWPre, a new tool for data preprocessing, and generalized additive mixed modeling (GAMM), a relatively new approach for nonlinear time-series analysis (using mgcv and itsadug), which are all available in R. An example application of GAMM using preprocessed data is provided to illustrate its advantages in addressing the issues inherent to other methods, allowing researchers to more fully understand and interpret VWP data.


Visual World Paradigm Generalized Additive Mixed Modeling 


  1. 1.
    Cooper, R.M.: The control of eye fixation by the meaning of spoken language: a new methodology for the real-time investigation of speech perception, memory, and language processing. Cogn. Psychol. 6, 84–107 (1974)CrossRefGoogle Scholar
  2. 2.
    Huettig, F., Rommers, J., Meyer, A.S.: Using the visual world paradigm to study language processing: a review and critical evaluation. Acta Psychol. (Amst)137, 151–171 (2011)CrossRefGoogle Scholar
  3. 3.
    R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna (2016)Google Scholar
  4. 4.
    Porretta, V., Tucker, B.V., Järvikivi, J.: The influence of gradient foreign accentedness and listener experience on word recognition. J. Phonetics 58, 1–21 (2016)CrossRefGoogle Scholar
  5. 5.
    Porretta, V., Kyröläinen, A.-J., van Rij, J., Järvikivi, J.: VWPre: tools for preprocessing visual world data (2016)Google Scholar
  6. 6.
    Wood, S.N.: mgcv: mixed GAM computation vehicle with GCV/AIC/REML smoothness estimation (2016)Google Scholar
  7. 7.
    van Rij, J., Wieling, M., Baayen, R.H., van Rijn, H.: itsadug: interpreting time series and autocorrelated data using GAMMs (2015)Google Scholar
  8. 8.
    Tanenhaus, M.K., Spivey-Knowlton, M.J., Eberhard, K.M., Sedivy, J.E.: Integration of visual and linguistic information in spoken language comprehension. Science 268, 1632–1634 (1995)CrossRefGoogle Scholar
  9. 9.
    Nixon, J.S., van Rij, J., Mok, P., Baayen, R.H., Chen, Y.: The temporal dynamics of perceptual uncertainty: eye movement evidence from Cantonese segment and tone perception. J. Mem. Lang. 90, 103–125 (2016)CrossRefGoogle Scholar
  10. 10.
    Allopenna, P.D., Magnuson, J.S., Tanenhaus, M.K.: Tracking the time course of spoken word recognition using eye movements: evidence for continuous mapping models. J. Mem. Lang. 38, 419–439 (1998)CrossRefGoogle Scholar
  11. 11.
    Chambers, C.G., Tanenhaus, M.K., Magnuson, J.S.: Actions and affordances in syntactic ambiguity resolution. J. Exp. Psychol. Learn. Mem. Cogn. 30, 687–696 (2004)CrossRefGoogle Scholar
  12. 12.
    van Rij, J., Hollebrandse, B., Hendriks, P.: Children’s eye gaze reveals their use of discourse context in object pronoun resolution. In: Holler, A., Goeb, C., Suckow, K. (eds.) Empirical Perspectives on Anaphora Resolution. De Gruyter, Berlin (2016)Google Scholar
  13. 13.
    Kamide, Y., Altmann, G.T.M., Haywood, S.L.: The time-course of prediction in incremental sentence processing: evidence from anticipatory eye movements. J. Mem. Lang. 49, 133–156 (2003)CrossRefGoogle Scholar
  14. 14.
    Järvikivi, J., Pyykkönen-Klauck, P., Schimke, S., Colonna, S., Hemforth, B.: Information structure cues for 4-year-olds and adults: tracking eye movements to visually presented anaphoric referents. Lang. Cogn. Neurosci. 29, 877–892 (2014)CrossRefGoogle Scholar
  15. 15.
    Dussias, P.E., Valdés Kroff, J., Gerfen, C.: Using the visual world to study spoken language processing. In: Jegerski, J., Van Patten, B. (eds.) Research Methods in Second Language Psycholinguistics, pp. 93–126. Routledge, New York (2014)Google Scholar
  16. 16.
    Baayen, R.H., van Rij, J., Cecile, D., Wood, S.N.: Autocorrelated errors in experimental data in the language sciences: some solutions offered by generalized additive mixed models. In: Speelman, D., Heylan, K., Geeraerts, D. (eds.) Mixed Effects Regression Models in Linguistics. Springer, Berlin (2016)Google Scholar
  17. 17.
    Hastie, T.J., Tibshirani, R.J.: Generalized Additive Models. Chapman & Hall/CRC, London (1990)zbMATHGoogle Scholar
  18. 18.
    Wood, S.N.: Generalized Additive Models: An Introduction with R. Chapman & Hall/CRC Press, Boca Raton (2006)zbMATHGoogle Scholar
  19. 19.
    Baayen, R.H., Davidson, D.J., Bates, D.M.: Mixed-effects modeling with crossed random effects for subjects and items. J. Mem. Lang. 59, 390–412 (2008)CrossRefGoogle Scholar
  20. 20.
    Mirman, D., Dixon, J.A., Magnuson, J.S.: Statistical and computational models of the visual world paradigm: Growth curves and individual differences. J. Mem. Lang. 59, 475–494 (2008)CrossRefGoogle Scholar
  21. 21.
    Barr, D.J.: Analyzing “visual world” eyetracking data using multilevel logistic regression. J. Mem. Lang. 59, 457–474 (2008)CrossRefGoogle Scholar
  22. 22.
    Fischer, B.: Saccadic reaction time: Implications for reading, dyslexia, and visual cognition. In: Rayner, K. (ed.) Eye Movements and Visual Cognition, pp. 31–45. Springer, New York (1992)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.University of AlbertaEdmontonCanada
  2. 2.University of TurkuTurkuFinland
  3. 3.University of GroningenGroningenThe Netherlands

Personalised recommendations