Pupil Size as Input Data to Distinguish Comprehension State in Auditory Word Association Task Using Machine Learning

  • Kosei Minami
  • Keiichi Watanuki
  • Kazunori Kaede
  • Keiichi Muramatsu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 903)


In communication, it is very important for a speaker to understand the comprehension state of the speaking partner. In this study, the “comprehension state” is defined as whether or not the speaker’s message is clearly understood, which is difficult to accurately evaluate. This study aims to evaluate the comprehension state from the pupil size using machine learning. We conduct a word association task using elements that are similar to those used in conversations and measure the pupil size; this pupil size data is used as input data for machine learning. The results show that high accuracy is achieved by learning the low frequency components of the pupil size.


Pupil size Comprehension state Word association task 


  1. 1.
    Clark, H.H., Schaefer, E.F.: Contributing to discourse. Cogn. Sci. 13, 259–294 (1989)CrossRefGoogle Scholar
  2. 2.
    Bradlay, M.M., Miccoli, L., Escrig, M.A., Lang., P.J.: The pupil as a measure of emotional arousal and autonomic activation. Psychophysiology 45, 602–607 (2008)CrossRefGoogle Scholar
  3. 3.
    Hess, E.H.: Attitude and pupil size. Sci. Am. 212, 46–54 (1965)CrossRefGoogle Scholar
  4. 4.
    Minami, K., Watanuki, K., Kaede, K., Muramatsu, K.: The effects of listener understanding on pupil and blinks. In: Proceedings of the 28th Design and System Division Conference, The Japan Society of Mechanical Engineers, (JSME D&S 2018) (2018). (in Japanese). (in Press)Google Scholar
  5. 5.
    Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)zbMATHGoogle Scholar
  6. 6.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learrn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Shiga, N., Ohkubo, Y.: Pupillary reflex dilatation to the auditory stimuli - the effects of parasympathetic activity on the pattern of the pupillary reflex dilation. Tohoku Psychol. Folia 39, 31–39 (1981)Google Scholar
  8. 8.
    Yao, J.T., Zhao, S.L., Saxton, L.V.: A study on fuzzy intrusion detection. In: Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security, vol. 5812, pp. 23–30. The International Society for Optics and Photonics (2005)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Kosei Minami
    • 1
  • Keiichi Watanuki
    • 1
  • Kazunori Kaede
    • 1
  • Keiichi Muramatsu
    • 1
  1. 1.Graduate School of Science and EngineeringSaitama UniversitySaitamaJapan

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