Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Clark, H.H., Schaefer, E.F.: Contributing to discourse. Cogn. Sci. 13, 259–294 (1989)
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)
Hess, E.H.: Attitude and pupil size. Sci. Am. 212, 46–54 (1965)
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)
Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Minami, K., Watanuki, K., Kaede, K., Muramatsu, K. (2019). Pupil Size as Input Data to Distinguish Comprehension State in Auditory Word Association Task Using Machine Learning. In: Karwowski, W., Ahram, T. (eds) Intelligent Human Systems Integration 2019. IHSI 2019. Advances in Intelligent Systems and Computing, vol 903. Springer, Cham. https://doi.org/10.1007/978-3-030-11051-2_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-11051-2_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-11050-5
Online ISBN: 978-3-030-11051-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)