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Self and Non-self Discrimination Mechanism Based on Predictive Learning with Estimation of Uncertainty

  • Ryoichi Nakajo
  • Maasa Takahashi
  • Shingo Murata
  • Hiroaki Arie
  • Tetsuya OgataEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9950)

Abstract

In this paper, we propose a model that can explain the mechanism of self and non-self discrimination. Infants gradually develop their abilities for self–other cognition through interaction with the environment. Predictive learning has been widely used to explain the mechanism of infants’ development. We hypothesized that infants’ cognitive abilities are developed through predictive learning and the uncertainty estimation of their sensory-motor inputs. We chose a stochastic continuous time recurrent neural network, which is a dynamical neural network model, to predict uncertainties as variances. From the perspective of cognitive developmental robotics, a predictive learning experiment with a robot was performed. The results indicate that training made the robot predict the regions related to its body more easily. We confirmed that self and non-self cognitive abilities might be acquired through predictive learning with uncertainty estimation.

Keywords

Self/non-self cognition Cognitive developmental robotics Recurrent neural network 

Notes

Acknowledgements

This work was supported by JSPS KAKENHI Grant Numbers 24119003, 15H01710, and 16H05878.

References

  1. 1.
    Asada, M., Hosoda, K., Kuniyoshi, Y., Ishiguro, H., Inui, T., Yoshikawa, Y., Ogino, M., Yoshida, C.: Cognitive developmental robotics: a survey. IEEE Trans. Auton. Ment. Dev. 1(1), 12–34 (2009)CrossRefGoogle Scholar
  2. 2.
    Berthier, N.E., Keen, R.: Development of reaching in infancy. Exp. Brain Res. 169(4), 507–518 (2006)CrossRefGoogle Scholar
  3. 3.
    Clark, A.: Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci. 36(3), 181–204 (2013)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Murata, S., Namikawa, J., Arie, H., Sugano, S., Tani, J.: Learning to reproduce fluctuating time series by inferring their time-dependent stochastic properties: application in robot learning via tutoring. IEEE Trans. Auton. Ment. Dev. 5, 298–310 (2013)CrossRefGoogle Scholar
  5. 5.
    Nagai, Y., Kawai, Y., Asada, M.: Emergence of mirror neuron system: immature vision leads to self-other correspondence. In: Proceedings of the 1st Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (2011)Google Scholar
  6. 6.
    Nishide, S., Nobuta, H., Okuno, H.G., Ogata, T.: Preferential training of neurodynamical model based on predictability of target dynamics. Adv. Robot. 29(9), 587–596 (2015)CrossRefGoogle Scholar
  7. 7.
    Premack, D., Woodruff, G.: Does the chimpanzee have a theory of mind? Behav. Brain Sci. 1, 515–526 (1978)CrossRefGoogle Scholar
  8. 8.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Chap. 8, pp. 318–362. MIT Press, Cambridge (1986)Google Scholar
  9. 9.
    Thelen, E.: Rhythmical stereotypies in normal human infants. Anim. Behav. 27(3), 699–715 (1996)Google Scholar
  10. 10.
    White, B.L.: The New First Three Years of Life. Touchstone, New York (1995)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Ryoichi Nakajo
    • 1
  • Maasa Takahashi
    • 1
  • Shingo Murata
    • 2
  • Hiroaki Arie
    • 2
  • Tetsuya Ogata
    • 1
    Email author
  1. 1.Department of Intermedia Art and ScienceWaseda UniversityTokyoJapan
  2. 2.Department of Modern Mechanical EngineeringWaseda UniversityTokyoJapan

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