Self and non-self discrimination mechanism based on predictive learning with estimation of uncertainty

Ryoichi Nakajo, Maasa Takahashi, Shingo Murata, Hiroaki Arie, Tetsuya Ogata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationNeural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
EditorsKazushi Ikeda, Minho Lee, Akira Hirose, Seiichi Ozawa, Kenji Doya, Derong Liu
PublisherSpringer Verlag
Pages228-235
Number of pages8
ISBN (Print)9783319466804
DOIs
Publication statusPublished - 2016 Jan 1
Externally publishedYes
Event23rd International Conference on Neural Information Processing, ICONIP 2016 - Kyoto, Japan
Duration: 2016 Oct 162016 Oct 21

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9950 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other23rd International Conference on Neural Information Processing, ICONIP 2016
CountryJapan
CityKyoto
Period16/10/1616/10/21

Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Nakajo, R., Takahashi, M., Murata, S., Arie, H., & Ogata, T. (2016). Self and non-self discrimination mechanism based on predictive learning with estimation of uncertainty. In K. Ikeda, M. Lee, A. Hirose, S. Ozawa, K. Doya, & D. Liu (Eds.), Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings (pp. 228-235). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9950 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46681-1_28