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)


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.


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



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


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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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