TY - GEN
T1 - Self and non-self discrimination mechanism based on predictive learning with estimation of uncertainty
AU - Nakajo, Ryoichi
AU - Takahashi, Maasa
AU - Murata, Shingo
AU - Arie, Hiroaki
AU - Ogata, Tetsuya
N1 - Funding Information:
This work was supported by JSPS KAKENHI Grant Numbers 24119003, 15H01710, and 16H05878.
Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Cognitive developmental robotics
KW - Recurrent neural network
KW - Self/non-self cognition
UR - http://www.scopus.com/inward/record.url?scp=84992723857&partnerID=8YFLogxK
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U2 - 10.1007/978-3-319-46681-1_28
DO - 10.1007/978-3-319-46681-1_28
M3 - Conference contribution
AN - SCOPUS:84992723857
SN - 9783319466804
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 228
EP - 235
BT - Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
A2 - Ikeda, Kazushi
A2 - Lee, Minho
A2 - Hirose, Akira
A2 - Ozawa, Seiichi
A2 - Doya, Kenji
A2 - Liu, Derong
PB - Springer Verlag
T2 - 23rd International Conference on Neural Information Processing, ICONIP 2016
Y2 - 16 October 2016 through 21 October 2016
ER -