Learning to Perceive the World as Probabilistic or Deterministic via Interaction with Others: A Neuro-Robotics Experiment

Shingo Murata, Yuichi Yamashita, Hiroaki Arie, Tetsuya Ogata, Shigeki Sugano, Jun Tani

    Research output: Contribution to journalArticle

    14 Citations (Scopus)

    Abstract

    We suggest that different behavior generation schemes, such as sensory reflex behavior and intentional proactive behavior, can be developed by a newly proposed dynamic neural network model, named stochastic multiple timescale recurrent neural network (S-MTRNN). The model learns to predict subsequent sensory inputs, generating both their means and their uncertainty levels in terms of variance (or inverse precision) by utilizing its multiple timescale property. This model was employed in robotics learning experiments in which one robot controlled by the S-MTRNN was required to interact with another robot under the condition of uncertainty about the other's behavior. The experimental results show that self-organized and sensory reflex behavior-based on probabilistic prediction-emerges when learning proceeds without a precise specification of initial conditions. In contrast, intentional proactive behavior with deterministic predictions emerges when precise initial conditions are available. The results also showed that, in situations where unanticipated behavior of the other robot was perceived, the behavioral context was revised adequately by adaptation of the internal neural dynamics to respond to sensory inputs during sensory reflex behavior generation. On the other hand, during intentional proactive behavior generation, an error regression scheme by which the internal neural activity was modified in the direction of minimizing prediction errors was needed for adequately revising the behavioral context. These results indicate that two different ways of treating uncertainty about perceptual events in learning, namely, probabilistic modeling and deterministic modeling, contribute to the development of different dynamic neuronal structures governing the two types of behavior generation schemes.

    Original languageEnglish
    Pages (from-to)830-848
    Number of pages19
    JournalIEEE Transactions on Neural Networks and Learning Systems
    Volume28
    Issue number4
    DOIs
    Publication statusPublished - 2017 Apr 1

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    Keywords

    • Generative model
    • neuro-robotics
    • precision
    • prediction error
    • predictive coding
    • recurrent neural network (RNN)

    ASJC Scopus subject areas

    • Software
    • Computer Science Applications
    • Computer Networks and Communications
    • Artificial Intelligence

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