Schemata Learning

Chapter
Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS)

Abstract

This chapter describes a possible brain model that could account for neuronal mechanisms of schemata learning and discusses the results of robotic experiments implemented with the model. We consider a dynamic neural network model which is characterized by their multiple time-scales dynamics. The model assumes that the slow dynamic part corresponding to the premotor cortex interacts with the fast dynamics part corresponding to the inferior parietal lobe (IPL). Using this model, the robotics experiments on developmental tutoring of a set of goal-directed actions were conducted. The results showed that functional hierarchical structures emerge through stages of developments where behavior primitives are generated in the fast dynamics part in earlier stages, and their compositional sequences of achieving goals appear in the slow dynamics part in later stages. It was also observed that motor imagery is generated in earlier stages compared to actual behaviors. We discuss that schemata of goal-directed actions should be acquired with gradual development of the internal image and compositionality for the actions.

Keywords

Manifold Torque Recombination Coherence Explosive 

Notes

Acknowledgements

The authors thank Sony Corporation for providing them with a humanoid robot as a research platform. The study has been partially supported by a Grant-in-Aid for Scientific Research on Priority Areas “Emergence of Adaptive Motor Function through Interaction between Body, Brain and Environment” from the Japanese Ministry of Education, Culture, Sports, Science and Technology.

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Laboratory for Behavior and Dynamic CognitionRIKEN Brain Science InstituteSaitamaJapan

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