Schemata Learning

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


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.


Motor Imagery Humanoid Robot Mirror Neuron Inferior Parietal Lobe Teaching Sequence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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.


  1. .
    Arbib, M.: Perceptual structures and distributed motor control. In: Handbook of physiology: the nervous system, II. motor control, pp. 1448–1480. MIT, Cambridge, MA (1981)Google Scholar
  2. .
    Beer, R.: A dynamical systems perspective on agent-environment interaction. Artificial Intelligence 72(1), 173–215 (1995)CrossRefGoogle Scholar
  3. .
    Billard, A., Mataric, M.: Learning human arm movements by imitation: evaluation of a biologically-inspired connectionist architecture. Robotics and Autonomous Systems 941, 1–16 (2001)Google Scholar
  4. .
    Colby, C., Duhamel, J., Goldberg, M.: Ventral intraparietal area of the macaque: anatomic location and visual response properties. Journal of Neurophysiology 69, 902–914 (1993)PubMedGoogle Scholar
  5. .
    Decety, J.: Do executed and imagined movements share the same central structures? Cognitive Brain Research 3, 87–93 (1996)CrossRefPubMedGoogle Scholar
  6. .
    Demiris, J., Hayes, G.: Imitation as a dual-route process featuring predictive and learning components: a biologically plausible computational model. In: Imitation in animals and artifacts, pp. 327–361. MIT, Cambridge, MA (2002)Google Scholar
  7. .
    Diamond, A.: Neuropsychological insights into the meaning of object concept development. In: The epigenesis of mind: essays on biology and cognition, pp. 67–110. Erlbaum, Hillsdale, NJ (1991)Google Scholar
  8. .
    Doya, K., Yoshizawa, S.: Memorizing oscillatory patterns in the analog neuron network. In: Proceedings of 1989 International Joint Conference on Neural Networks, pp. I:27–32. Washington, DC (1989)Google Scholar
  9. .
    Ehrsson, H., Fagergren, A., Johansson, R., Forssberg, H.: Evidence for the involvement of the posterior parietal cortex in coordination of fingertip forces for grasp stability in manipulation. Journal of Neurophysiology 90, 2978–2986 (2003)CrossRefPubMedGoogle Scholar
  10. .
    Eskandar, E., Assad, J.: Dissociation of visual, motor and predictive signals in parietal cortex during visual guidance. Nature Neuroscience 2, 88–93 (1999)CrossRefPubMedGoogle Scholar
  11. .
    Fagg, A.H., Arbib, M.A.: Modeling parietal-premotor interactions in primate control of grasping. Neural Networks 11, 1277–1303 (1998)CrossRefPubMedGoogle Scholar
  12. .
    Feltz, D.L., Landers, D.M.: The effects of mental practice on motor skill learning and performance: a meta-analysis. Journal of Sport Psychology 5, 25–57 (1983)Google Scholar
  13. .
    Fetz, E., Finocchio, D., Baker, M., Soso, M.: Sensory and motor responses of precentral cortex cells during comparable passive and active joint movements. Journal of Neurophysiology 43, 1070–1089 (1980)PubMedGoogle Scholar
  14. .
    Flanagan, J., Vetter, P., Johansson, R., Wolpert, D.: Prediction precedes control in motor learning. Current Biology 13(2), 146–150 (2003)CrossRefPubMedGoogle Scholar
  15. .
    Fogassi, L., Ferrari, P., Gesierich, B., Rozzi, S., Chersi, F., Rizzolatti, G.: Parietal lobe: from action organization to intention understanding. Science 308, 662–667 (2005)CrossRefPubMedGoogle Scholar
  16. .
    Geschwind, N., Kaplan, E.: Human cerebral disconnection syndromes. Neurology 12, 675–685 (1962)PubMedGoogle Scholar
  17. .
    Heilman, K.: Ideational apraxia - a re-definition. Brain 96, 861–864 (1973)CrossRefPubMedGoogle Scholar
  18. .
    Hesslow, G.: Conscious thought as simulation of behaviour and perception. Trends in Cognitive Sciences 6(6), 242–247 (2002)CrossRefPubMedGoogle Scholar
  19. .
    Inamura, T., Nakamura, N., Ezaki, H., Toshima, I.: Imitation and primitive symbol acquisition of humanoids by the integrated mimesis loop. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 4208–4213 (2001)Google Scholar
  20. .
    Inamura, T., Toshima, I., Tanie, H., Nakamura, Y.: Embodied symbol emergence based on mimesis theory. International Journal of Robotics Research 23(44), 363–377 (2004)Google Scholar
  21. .
    Isomura, Y., Akazawa, T., Nambu, A., Takada, M.: Neural coding of “attention for action” and “response selection” in primate anterior cingulate cortex. The Journal of Neuroscience 23, 8002–8012 (2003)PubMedGoogle Scholar
  22. .
    Ito, M.: Bases and implications of learning in the cerebellum – adaptive control and internal model mechanism. Progress in Brain Research 148, 95–109 (2005)CrossRefPubMedGoogle Scholar
  23. .
    Ito, M., Noda, K., Hoshino, Y., Tani, J.: Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model. Neural Networks 19, 323–337 (2006)CrossRefPubMedGoogle Scholar
  24. .
    Jeannerod, M.: The representing brain: neural correlates of motor imitation and imaginary. Behavioral and Brain Science 17, 187–245 (1994)CrossRefGoogle Scholar
  25. .
    Jeannerod, M.: Mental imagery in the motor context. Neuropsychologia 33(11), 1419–1432 (1995)CrossRefPubMedGoogle Scholar
  26. .
    Jordan, M., Rumelhart, D.: Forward models: supervised learning with a distal teacher. Cognitive Science 16, 307–354 (1992)CrossRefGoogle Scholar
  27. .
    Karmiloff-Smith, A.: Beyond modularity. A developmental perspective on cognitive science. MIT, Cambridge, MA (1992)Google Scholar
  28. .
    Kawato, M., Furukawa, K., Suzuki, R.: A hierarchical neural network model for the control and learning of voluntary movement. Biological Cybernetics 57, 169–185 (1987)CrossRefPubMedGoogle Scholar
  29. .
    Kawato, M., Maeda, Y., Uno, Y., Suzuki, R.: Trajectory formation of arm movement by cascade neural network model based on minimum torque-change criterion. Biological Cybernetics 62(4), 275–288 (1990)CrossRefPubMedGoogle Scholar
  30. .
    Liepmann, H.: Apraxie. Ergebnisse der Gesamten Medizin 1, 516–543 (1920)Google Scholar
  31. .
    Luria, A.: The working brain. Penguin Books, New York (1973)Google Scholar
  32. .
    McCarthy, J.: Situations, actions and causal laws. Stanford Artificial Intelligence Project, Memo2, (1963)Google Scholar
  33. .
    McDonald, S., Tate, R., Rigby, J.: Error types in ideomotor apraxia: a qualitative analysis. Brain and Cognition 25(2), 250–270 (1994)CrossRefPubMedGoogle Scholar
  34. .
    Nishimoto, R., Namikawa, J., Tani, J.: Learning multiple goal-directed actions through self-organization of a dynamic neural network model: a humanoid robot experiment. Adaptive Behavior 16, 166–181 (2008)CrossRefGoogle Scholar
  35. .
    Nolfi, S.: Evolving robots able to self-localize in the environment: The importance of viewing cognition as the result of processes occurring at different time scales. Connection Science 14(3), 231–244 (2002)CrossRefGoogle Scholar
  36. .
    Ohshima, F., Takeda, K., Bandou, M., Inoue, K.: A case of ideational apraxia -an impairment in the sequence of acts. Journal of Japanese Neuropsychology 14, 42–48 (1998)Google Scholar
  37. .
    Okamoto, H., Isomura, Y., Takada, M., Fukai, T.: Temporal intagration by stochastic recurrent network dynamics with bimodal neurons. Journal of Neurophysiology 97, 3859–3867 (2007)CrossRefPubMedGoogle Scholar
  38. .
    Oztop, E., Arbib, M.A.: Schema design and implementation of the grasp-related mirror neuron system. Biological Cybernetics 87, 116–140 (2002)CrossRefPubMedGoogle Scholar
  39. .
    Pezzulo, G.: Coordinating with the future: the anticipatory nature of representation. Minds and Machines 18, 179–225 (2008)CrossRefGoogle Scholar
  40. .
    Piaget, J.: The construction of reality in the child. Basic Books, New York (1954)CrossRefGoogle Scholar
  41. .
    Rizzolatti, G., Fadiga, L., Galless, V., Fogassi, L.: Premotor cortex and the recognition of motor actions. Cognitive Brain Research 3, 131–141 (1996)CrossRefPubMedGoogle Scholar
  42. .
    Rumelhart, D., Hinton, G., Williams, R.: Learning internal representations by error propagation. In: D. Rumelhart, J. McClelland (eds.) Parallel distributed processing, pp. 318–362. MIT, Cambridge, MA (1986)Google Scholar
  43. .
    Sakai, Y., Okamoto, H., Fukai, T.: Computational algorithms and neuronal network models underlying decision processes. Neural Networks 19, 1091–1105 (2006)CrossRefPubMedGoogle Scholar
  44. .
    Sakata, H., Taira, M., Murata, A., Mine, S.: Neural mechanisms of visual guidance of hand action in the parietal cortex of the monkey. Cerebral Cortex 5, 429–438 (1995)CrossRefPubMedGoogle Scholar
  45. .
    Sato, N., Sakata, H., Tanaka, Y., Taira, M.: Navigation-associated medial parietal neurons in monkeys. Proceedings of the National Academy of Sciences of USA 103, 17,001–17,006 (2006)Google Scholar
  46. .
    Schaal, S., Ijspeert, A., Billard, A.: Computational approaches to motor learning by imitation. Philosophical Transaction of the Royal Society of London: Series B, Biological Sciences 358(1431), 537–547 (2003)CrossRefGoogle Scholar
  47. .
    Schoner, S., Kelso, S.: Dynamic pattern generation in behavioral and neural systems. Science 239, 1513–1519 (1988)CrossRefPubMedGoogle Scholar
  48. .
    Smith, L., Thelen, E.: A dynamic systems approach to the development of cognition and action. MIT, Cambridge, MA (1994)Google Scholar
  49. .
    Soso, M., Fetz, E.: Responses of identified cells in postcentral cortex of awake monkeys during comparable active and passive joint movements. Journal of Neurophysiology 43, 1090–1110 (1980)PubMedGoogle Scholar
  50. .
    Tani, J.: Model-based learning for mobile robot navigation from the dynamical systems perspective. IEEE Transactions on Systems, Man, and Cybernetics B 26(3), 421–436 (1996)CrossRefGoogle Scholar
  51. .
    Tani, J.: Learning to generate articulated behavior through the bottom-up and the top-down interaction process. Neural Networks 16, 11–23 (2003)CrossRefPubMedGoogle Scholar
  52. .
    Tani, J., Fukumura, N.: Learning goal-directed sensory-based navigation of a mobile robot. Neural Networks 7(3) (1994)Google Scholar
  53. .
    Tani, J., Ito, M., Sugita, Y.: Self-organization of distributedly represented multiple behavior schemata in a mirror system: reviews of robot experiments using RNNPB. Neural Networks 17, 1273–1289 (2004)CrossRefPubMedGoogle Scholar
  54. .
    Tani, J., Nishimoto, R., Namikawa, J., Ito, M.: Codevelopmental learning between human and humanoid robot using a dynamic neural network model. IEEE Transactions on Systems, Man, and Cybernetics 38(1), 43–59 (2008)CrossRefPubMedGoogle Scholar
  55. .
    Tani, J., Nishimoto, R., Paine, R.: Achieving “organic compositionality” through self-organization: reviews on brain-inspired robotics experiments. Neural Networks 21, 584–603 (2008)CrossRefPubMedGoogle Scholar
  56. .
    Tani, J., Nolfi, S.: Learning to perceive the world as articulated: an approach for hierarchical learning in sensory-motor systems. In: R. Pfeifer, B. Blumberg, J. Meyer, S. Wilson (eds.) From animals to animats 5. MIT, Cambridge, MA (1998). Later published in Neural Networks, vol 12, pp. 1131–1141, 1999Google Scholar
  57. .
    Vogt, S.: On relations between perceiving, imaging and performing in the learning of cyclical movement sequences. British Journal of Psychology 86, 191–216 (1995)PubMedGoogle Scholar
  58. .
    Wolpert, D., Kawato, M.: Multiple paired forward and inverse models for motor control. Neural Networks 11, 1317–1329 (1998)CrossRefPubMedGoogle Scholar
  59. .
    Yamashita, Y., Tani, J.: Emergence of functional hierarchy in a multiple timescale neural network model: a humanoid robot experiment. PLoS Computational Biology 4(11) (2008)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

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

Personalised recommendations