Abstract
Computational models of the mirror (neuron) system are attractive in robotics as they may inspire novel approaches to implement e.g. action understanding. Here, we present a simple self-organising map which forms the first part of larger ongoing work in building such a model. We show that minor modifications to the standard implementation of such a map allows it to continuously learn new motor concepts. We find that this learning is facilitated by an initial motor babbling phase, which is in line with an embodied view of cognition. Interestingly, we also find that the map is capable of reproducing neurophysiological data on goal-encoding mirror neurons. Overall, our model thus fulfils the crucial requirement of being able to learn new information throughout its lifetime. Further, although conceptually simple, its behaviour has interesting parallels to both cognitive and neuroscientific evidence.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bonaiuto, J., Rosta, E., Arbib, M.A.: Extending the mirror neuron system model, I. Biological Cybernetics 96, 9–38 (2007)
Chersi, F., Mukovskiy, A., Fogassi, L., Ferrari, P.F., Erlhagen, W.: A model of intention understanding based on learned chains of motor acts in the parietal lobe. In: Proceedings of the 15th Annual Computational Neuroscience Meeting, Edinburgh, UK (2006)
Der, R., Martins, G.: From motor babbling to purposive actions: Emerging self-exploration in a dynamical systems approach to early robot development. In: From Animals to Animats, vol. 9, pp. 406–421 (2006)
Erlhagen, W., Mukovskiy, A., Chersi, F., Bicho, E.: On the development of intention understanding for joint action tasks. In: Proceedings of the 6th IEEE International Conference on Development and Learning. Imperial College, London (2007)
Fogassi, L., Ferrari, P.F., Gesierich, B., Rozzi, S., Chersi, F., Rizzolatti, G.: Parietal lobe: from action organization to intention understanding. Science 308, 662–667 (2005)
Hickok, G.: Eight problems for the mirror neuron theory of action understanding in monkeys and humans. Journal of Cognitive Neuroscience 21(7), 1229–1243 (2008)
Koenig, N., Matarić, M.J.: Behavior-based segmentation of demonstrated tasks. In: Proc. of Int. Conf. on Development and Learning (2006)
Kohonen, T.: Self-organizing maps. Springer, Heidelberg (1997)
Kulić, D., Nakamura, Y.: Scaffolding on-line segmentation of fully body human motion patterns. In: IEEE Int. Conf. on Intelligent Robots and Systems, pp. 2860–2866 (2008)
Lieberman, J., Breazeal, C.: Improvements on action parsing and action interpolatin for learning through demonstration. In: IEEE/RAS Int. Conf. on Humanoid Robots, pp. 342–365 (2004)
Meltzoff, A.N., Moore, K.M.: Explaining facial imitation: a theoretical model. Early Development and Parenting 6(2), 179–192 (1997)
Oztop, E., Kawato, M., Arbib, M.A.: Mirror neurons and imitation: A computationally guided review. Neural Networks 19, 254–271 (2006)
Pomplun, M., Matarić, M.J.: Evaluation metrics and results of human arm movement imitation. In: IEEE-RAS Int. Conf. on Humanoid Robotics (2000)
Rizzolatti, G., Fadiga, L., Gallese, V., Fogassi, L.: Premotor cortex and the recognition of motor actions. Cognitive Brain Research 3(2), 131–141 (1996)
Wermter, S., Elshaw, M., Farrand, S.: A modular approach to self-organization of robot control based on language instruction. Connection Science 15(2-3), 73–94 (2003)
Yamashita, Y., Tani, J.: Emergence of functional hierarchy in a multiple timescale neural network model: a humanoid robot experiment. PLoS Computational Biology 4(11), 1–18 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Thill, S., Ziemke, T. (2010). Learning New Motion Primitives in the Mirror Neuron System: A Self-organising Computational Model. In: Doncieux, S., Girard, B., Guillot, A., Hallam, J., Meyer, JA., Mouret, JB. (eds) From Animals to Animats 11. SAB 2010. Lecture Notes in Computer Science(), vol 6226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15193-4_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-15193-4_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15192-7
Online ISBN: 978-3-642-15193-4
eBook Packages: Computer ScienceComputer Science (R0)