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The Cognitive Body: From Dynamic Modulation to Anticipation

  • Alberto Montebelli
  • Robert Lowe
  • Tom Ziemke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5499)

Abstract

Starting from the situated and embodied perspective on the study of cognition as a source of inspiration, this paper programmatically outlines a path towards an experimental exploration of the role of the body in a minimal anticipatory cognitive architecture. Cognition is here conceived and synthetically analyzed as a broadly extended and distributed dynamic process emerging from the interplay between a body, a nervous system and their environment. Firstly, we show how a non-neural internal state, crucially characterized by slowly changing dynamics, can modulate the activity of a simple neurocontroller. The result, emergent from the use of a standard evolutionary robotic simulation, is a self-organized, dynamic action selection mechanism, effectively operating in a context dependent way. Secondly, we show how these characteristics can be exploited by a novel minimalist anticipatory cognitive architecture. Rather than a direct causal connection between the anticipation process and the selection of the appropriate behavior, it implements a model for dynamic anticipation that operates via bodily mediation (bodily-anticipation hypothesis). This allows the system to swiftly scale up to more complex tasks never experienced before, achieving flexible and robust behavior with minimal adaptive cost.

Keywords

Hide Layer Adaptive Behavior Dynamic Modulation Microbial Fuel Cell Humanoid Robot 
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.

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References

  1. 1.
    Varela, F.J., Thompson, E.T., Rosch, E.: The Embodied Mind: Cognitive Science and Human Experience. MIT Press, Cambridge (1992)Google Scholar
  2. 2.
    Thelen, E., Smith, L.B.: A Dynamic Systems Approach to the Development of Cognition and Action. MIT Press, Cambridge (1996)Google Scholar
  3. 3.
    Clark, A.: Being There: Putting Brain, Body, and World Together Again. MIT Press, Cambridge (1997)Google Scholar
  4. 4.
    Chrisley, R., Ziemke, T.: Embodiment. In: Encyclopedia of Cognitive Science, pp. 1102–1108. McMillan, London (2002)Google Scholar
  5. 5.
    Ziemke, T., Zlatev, J., Frank, R.M. (eds.): Body, Language and Mind: Embodiment, vol. 1. Mouton de Gruyter, Berlin (2007)Google Scholar
  6. 6.
    Nolfi, S.: Power and limits of reactive agents. Neurocomputing 42(1-4), 119–145 (2002)CrossRefzbMATHGoogle Scholar
  7. 7.
    Wiener, N.: Cybernetics, or Control and Communication in the Animal and the Machine. MIT Press, Cambridge (1965)Google Scholar
  8. 8.
    Ashby, W.R.: Design for a Brain: The Origin of Adaptive Behavior. Chapman ‘&’ Hall, London (1952)zbMATHGoogle Scholar
  9. 9.
    Köhler, W.: Gestalt Psychology. Liveright (1947)Google Scholar
  10. 10.
    Gibson, J.J.: The Ecological Approach To Visual Perception. Houghton Mifflin (1979)Google Scholar
  11. 11.
    Van Gelder, T.: The dynamical hypothesis in cognitive science. Behavioral and Brain Sciences 21, 615–628 (2000)Google Scholar
  12. 12.
    Kelso, J.A.S.: Dynamic Patterns: The Self-organization of Brain and Behavior. MIT Press, Cambridge (1995)Google Scholar
  13. 13.
    Beer, R.D.: Parameter space structure of continuous-time recurrent neural networks neural networks. Neural Computation 18, 3009–3051 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Beer, R.D.: Dynamical approaches to cognitive science. Trends in Cognitive Sciences 4(3), 91–99 (2000)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Chiel, H., Beer, R.D.: The brain has a body: Adaptive behavior emerges from interactions of nervous system, body and environment. Trends in Neurosciences 20(12), 553–557 (1997)CrossRefGoogle Scholar
  16. 16.
    Suzuki, M., Floreano, D.: Enactive robot vision. Adaptive Behavior 16, 122–128 (2008)CrossRefGoogle Scholar
  17. 17.
    Parisi, D.: Internal robotics. Connection Science 16(4), 325–338 (2004)CrossRefGoogle Scholar
  18. 18.
    Ziemke, T.: On the role of emotion in biological and robotic autonomy. BioSystems 91, 401–408 (2008)CrossRefGoogle Scholar
  19. 19.
    Ziemke, T., Lowe, R.: On the role of emotion in embodied cognitive architectures: From organisms to robots. In: Cognitive computation (2009) (accepted)Google Scholar
  20. 20.
    Damasio, A.: The Feeling of What Happens: Body and Emotion in the Making of Consciousness. Harvest Books (2000)Google Scholar
  21. 21.
    Damasio, A.: Looking for Spinoza: Joy, Sorrow, and the Feeling Brain. Harcourt (2003)Google Scholar
  22. 22.
    Montebelli, A., Herrera, C., Ziemke, T.: On cognition as dynamical coupling: An analysis of behavioral attractor dynamics. Adaptive Behavior 16(2-3), 182–195 (2008)CrossRefGoogle Scholar
  23. 23.
    Montebelli, A., Herrera, C., Ziemke, T.: An analysis of behavioral attractor dynamics. In: Almeida e Costa, F. (ed.) Advances in Artificial Life: Proceedings of the 9th European Conference on Artificial Life, pp. 213–222. Springer, Berlin (2007)CrossRefGoogle Scholar
  24. 24.
    Strogatz, S.H.: Nonlinear Dynamics and Chaos. Westview Press, Cambridge (1994)Google Scholar
  25. 25.
    Rosen, R.: Anticipatory Systems. Pergamon Press, Oxford (1985)zbMATHGoogle Scholar
  26. 26.
    Butz, M.V., Sigaud, O., Gérard, P.: Anticipatory behavior: Exploiting knowledge about the future to improve current behavior. In: Butz, M.V., Sigaud, O., Gérard, P. (eds.) Anticipatory Behavior in Adaptive Learning Systems. LNCS, vol. 2684, pp. 1–10. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  27. 27.
    Butz, M.V., Pezzulo, G.: Benefits of anticipation in cognitive agents. In: Pezzulo, G., Butz, M.V., Castelfranchi, C., Falcone, R. (eds.) The Challenge of Anticipation: A Unifying Framework for the Analysis and Design of Artificial Cognitive Systems, pp. 45–62. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  28. 28.
    Grush, R.: The emulation theory of representation: motor control, imagery, and perception. Behavioral and Brain Sciences 27, 377–442 (2004)Google Scholar
  29. 29.
    Barsalou, L.W.: Perceptual symbol systems. Behavioral and Brain Sciences 22, 577–660 (1999)Google Scholar
  30. 30.
    Barsalou, L.W.: Social embodiment. In: Ross, B.H. (ed.) The Psychology of Learning and Motivation, pp. 43–92. Academic Press, London (2003)Google Scholar
  31. 31.
    Butz, M.V.: How and why the brain lays the foundations for a conscious self. Constructivist Foundations 4(1), 1–42 (2008)MathSciNetGoogle Scholar
  32. 32.
    Parisi, D.: Mente: i nuovi modelli della Vita Artificiale. Il Mulino, Bologna (1999)Google Scholar
  33. 33.
    Tani, J., Nolfi, S.: Learning to perceive the world as articulated: an approach for hierarchical learning in sensory-motor systems. Neural Networks 12(7-8), 1131–1141 (1999)CrossRefGoogle Scholar
  34. 34.
    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(3), 323–337 (2006)CrossRefzbMATHGoogle Scholar
  35. 35.
    Ziemke, T., Hesslow, G., Jirenhed, D.A.: Internal simulation of perception: a minimal neuro-robotic model. Neurocomputing 68, 85–104 (2005)CrossRefGoogle Scholar
  36. 36.
    Schoenbaum, G., Chiba, A.A., Gallagher, M.: Orbitofrontal cortex and basolateral amygdala encode expected outcomes during learning. Nature Neuroscience 1(2), 155–159 (1998)CrossRefGoogle Scholar
  37. 37.
    Haykin, S.: Neural networks: a comprehensive foundation. Prentice Hall, Englewood Cliffs (1999)zbMATHGoogle Scholar
  38. 38.
    Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, Geoffrey, E.: Adaptive mixtures of local experts. Neural Computation 3(1), 79–87 (1991)CrossRefGoogle Scholar
  39. 39.
    Morse, A., Lowe, R., Ziemke, T.: Towards an enactive cognitive architecture. In: Proceedings of the 2008 International Conference on Cognitive Systems (2008)Google Scholar
  40. 40.
    Kiebel, S.J., Daunizeau, J., Friston, K.J.: A hierarchy of time-scales and the brain. PLoS Computational Biology 4(11) (2008)Google Scholar
  41. 41.
    Fusi, S., Asaad, W.F., Miller, E.K., Wang, X.J.: A neural circuit model of flexible sensorimotor mapping: Learning and forgetting on multiple timescales. Neuron 54(2), 319–333 (2007)CrossRefGoogle Scholar
  42. 42.
    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
  43. 43.
    Paine, R.W., Tani, J.: How hierarchical control self-organizes in artificial adaptive systems. Adaptive Behavior 13(3), 211–225 (2005)CrossRefGoogle Scholar
  44. 44.
    Maniadakis, M., Tani, J.: Dynamical systems account for meta-level cognition. In: Asada, M., Hallam, J.C.T., Meyer, J.-A., Tani, J. (eds.) SAB 2008. LNCS, vol. 5040, pp. 311–320. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  45. 45.
    Jaeger, H., Haas, H.: Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. Science 304(5667), 78–80 (2004)CrossRefGoogle Scholar
  46. 46.
    Maass, W., Natschlager, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation 14(11), 2531–2560 (2002)CrossRefzbMATHGoogle Scholar
  47. 47.
    Melhuish, C., Ieropoulos, I., Greenman, J., Horsfield, I.: Energetically autonomous robots: food for thought. Autonomous Robots 21, 187–198 (2006)CrossRefGoogle Scholar
  48. 48.
    Tani, J., Ito, M.: Self-organization of behavioral primitives as multiple attractor dynamics: A robot experiment. IEEE Trans. on Systems, Man, and Cybernetics. Part B 33(4), 481–488 (2003)CrossRefGoogle Scholar
  49. 49.
    Tani, J.: Learning to generate articulated behavior through the bottom-up and the top-down interaction processes. Neural Networks 16, 11–23 (2003)CrossRefGoogle Scholar
  50. 50.
    Hesslow, G.: Conscious thought as simulation of behaviour and perception. Trends in Cognitive Sciences 6(6), 242–247 (2002)CrossRefGoogle Scholar
  51. 51.
    Bechara, A.: The role of emotion in decision-making: Evidence from neurological patients with orbitofrontal damage. Brain and Cognition (55), 30–40 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Alberto Montebelli
    • 1
  • Robert Lowe
    • 1
  • Tom Ziemke
    • 1
  1. 1.School of Humanities and InformaticsUniversity of SkövdeSkövdeSweden

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