Advertisement

What We Need from an Embodied Cognitive Architecture

  • Serge ThillEmail author
Chapter
Part of the Intelligent Systems, Control and Automation: Science and Engineering book series (ISCA, volume 94)

Abstract

Given that original purpose of cognitive architectures was to lead to a unified theory of cognition, this chapter considers the possible contributions that cognitive architectures can make to embodied theories of cognition in particular. This is not a trivial question since the field remains very much divided about what embodied cognition actually means, and we will see some example positions in this chapter. It is then argued that a useful embodied cognitive architecture would be one that can demonstrate (a) what precisely the role of the body in cognition actually is, and (b) whether a body is constitutively needed at all for some (or all) cognitive processes. It is proposed that such questions can be investigated if the cognitive architecture is designed so that consequences of varying the precise embodiment on higher cognitive mechanisms can be explored. This is in contrast with, for example, those cognitive architectures in robotics that are designed for specific bodies first; or architectures in cognitive science that implement embodiment as an add-on to an existing framework (because then, that framework is by definition not constitutively shaped by the embodiment). The chapter concludes that the so-called semantic pointer architecture by Eliasmith and colleagues may be one framework that satisfies our desiderata and may be well-suited for studying theories of embodied cognition further.

References

  1. 1.
    Barsalou, L. W. (1999). Perceptual symbol systems. Behavioral and Brain Sciences, 22(4), 577–660.Google Scholar
  2. 2.
    Barsalou, L. W., Santos, A., Simmons, W. K., & Wilson, C. D. (2008). Language and simulation in conceptual processing. Symbols, embodiment, and meaning (pp. 245–283). Oxford: Oxford University Press.CrossRefGoogle Scholar
  3. 3.
    Cangelosi, A., & Schlesinger, M. (2015). Developmental robotics: From babies to robots. MIT Press.Google Scholar
  4. 4.
    Chemero, A. (2009). Radical embodied cognitive science. Cambridge, MA: MIT Press.Google Scholar
  5. 5.
    Chersi, F., Thill, S., Ziemke, T., & Borghi, A. M. (2010). Sentence processing: Linking language to motor chains. Frontiers in Neurorobotics, 4(4).Google Scholar
  6. 6.
    Cisek, P., & Kalaska, J. F. (2010). Neural mechanisms for interacting with a world full of action choices. Annual Review of Neuroscience, 33(1), 269–298. PMID: 20345247.CrossRefGoogle Scholar
  7. 7.
    Dove, G. (2011). On the need for embodied and dis-embodied cognition. Frontiers in Psychology, 1(242).Google Scholar
  8. 8.
    Eliasmith, C. (2013). How to build a brain: A neural architecture for biological cognition. Oxford: Oxford University Press.CrossRefGoogle Scholar
  9. 9.
    Eliasmith, C., & Anderson, C. H. (2002). Neural engineering: Computation, representation, and dynamics in neurobiological systems. Cambridge, MA: MIT Press.Google Scholar
  10. 10.
    Eliasmith, C., Stewart, T. C., Choo, X., Bekolay, T., DeWolf, T., Tang, Y., et al. (2012). A large-scale model of the functioning brain. Science, 338(6111), 1202–1205.CrossRefGoogle Scholar
  11. 11.
    Erlhagen, W., & Schöner, G. (2002). Dynamic field theory of movement preparation. Psychological Review, 109(3), 545–572.CrossRefGoogle Scholar
  12. 12.
    Felleman, D. J., & Van Essen, D. C. (1991). Distributed hierarchical processing in primate visual cortex. Cerebral Cortex, 1, 1–47.CrossRefGoogle Scholar
  13. 13.
    Harnad, S. (1990). The symbol grounding problem. Physica D: Nonlinear Phenomena, 42(1–3), 335–346.CrossRefGoogle Scholar
  14. 14.
    Mahon, B. Z., & Caramazza, A. (2008). A critical look at the embodied cognition hypothesis and a new proposal for grounding conceptual content. Journal of Physiology-Paris, 102(1), 59–70. Links and Interactions Between Language and Motor Systems in the Brain.Google Scholar
  15. 15.
    Pfeifer, R., Bongard, J., & Grand, S. (2007). How the body shapes the way we think: A new view of intelligence. Cambridge, MA: MIT press.Google Scholar
  16. 16.
    Pfeifer, R., & Iida, F. (2005). Morphological computation: Connecting body, brain and environment. Japanese Scientific Monthly.Google Scholar
  17. 17.
    Searle, J. R. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3(9), 417–424.CrossRefGoogle Scholar
  18. 18.
    Spencer, J. P., Austin, A., & Schutte, A. R. (2012). Contributions of dynamic systems theory to cognitive development. Cognitive Development, 27(4), 401–418. The Potential Contribution of Computational Modeling to the Study of Cognitive Development: When, and for What Topics?Google Scholar
  19. 19.
    Stapleton, M. (2011). Proper embodiment: The role of the body in affect and cognition. Ph.D. thesis, The University of Edinburgh.Google Scholar
  20. 20.
    Stapleton, M. (2013). Steps to a “properly embodied” cognitive science. Cognitive Systems Research, 22–23, 1–11.CrossRefGoogle Scholar
  21. 21.
    Stewart, T. C., Tang, Y., & Eliasmith, C. (2010). A biologically realistic cleanup memory: Autoassociation in spiking neurons. Cognitive Systems Research, 12(2), 84–92.CrossRefGoogle Scholar
  22. 22.
    Stramandinoli, F., Cangelosi, A., & Marocco, D. (2011). Towards the grounding of abstract words: A neural network model for cognitive robots. In The 2011 International Joint Conference on Neural Networks (IJCNN) (pp. 467–474).Google Scholar
  23. 23.
    Sun, R. (2004). Desiderata for cognitive architectures. Philosophical Psychology, 17(3), 341–373.MathSciNetCrossRefGoogle Scholar
  24. 24.
    Thill, S., Caligiore, D., Borghi, A. M., Ziemke, T., & Baldassarre, G. (2013). Theories and computational models of affordance and mirror systems: An integrative review. Neuroscience & Biobehavioral Reviews, 37(3), 491–521.CrossRefGoogle Scholar
  25. 25.
    Thill, S., Padó, S., & Ziemke, T. (2014). On the importance of a rich embodiment in the grounding of concepts: Perspectives from embodied cognitive science and computational linguistics. Topics in Cognitive Science, 6(3), 545–558.CrossRefGoogle Scholar
  26. 26.
    Thill, S., Svensson, H., & Ziemke, T. (2011). Modeling the development of goal-specificity in mirror neurons. Cognitive Computation, 3(4), 525–538.CrossRefGoogle Scholar
  27. 27.
    Thill, S., & Twomey, K. (2016). What’s on the inside counts: A grounded account of concept acquisition and development. Frontiers in Psychology: Cognition, 7(402).Google Scholar
  28. 28.
    van der Velde, F., & de Kamps, M. (2006). Neural blackboard architectures of combinatorial structures in cognition. Behavioral and Brain Sciences, 29(2), 37–70.Google Scholar
  29. 29.
    Vernon, D. (2014). Artificial cognitive systems: A primer. Cambridge, MA: MIT Press.Google Scholar
  30. 30.
    Vernon, D., von Hofsten, C., & Fadiga, L. (2016). Desiderata for developmental cognitive architectures. Biologically Inspired Cognitive Architectures, 18, 116–127.CrossRefGoogle Scholar
  31. 31.
    Wilson, M. (2002). Six views of embodied cognition. Psychonomic Bulletin & Review, 9(4), 625–636.MathSciNetCrossRefGoogle Scholar
  32. 32.
    Ziemke, T. (2003). What’s that thing called embodiment? In Proceedings of the 25th Annual Meeting of the Cognitive Science Society (pp. 1305–1310).Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Centre for Robotics and Neural SystemsUniversity of PlymouthPlymouthUK
  2. 2.School of InformaticsUniversity of SkövdeSkövdeSweden

Personalised recommendations