A Basis for Cognitive Machines

  • J. G. Taylor
  • S. Kasderidis
  • P. Trahanias
  • M. Hartley
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4131)


We propose a general attention-based approach to thinking and cognition (more specifically reasoning and planning) in cognitive machines as based on the ability to manipulate neural activity in a virtual manner so as to achieve certain goals; this can then lead to decisions to make movements or to no actions whatever. The basic components are proposed to consist of forward/inverse model motor control pairs in an attention-control architecture, in which buffers are used to achieve sequencing by recurrence of virtual actions and attended states. How this model can apply to various reasoning paradigm will be described and first simulations presented using a virtual robot environment.


Forward Model Goal State Visual Input Visual Working Memory Goal Module 
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  1. 1.
    Taylor, J.G.: Paying Attention to Consciousness. Progress in Neurobiology 71, 305–335 (2003)CrossRefGoogle Scholar
  2. 2.
    Taylor, J.G.: From Matter to Consciousness: Towards a Final Solution? Physics of Life Reviews 2, 1–44 (2005)CrossRefGoogle Scholar
  3. 3.
    Emery, N.J., Clayton, N.S.: The Mentality of Crows: Convergent Evolution of Intelligence in Corvids and Apes. Science 306, 1903–1907 (2004)CrossRefGoogle Scholar
  4. 4.
    Bhushan, N., Shadmehr, R.: Computational nature of human adaptive control during learning of reaching movements in force fields. Biol Cybern. 81, 39 (1999)MATHCrossRefGoogle Scholar
  5. 5.
    Oztop, E., et al.: Mental state inference using visual control parameters. Brain Res. Cogn Brain Res. 22, 129 (2005)CrossRefGoogle Scholar
  6. 6.
    Taylor, J.G., Fragopanagos, N.: Simulations of Attention Control Models in Sensory and Motor Paradigms. In: Kaynak, O., Alpaydın, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714. Springer, Heidelberg (2003)Google Scholar
  7. 7.
    Taylor, N.R., Taylor, J.G.: Hard-wired models of working memory and temporal sequence storage and generation. Neural Netw 12, 201 (2000)CrossRefGoogle Scholar
  8. 8.
    McGrew, W.C.: Chimpanzee Material Culture. Cambridge University Press, Cambridge (1992)CrossRefGoogle Scholar
  9. 9.
    Boysen, S.T., Himes, G.T.: Current Issues and Emerging Theories in Animal Cognition. Annual Reviews of Psychology 50, 683–705 (1999)CrossRefGoogle Scholar
  10. 10.
    Mulcahy, N.J., Call, J., Dunbar, R.I.M.: Gorillas and Orang Utans Encode Relevant Problem Features in a Tool Using Task. Journal of Comparative Psychology 119, 23–32 (2005)CrossRefGoogle Scholar
  11. 11.
    Rushworth, M.F.S., Ellison, A., Walsh, V.: Complementary localization and lateralization of orienting and motor attention. Nature Neuroscience 4(6), 656–661 (2001)CrossRefGoogle Scholar
  12. 12.
    Rushworth, M.F.S., Johansen-Berg, H., Gobel, S.M., Devlin, J.T.: The left parietal and premotor cortices: motor attention and selection. NeuroImage 20, S89–S100 (2003)CrossRefGoogle Scholar
  13. 13.
    Desmurget, M., Grafton, S.: Forward modeling allows feedback control for fast reaching movements. Trends Cogn Sci. 4, 423 (2000)CrossRefGoogle Scholar
  14. 14.
    Wise, S.P., Shadmehr, R.: Motor Control. Encyclopedia of the Brain, vol. 3, pp. 1–21. Elsevier, USA (2002)Google Scholar
  15. 15.
    Morasso, P.: Spatial control of arm movements. Experimental Brain Research 42, 223–227 (1981)CrossRefGoogle Scholar
  16. 16.
    Taylor, J.G., Fragopanagos, N.: Modelling Human Attention and Emotions. In: Proc IJCNN 2004, Budapest (2004)Google Scholar
  17. 17.
    Davidson, P.R., Jones, R.D., Andreae, J.H., Sirisena, H.R.: Simulating Closed and Open-Loop Voluntary Movement: A Nonlinear Control-Systems Approach. IEEE Trans Biomedical Engineering 49, 1242–1252 (2002)CrossRefGoogle Scholar
  18. 18.
    Neilson, P.D., Neilson, M.D.: A neuroengineering solution to the optimal tracking problem. Human Movement Science 18, 155–183 (1999)CrossRefGoogle Scholar
  19. 19.
    Ohyama, T., Nores, W.L., Murphy, M., Mauk, M.D.: What the cerebellum computes. Trends in Neuroscience 26(4), 222–226 (2003)CrossRefGoogle Scholar
  20. 20.
    Rozzi, S., Calzavara, R., Belmalih, A., Borra, E., Gregoriou, G.G., Matelli, M., Luppino, G.: Cortical Connections of the Parietal Cortical Convexity of the Macaque Monkey. Cerebral Cortex (November 23, 2005)Google Scholar
  21. 21.
    Webots. Commercial Mobile Robot Simulation Software,

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • J. G. Taylor
    • 1
    • 2
  • S. Kasderidis
    • 1
    • 2
  • P. Trahanias
    • 1
    • 2
  • M. Hartley
    • 1
    • 2
  1. 1.Dept of MathematicsKing’s College LondonUK
  2. 2.Institute of Computer ScienceFORTHHeraklion, Crete

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