Visuomotor Coordination: From Neural Nets to Schema Theory

  • Michael A. Arbib
Part of the NATO Conference Series book series (NATOCS, volume 16)


In much of control theory, the system to be controlled is represented by an n-dimensional vector, representing for example the position and momentum of key components. In this paper, we look at the adaptive control of systems which cannot be well-defined in such terms. Here, the system to be controlled is the body of a human, animal, or robot in interaction with a complex world made up of diverse objects. We shall study controllers which represent this world by a family of ‘schemas’, and which embed their computations in layers of highly-parallel computing elements. We particularly note the flexibility of ‘schema-asemblages’ as representations when compared to n-vectors; and the way in which identification algorithms within each (motor) schema can provide important tools for adaptive control.


Optic Flow Feature Match Climbing Fiber Motor Schema Perceptual Schema 
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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  1. Arbib, M. A. “The Metaphorical Brain: An Introduction to Cybernetics as Artificial Intelligence and Brain Theory,” New York: Interscience, 1972.MATHGoogle Scholar
  2. Arbib, M. A., 1975, Parallelism, slides, schemas and frames, in; “Systems: Approaches, Theories, Applications,” W. E. Hartnett, ed., Reidel, pp. 27–43.Google Scholar
  3. Arbib, M. A., 1979, Local organizing processes and motion schemas in visual perception, in: “Machine Intelligence 9,” J. E. Hayes, D. Michie and L. I. Mikulich, eds., Ellis Horwood Ltd., pp. 287–298.Google Scholar
  4. Arbib, M. A., 1981, Perceptual structures and distributed motor control, in: “Motor Control,” V. B. Brooks, ed., Vol. II, “Section on Neurophysiology, Handbook of Physiology,” Amer. Physiological Soc., pp. 1449–1480.Google Scholar
  5. Arbib, M.A., 1982, Modelling neural mechanisms of visuomotor coordination in frog and toad, “Competition and Cooperation in Neural Nets,” S. Amari and M.A. Arbib, eds., Lecture Notes in Biomathematics 45, Springer-Verlag, pp. 342–370.CrossRefGoogle Scholar
  6. Arbib, M. A., C. C. Boylls, and P. Dev, 1974, Neural models of spatial perception and the control of movement, in: “Cybernetics and Bionics,” W. D. Keidel, W. Handler and M. Spreng, eds., Oldenbourg, pp. 216–231.Google Scholar
  7. Arbib, M. A. and R. Lara, 1982, A neural model of the interaction of tectal columns in prey-catching behavior. Biol. Cybern. 44: 185–196.CrossRefGoogle Scholar
  8. Bartlett, F. C., 1932, “Remembering,” Cambridge Univ. Press.Google Scholar
  9. Boylls, C. C., 1975, A Theory of Cerebellar Function with Applications to Locomotion. I. The Physiological Role of Climbing Fiber Inputs in Anterior Lobe Operation. Univ. of Massachusetts at Amherst, COINS Tech. Report 75C-6.Google Scholar
  10. Boylls, C. C., 1976, A Theory of Cerebellar Function with Applications to Locomotion. II. The Relation of Anterior Lobe Climbing Fiber Function to Locomotor Behavior in the Cat. Univ. of Massachusetts at Amherst, COINS Tech. Report 76–1.Google Scholar
  11. Collett, T. S., 1979, A toad’s devious approach to its prey: a study of some complex uses of depth vision. J. Comp. Physiol. A. 131: 179–189.CrossRefGoogle Scholar
  12. Craik, K. J. W., 1943, “The Nature of Explanation,” Cambridge Univ. Press.Google Scholar
  13. Dev, P., 1975, Computer simulation of a dynamic visual perception model. Int. J. Man-Mach. Stud. 7: 511–528.MATHCrossRefGoogle Scholar
  14. Didday, R. L., 1970, “The Simulation and Modelling of Distributed Information Processing in the Frog Visual System,” (Ph.D. Thesis) Stanford Univ.Google Scholar
  15. Didday, R. L., 1976, A model of visuomotor mechanisms in the frog optic tectum. Math. Biosci. 30: 169–180.CrossRefGoogle Scholar
  16. Ewert, J.-P., 1976, The visual system of the toad: behavioral and physiological studies on a pattern recognition system, in: “The Amphibian Visual System: A Multidisciplinary Approach,” K. V. Fite, ed., Academic Press, pp. 141–202.Google Scholar
  17. Ewert, J.-P., and W. von Seelen, 1974, Neurobiologie und System-Theorie eines visuellen Muster-Erkennungsmechanismus bei Kroten. Kybernetik 14: 167–183.CrossRefGoogle Scholar
  18. Frisby, J. P. and J.E.W. Mayhew, 1977, Global processes in stereopsis. Perception 6:195–206.CrossRefGoogle Scholar
  19. Gibson, J. J., 1955, The optical expansion-pattern in aerial location. Am. J. Psychol. 68: 480–484.CrossRefGoogle Scholar
  20. Gibson, J. J., 1966, “The Senses Considered as Perceptual Systems,” Allen and Unwin.Google Scholar
  21. Gibson, J. J., 1977, The theory of affordances. in: “Perceiving, Acting and Knowing,” R. E. Shaw and J. Bransford, eds., Erlbaum.Google Scholar
  22. Gregory, R. L., 1969, On how so little information controls so much behavior, “Towards a Theoretical Biology, 2: Sketches,” C. H. Waddington, ed., Edinburgh Univ. Press.Google Scholar
  23. Hanson, A. R. and E. M. Riseman (eds.), 1978, “Computer Vision Systems,” Academic Press.Google Scholar
  24. Harth, E., 1976, Visual perception: a dynamic theory. Biol. Cybernetics 22: 169–180.CrossRefGoogle Scholar
  25. Ingle, D., 1975, Focal attention in the frog: Behavioral and physiological correlates. Science 188: 1033–1035.CrossRefGoogle Scholar
  26. Ingle, D., 1976, Spatial vision in anurans. “The Amphibian Visual System: A Multidisciplinary Approach,” K. V. Fite, ed., Academic Press, pp. 119–141.Google Scholar
  27. Ingle, D., in press, A functional approach to the many visual systems dilemma.Google Scholar
  28. Jackson, J. H., 1898, Relations of different divisions of the central nervous system to one another and to parts of the body. Lancet, January 8 (1898).Google Scholar
  29. Jeannerod, M., and B. Biguer, 1982, Visuomotor mechanisms in reaching within extrapersonal space, in: “Analysis of Visual Behavior,” D. J. Ingle, M.A. Goodale and R.J.W. Mansfield, eds., MIT Press, pp. 387–409.Google Scholar
  30. Julesz, B., 1971, “Foundations of Cyclopean Perception,” Univ. of Chicago Press.Google Scholar
  31. Lara, R. and M. A. Arbib, 1982, A neural model of interaction between tectum and pretectum in prey selection. Cognition and Brain Theory 5: 149–171.Google Scholar
  32. Lara, R., M. A. Arbib and A. S. Cromarty, 1982, The role of the tectal column in facilitation of amphibian prey-catching behavior: a neural model. J. Neuroscience 2: 521–530.Google Scholar
  33. Lee, D. N., 1974, Visual information during locomotion, in: “Perception: Essays in Honor of James J. Gibson,” R. B. MacLeod and H. L. Pick, Jr., eds., Cornell Univ. Press, pp. 250–267.Google Scholar
  34. Lee, D. N. and J. R. Lishman, 1977, Visual control of locomotion. Scand. J. Psychol. 18: 224–230.CrossRefGoogle Scholar
  35. Lettvin, J. Y., H. Maturana, W. S. McCulloch and W. H. Pitts, 1959, What the frog’s eye tells the frog’s brain. Proc. IRE 47: 1940–1951.CrossRefGoogle Scholar
  36. MacKay, D. M., 1966, Cerebral organization and the conscious control of action, “Brain and Conscious Experience,” J. C. Eccles, ed., Springer-Verlag, pp. 422–440.Google Scholar
  37. Marr, D. and T. Poggio, 1977, Cooperative computation of stereo disparity. Science 194: 283–287.CrossRefGoogle Scholar
  38. Marr, D. M. and T. Poggio, 1979, A theory of human stereopsis. Proc. Roy. Soc. Ser. B 204: 301–328.CrossRefGoogle Scholar
  39. Minsky, M. L., 1975, A framework for representing knowledge, in: “The Psychology of Computer Vision,” P. H. Winston, ed., McGraw-Hill, pp. 211–277.Google Scholar
  40. Prager, J. M., 1979, “Segmentation of Static and Dynamic Scenes,” Ph.D. Thesis. Dept. of Computer and Information Science, Univ. of Massachusetts at Amherst.Google Scholar
  41. Prager, J. M. and M. A. Arbib, in press. Computing the optic flow: The MATCH algorithm. Comp. Graphics and Image Proc.Google Scholar
  42. Robinson, D. A., 1976, Adaptive gain control of vestibulo-ocular reflex by the cerebellum. J. Neurophysiol. 39: 954–969.Google Scholar
  43. Singer, W., 1977, Control of thalamic transmission by corticofugal and ascending reticular pathways in the visual system. Physiol. Rev. 57: 386–420.Google Scholar
  44. Sperling, G., 1970, Binocular vision: a physical and neural theory. Am. J. Psych. 83: 463–534.Google Scholar

Copyright information

© Plenum Press, New York 1984

Authors and Affiliations

  • Michael A. Arbib
    • 1
  1. 1.Center for Systems Neuroscience, Department of Computer and Information ScienceUniversity of MassachusettsAmherstUSA

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