Learning and Adaptation of Sensorimotor Contingencies: Prism-Adaptation, a Case Study

  • Gert Kootstra
  • Niklas Wilming
  • Nico M. Schmidt
  • Mikael Djurfeldt
  • Danica Kragic
  • Peter König
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7426)


This paper focuses on learning and adaptation of sensorimotor contingencies. As a specific case, we investigate the application of prism glasses, which change visual-motor contingencies. After an initial disruption of sensorimotor coordination, humans quickly adapt. However, scope and generalization of that adaptation is highly dependent on the type of feedback and exhibits markedly different degrees of generalization. We apply a model with a specific interaction of forward and inverse models to a robotic setup and subject it to the identical experiments that have been used on previous human psychophysical studies. Our model demonstrates both locally specific adaptation and global generalization in accordance with the psychophysical experiments. These results emphasize the role of the motor system for sensory processes and open an avenue to improve on sensorimotor processing.


Sensorimotor contingencies prism-adaptation motor learning/ adaptation body maps inverse kinematics 


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  1. 1.
    Baraduc, P., Wolpert, D.: Adaptation to a visuomotor shift depends on the starting posture. Journal of Neurophysiology 88(2), 973–981 (2002)Google Scholar
  2. 2.
    D’Souza, A., Vijayakumar, S., Schaal, S.: Learning inverse kinematics. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2001)Google Scholar
  3. 3.
    Einhäuser, W., Martin, K., König, P.: Are switches in perception of the necker cube related to eye position? European Journal of Neuroscience 20(10), 2811–2818 (2004)CrossRefGoogle Scholar
  4. 4.
    Held, R., Hein, A.V.: Adaptation of disarranged hand-eye coordination contingent upon re-afferent stimulation. Perceptual and Motor Skills 8(3), 87–90 (1958)CrossRefGoogle Scholar
  5. 5.
    Held, R., Schlank, M.: Adaptation to disarranged eye-hand coördination in the distance-dimension. The American Journal of Psychology 72(4), 603–605 (1959)CrossRefGoogle Scholar
  6. 6.
    Kornheiser, A.: Adaptation to laterally displaced vision: A review. Psychological Bulletin 83(5), 783–816 (1976)CrossRefGoogle Scholar
  7. 7.
    Martin, T., Keating, J., Goodkin, H., Bastian, A., Thach, W.: Throwing while looking through prisms. ii. Specificity and storage of multiple gaze-throw calibrations. Brain 119(4), 1199–1212 (1996)Google Scholar
  8. 8.
    Nagel, S., Carl, C., Kringe, T., Märtin, R., König, P.: Beyond sensory substitution – learning the sixth sense. Journal of Neural Engineering 2, R13 (2005)Google Scholar
  9. 9.
    Nguyen-Tuong, D., Peters, J.: Model learning for robot control: A survey. Cognitive Processing 12(4), 319–340 (2011)CrossRefGoogle Scholar
  10. 10.
    Nguyen-Tuong, D., Seeger, M., Peters, J.: Model learning with local gaussian process regression. Advanced Robotics 23(15), 2015–2034 (2009)CrossRefGoogle Scholar
  11. 11.
    O’Regan, J., Noe, A.: A sensorimotor account of vision and visual consciousness. Behavioral and Brain Sciences 24(5), 939–1031 (2001)CrossRefGoogle Scholar
  12. 12.
    Popović, M., Kootstra, G., Jørgensen, J.A., Kragic, D., Krüger, N.: Grasping unknown objects using an early cognitive vision system for general scene understanding. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 987–994. IEEE, San Francisco (2011)Google Scholar
  13. 13.
    Rasmussen, C.E., Williams, C.: Gaussian Processes for Machine Learning. The MIT Press (2006)Google Scholar
  14. 14.
    Redding, G.M., Wallace, B.: Components of prism adaptation in terminal and concurrent exposure: Organization of the eye-hand coordination loop. Perception and Psychophysics 44(1), 59–68 (1988)CrossRefGoogle Scholar
  15. 15.
    Redding, G.M., Wallace, B.: Generalization of prism adaptation. Journal of Experimental Psychology: Human Perception and Performance 32(4), 1006–1022 (2006)CrossRefGoogle Scholar
  16. 16.
    Redding, G.M., Wallace, B.: Intermanual transfer of prism adaptation. Journal of Motor Behavior 40(3), 246–262 (2008)CrossRefGoogle Scholar
  17. 17.
    Schaal, S., Atkeson, C.G., Vijayakumar, S.: Scalable techniques from nonparametric statistics for real-time robot learning. Applied Intelligence 17(1), 49–60 (2002)MATHCrossRefGoogle Scholar
  18. 18.
    Welch, R., Bridgeman, B., Anand, S., Browman, K.: Alternating prism exposure causes dual adaptation and generalization to a novel displacement. Perceptual Psychophysics 54(2), 195–205 (1993)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Gert Kootstra
    • 1
  • Niklas Wilming
    • 2
  • Nico M. Schmidt
    • 3
  • Mikael Djurfeldt
    • 4
  • Danica Kragic
    • 1
  • Peter König
    • 2
    • 5
  1. 1.CAS-CVAP, CSCRoyal Institute of Technology (KTH)Sweden
  2. 2.Institute of Cognitive ScienceUniversity of OsnabrückGermany
  3. 3.AI LabUniversity of ZürichSwitzerland
  4. 4.PDCRoyal Institute of Technology (KTH)Sweden
  5. 5.Department of Neurophysiology and PathophysiologyUniversity Medical Center Hamburg-EppendorfGermany

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