Motion Shapes: Empirical Studies and Neural Modeling

  • Florian Röhrbein
  • Kerstin Schill
  • Volker Baier
  • Klaus Stein
  • Christoph Zetzsche
  • Wilfried Brauer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2685)


Any mobile agent able to interact with moving objects or other mobile agents requires the ability to process motion shapes. The human visual system is an excellent, fast and proven machinery for dealing with such information. In order to obtain insight into the properties of this biological machine and to transfer it to artificial agents we analyze the limitations and capabilities of human perception of motion shapes. Here we present new empirical results on the classification, extrapolation and prediction of motion shape with varying degrees of complexity. In addition, results on the processing of multisensory spatio-temporal information will be presented. We make use of our earlier argument for the existence of a spatio-temporal memory in early vision and use the basic properties of this structure in the first layer of a neural network model. We discuss major architectural features of this network, which is based on Kohonens self-organizing maps. This network can be used as an interface to further representational stage on which motion vectors are implemented in a qualitative way. Both components of this hybrid model are constrained by the results gained in the psychophysical experiments.


Neural Network Model Mobile Agent Neural Modeling Motion Path Inter Stimulus Interval 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    V. Baier, K. Schill, F. Röhrbein, W. Brauer. Processing of spatio-temporal structures: A hierarchical neural network model, Dynamische Perzeption, G. Baratoff, H. Neumann (eds.), 223–26, AKA, Akad.Verl.-Ges., Berlin, 2000.Google Scholar
  2. 2.
    G. Briscoe. Adaptive Behavioural Cognition, PhD thesis, Curtin University of Technology, School of Computing, Curtin, 1997.Google Scholar
  3. 3.
    Eisenkolb, A. Musto, K. Schill, D. Hernandez, W. Brauer. Representational levels for the perception of the courses of motion, An Interdisciplinary Approach to Representing and Processing Spatial Knowledge, Lecture Notes in Artificial Intelligence, C. Freksa, C. Habel, K. Wender (eds.), 1404, 129–55, Springer, Berlin, 1998.Google Scholar
  4. 4.
    A. Eisenkolb, K. Schill, F. Röhrbein, V. Baier, A. Musto, W. Brauer. Visual Processing and Representation of Spatio-Temporal Patterns. In C. Freksa, W. Brauer, C. Habel, K.F. Wender (eds.), Spatial Cognition II-Integrating Abstract Theories, Empirical Studies, Formal Methods, and Practical Applications, 145–56, Berlin: Springer, 2000.Google Scholar
  5. 5.
    D. M. Green, J. A. Swets. Signal Detection Theory and Psychophysics, Wiley, 1966.Google Scholar
  6. 6.
    T. Kohonen. Spatio-temporal connectionist networks: A taxonomy and review,, 1998.
  7. 7.
    J. Miller. Channel interaction and the redundant-targets effect in bimodal divided attention. J. Exp. Psych. HPP, 17(1), 160–69, 1991.CrossRefGoogle Scholar
  8. 8.
    A. Musto, K. Stein, A. Eisenkolb, T. Röfer, W. Brauer, K. Schill, From Motion Observation to Qualitative Motion Representation. In C. Freksa, W. Brauer, C. Habel, K.F. Wender (eds.), Spatial Cognition II — Integrating Abstract Theories, Empirical Studies, Formal Methods, and Practical Applications, 115–26, Berlin: Springer, 2000.Google Scholar
  9. 9.
    Y. Nihei. A Preliminary Study on the Geometrical Illusion of Motion Path: The Kinetic Illusion. Tohoku Psychologica Folia, 32, 108–14, 1973.Google Scholar
  10. 10.
    C. Peterken, B. Brown, K. Bowman. Predicting the future position of a moving target. Perception, 20, 5–16, 1991.CrossRefGoogle Scholar
  11. 11.
    C. M. Privitera, L. Shastri. Temporal compositional processing by a DSOM hierarchical model, Technical Reports 94704-1198, International Computer Science Institute Berkeley, Ca, 1996.Google Scholar
  12. 12.
    D. Raab. Statistical facilitation of simple reaction time. Transact. N.Y. Acad. of Sci. 43:574–90, 1962.Google Scholar
  13. 13.
    V. S. Ramachandran, S. M. Anstis. Extrapolation of motion path in human visual perception, Vision Research, 23, 83–85, 1983.CrossRefGoogle Scholar
  14. 14.
    F. Röhrbein, K. Schill, C. Zetzsche. Intermodal Sensory Interactions for Ecologically Valid Intensity Changes as Caused by Moving Observers or Moving Objects. In H.H. Bülthoff, M. Fahle, K. Gegenfurthner, H. Mallot (eds.), TWK 2000 —Beiträge zur 3. Tübinger Wahrnehmungskonferenz (62). Kirchentellinsfurth: Knirsch Verlag, 2000.Google Scholar
  15. 15.
    K. Schill, C. Zetzsche. A model of visual spatio-temporal memory: the icon revisited, Psychological Research, 57, 88–102, 1995.Google Scholar
  16. 16.
    J. Tani. Model-based learning for mobil robot navigation from the dynamical systems perspective, IEEE Trans. Systems, Man and Cybernetics (PartB), 26(3), 421–36, 1996.CrossRefGoogle Scholar
  17. 17.
    P. Wenderoth, M. Johnson. Relationship between the Kinetic, Alternating-Line, and Poggendorff Illusions: The Effects of Interstimulus Interval, Inducing Parallels, and Fixation. Perception and Psychophysics, 34(3), 273–79, 1983.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Florian Röhrbein
    • 1
  • Kerstin Schill
    • 1
  • Volker Baier
    • 2
  • Klaus Stein
    • 2
  • Christoph Zetzsche
    • 1
  • Wilfried Brauer
    • 2
  1. 1.Institut für Medizinische PsychologieLudwig-Maximilians-Universität MünchenGermany
  2. 2.Institut für InformatikTechnische Universität MünchenGermany

Personalised recommendations