Neurobiological Models of Visual Attention

  • John K. Tsotsos

Abstract

The number of models that address the neurobiology of visual attention in a non-trivial manner is small. The number that have real computational tests on actual images is even smaller. However, the history of important ideas that contribute to our understanding requires one to scan not only the neurobiological literature but also the psychological and computational literature. A selected historical perspective on these ideas is presented in this paper.

Keywords

Pyramid Suffix Dyslexia 

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References

  1. 1.
    D. Broadbent. Perception and communication, Pergamon Press, NY. (1958).CrossRefGoogle Scholar
  2. 2.
    J. Deutsch, D. Deutsch. Attention: Some theoretical considerations, Psych. Review 70, 80–90.(1963).CrossRefGoogle Scholar
  3. 3.
    D. Norman. Toward a theory of memory and attention, Psych. Review 75, 522–536. (1968).CrossRefGoogle Scholar
  4. 4.
    Treisman. The effect of irrelevant material on the efficiency of selective listening American J. Psychology 77 533–546. (1964).CrossRefGoogle Scholar
  5. 5.
    P. Milner. A model for visual shape recognition, Psych. Rev. 81, 521–535. (1974).CrossRefGoogle Scholar
  6. 6.
    S. Grossberg, G. Carpenter, et al. The what-and-where filter: a spatial mapping neural network for object recognition and image understanding, Computer Vision and Image Understanding 69(1): 1–22. (1998).CrossRefGoogle Scholar
  7. 7.
    S. Grossberg. How does the cerebral cortex work? Learning, attention and grouping by the laminar circuits of visual cortex, Technical Report CAS/CNS-97-023 (1998).Google Scholar
  8. 8.
    Treisman, G. Gelade. A feature integration theory of attention, Cognitive Psychology 72:97–136. (1980).CrossRefGoogle Scholar
  9. 9.
    von der Malsburg. The correlation theory of brain function, Internal Rpt. 81-2, Dept. of Neurobiology, Max-Planck-Institute for Biophysical Chemistry, Gottingen, Germany. (1981).Google Scholar
  10. 10.
    F. Crick. Function of the thalamic reticular complex: The searchlight hypothesis,Proc. Natl. Acad. Sci. USA 81, 4586–4590. (1984).CrossRefGoogle Scholar
  11. 11.
    Koch, S. Ullman. Shifts in selective visual attention: Towards the underlying neural circuitry, Human Neurobiology 4, 219–227. (1985).Google Scholar
  12. 12.
    Anderson, D. Van Essen. Shifter Circuits: a computational strategy for dynamic aspects of visual processing, Proc. Natl. Academy Sci. USA 84: 6297–6301. (1987).CrossRefGoogle Scholar
  13. 13.
    J. Wolfe, K. Cave, S. Franzel. Guided search: An alternative to the feature integration model for visual search, J. Exp. Psychology: Human Perception and Performance 15, 419–433. (1989).CrossRefGoogle Scholar
  14. 14.
    J. Wolfe. Guided search 2.0: a revised model of visual search, Psychonomic Bulletin and Review, 1(2):202–238. (1994).CrossRefGoogle Scholar
  15. 15.
    J. Wolfe, G. Gancarz. Guided Search 3.0: A Model of Visual Search Catches Up With Jay Enoch 40 Years Later, in V. Lakshminarayanan (Ed.), Basic and Clinical Applications Vision Science, Dordrecht, Netherlands: Kluwer Academic. p189–192. (1996).Google Scholar
  16. 16.
    P. Sandon. Simulating visual attention, J. Cognitive Neuroscience 2:213–231. (1990).CrossRefGoogle Scholar
  17. 17.
    R. Phaf, A. Van der Heijden, P. Hudson. SLAM: A connectionist model for attention in visual selection tasks, Cognitive Psychology 22, 273 - 341. (1990).CrossRefGoogle Scholar
  18. 18.
    J.K. Tsotsos. Analyzing Vision at the Complexity Level, Behavioral and Brain Sciences 13-3, p423– 445. (1990).CrossRefGoogle Scholar
  19. 19.
    J.K. Tsotsos. An Inhibitory Beam for Attention Selection, in Spatial Vision in Humans and Robots, ed. by L. Harris and M. Jerkin, p3l3 - 331, Cambridge University Press. (1993).Google Scholar
  20. 20.
    J.K. Tsotsos, S. Culhane, W. Wai, Y. Lai, N. Davis, F. Nuflo. Modeling visual attention via selective tuning, Artificial Intelligence 78(1-2),p 507 - 547. (1995).CrossRefGoogle Scholar
  21. 21.
    J.K. Tsotsos. Towards a Computational Model of Visual Attention, in Early Vision and Beyond, ed. by T. Papathomas, C, Chubb, A. Gorea, E. Kowler, MIT Press/Bradford Books, p2O7– 218. (1995).Google Scholar
  22. 22.
    J.K. Tsotsos, S. Culhane, F. Cutzu. From Theoretical Foundations to a Hierarchical Circuit for Selective Attention, Visual Attention and Cortical Circuits, ed. by J. Braun, C. Koch & J. Davis, MIT Press (in press).Google Scholar
  23. 23.
    S. Ahmad. VISIT: a neural model of covert visual attention, in Advances in Neural Information Processing Systems, edited by J.E. Moody, et al., 4:420–427, San Mateo, CA: Morgan Kaufmann. (1992).Google Scholar
  24. 24.
    M. Mozer. The perception of multiple objects, MIT Press, Cambridge, MA. (1991).Google Scholar
  25. 25.
    Olshausen, et al. A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information, J. of Neuroscience, 13(1):4100–4719. (1993).Google Scholar
  26. 26.
    Niebur, C. Koch, C. Rosin. An oscillation-based model for the neural basis of attention, Vision Research 33, 2789–2802. (1993).CrossRefGoogle Scholar
  27. 27.
    E. Niebur, C. Koch. A model for the neuronal implementation of selective visual attention based on temporal correlation among neurons, J. Comput. Neuroscience 1(1), 141– 158.(1994).CrossRefGoogle Scholar
  28. 28.
    M. Usher, E. Niebur. Modeling the temporal dynamic of IT neurons in visual search: A mechanism for top-down selective attention, J. Cognitive Neuroscience 8:4, 311–327. (1996).CrossRefGoogle Scholar
  29. 29.
    Postma et al. SCAN: a scalable model of attentional selection, Neural Networks 10(6): 993–1015. (1997).CrossRefGoogle Scholar
  30. 30.
    R. Desimone, J. Duncan. Neural mechanisms of selective visual attention,Annual Reviews of Neuroscience 18, 193–222. (1995).CrossRefGoogle Scholar
  31. 31.
    W. X. Schneider. VAM: neuro-cognitive model for visual attention control of segmentation, object recognition, and space-based motor action, Visual Cognition 2, 331–375.(1995).CrossRefGoogle Scholar
  32. 32.
    LaBerge. Attentional processing: The brain’s art of mindfulness. Cambridge, MA: Harvard University Press. (1995).Google Scholar
  33. 33.
    L. Itti, C. Koch, E. Niebur. A model for saliency-based visual attention for rapid scene analysis, IEEE Trans. Pattern Analysis and Machine Intelligence 20, 1254–1259. (1998).CrossRefGoogle Scholar
  34. 34.
    K. Cave. The FeatureGate model of visual selection, Psychological Res. 62: 182–194. (1999).CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • John K. Tsotsos
    • 1
  1. 1.Dept. of Computer Science, and Centre for Vision researchYork UniversityTorontoCanada

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