Attention in Early Vision: Some Psychophysical Insights

  • Kuntal Ghosh
  • Sankar K. Pal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4840)


The Spotlight Models of attention that rely upon a bottom-up approach specifically through the dorsal pathways, can be modeled using multi-scale Gaussian pyramids with excitatory-inhibitory feedforward cellular neural networks (CNN) as feature detectors. Here we propose a modified disinhibitory zero-feedback CNN model derived out of a linear combination of three Gaussians only, that explains many brightness perception based psychophysical phenomena unexplainable with the old model and in the process predicts three different input cloning templates for global smoothing, global enhancement, as well as controlled smoothing and enhancement of retinal images within the focus of attention. The proposed approach provides new clues, based on the psychophysical stimuli, suggestive of a role of top-down attentional control possibly through the ventral pathways, even at the stage of low-level vision.


Retinal Ganglion Cell Test Patch Retinal Image Cellular Neural Network Early Vision 
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.
    Eriksen, C.W., St. James, J.D.: Visual attention within and around the field of focal attention: a zoom lens model. Perception & Psychophysics 40, 225–240 (1986)CrossRefGoogle Scholar
  2. 2.
    Niebur, E., Itti, L., Koch, C.: Controlling the focus of visual selective attention. In: van Hemmen, J.L., et al. (eds.) Models of Neural Networks IV, Springer-Verlag, New York, USA (2001)Google Scholar
  3. 3.
    Chua, L.O., Roska, T.: Cellular Neural Networks and Visual Computing. Cambridge University Press, Cambridge, UK (2002)CrossRefGoogle Scholar
  4. 4.
    Hochstein, S., Shapley, R.M.: Linear and nonlinear spatial subunits in Y cat retinal ganglion cells. Journal of Physiology 262, 265–285 (1976)CrossRefGoogle Scholar
  5. 5.
    McIlwain, J.T.: Some evidence concerning the physiological basis of the periphery effect in cat’s retina. Experimental Brain Research 1, 265–271 (1966)CrossRefGoogle Scholar
  6. 6.
    Ikeda, H., Wright, H.J.: Functional organization of the periphery effect in retinal ganglion cells. Vision Research 12, 1857–1879 (1972)CrossRefGoogle Scholar
  7. 7.
    Kaplan, E., Benardete, E.: The dynamics of primate retinal ganglion cells. Progress in Brain Research 134, 1–18 (2001)CrossRefGoogle Scholar
  8. 8.
    Enroth-Cugell, C., Jakiela, H.G.: Suppression of cat retinal ganglion cell responses by moving patterns. Journal of Physiology 302, 49–72 (1980)CrossRefGoogle Scholar
  9. 9.
    Kruger, J.: The shift-effect enhances X- and suppresses Y-type response charecteristics of cat retinal ganglion cells. Brain Research 201, 71–84 (1984)CrossRefGoogle Scholar
  10. 10.
    Passaglia, L., Enroth-Cugell, C., Troy, J.B.: Effects of remote stimulation on the mean firing rate of cat retinal ganglion cells. Journal of Neuroscience 21, 5794–5803 (2001)Google Scholar
  11. 11.
    Rensink, R.A., O’Regan, J.K., Clark, J.J.: Image flicker is as good as saccades in making large scene changes invisible. Perception 24(suppl.), 26–27 (1995)Google Scholar
  12. 12.
    Palmer, S.E.: Vision Science: Photons to Phenomenology. The MIT Press, Cambridge, Massachusetts (1999)Google Scholar
  13. 13.
    Dennett, D.C.: Toward a cognitive theory of consciousness. In: Brainstorms: Philosophical Essays on Mind and Psychology, The MIT Press, Cambridge (1987)Google Scholar
  14. 14.
    Marr, D.: Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W.H. Freeman and Company, New York (1982)Google Scholar
  15. 15.
    Gregory, R.: Flagging the present with qualia. In: Rose, S. (ed.) From Brains to Consciousness, Penguin Books, London (1998)Google Scholar
  16. 16.
    Ma, S.D., Li, B.: Derivative computation by multiscale filters. Image and Vision Computing 16, 43–53 (1998)CrossRefGoogle Scholar
  17. 17.
    Ghosh, K., Sarkar, S., Bhaumik, K.: A possible explanation of the low-level brightness-contrast illusions in the light of an extended classical receptive field model of retinal ganglion cells. Biological Cybernetics 94, 89–96 (2006)CrossRefzbMATHGoogle Scholar
  18. 18.
    Ghosh, K., Sarkar, S., Bhaumik, K.: Proposing new methods in low-level vision from the Mach band illusion in retrospect. Pattern Recognition 39, 726–730 (2006)CrossRefGoogle Scholar
  19. 19.
    Blakeslee, B., McCourt, M.E.: A multiscale spatial filtering account of the white effect, simultaneous brightness contrast and grating induction. Vision Research 39, 4361–4377 (1999)CrossRefGoogle Scholar
  20. 20.
    Shou, T., Wang, W., Yu, H.: Orientation biased extended surround of the receptive field of cat retinal ganglion cells. Neuroscience 98, 207–212 (2000)CrossRefGoogle Scholar
  21. 21.
    Vogel, D.: A biologically plausible model of associative memory which uses disinhibition rather than long-term potentiation. Brain and Cognition 45, 212–228 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Kuntal Ghosh
    • 1
  • Sankar K. Pal
    • 1
  1. 1.Center for Soft Computing Research, Indian Statistical Institute, 203 B.T. Road, Kolkata-700108India

Personalised recommendations