Modeling of automatic capture and focusing of visual attention

An explanation, based on simple analysis of the spatiotemporal variations of the visual environment, is given to the automatic capture and focusing of visual attention. It is assumed that the transmittance for the sensory signals is modulated by separate control circuits that sample input from the same area of the visual field but at a lower resolution. When these circuits detect significant spatial and/or temporal variations, they “open gates” for the more accurate information arising from the same area. If the variations are related to the spatial resolution, which varies within wide limits over the retina, the visual field is “opened” up to a radius where it captures the most salient structures of the image. If the temporal variations of the signals are further emphasized, the high spatial frequencies begin to dominate. If then the gaze is moved by a small amount, the transmittance of the foveal signal paths is activated strongest.

[1]  R. A. Davidoff.  From Neuron to Brain , 1977, Neurology.

[2]  Martin Golubitsky,et al.  What Geometric Visual Hallucinations Tell Us about the Visual Cortex , 2002, Neural Computation.

[3]  W. Precht.  The synaptic organization of the brain G.M. Shepherd, Oxford University Press (1975). 364 pp., £3.80 (paperback) , 1976, Neuroscience.

[4]  G. Shepherd.  The Synaptic Organization of the Brain , 1979 .

[5]  C. Koch,et al.  An oscillation-based model for the neuronal basis of attention , 1993, Vision Research.

[6]  R. Eckhorn,et al.  Coherent oscillations: A mechanism of feature linking in the visual cortex? , 1988, Biological Cybernetics.

[7]  D. Hubel,et al.  Laminar and columnar distribution of geniculo‐cortical fibers in the macaque monkey , 1972, The Journal of comparative neurology.

[8]  S Marcelja,et al.  Mathematical description of the responses of simple cortical cells. , 1980, Journal of the Optical Society of America.

[9]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[10]  L. Palmer,et al.  The retinotopic organization of area 17 (striate cortex) in the cat , 1978, The Journal of comparative neurology.

[11]  J. Daugman.  Two-dimensional spectral analysis of cortical receptive field profiles , 1980, Vision Research.

[12]  Gustavo Deco,et al.  A neurodynamical model for selective visual attention using oscillators , 2001, Neural Networks.

[13]  D. Hubel,et al.  Integrative action in the cat's lateral geniculate body , 1961, The Journal of physiology.

[14]  J. Rovamo,et al.  Visual resolution, contrast sensitivity, and the cortical magnification factor , 2004, Experimental Brain Research.

[15]  J. P. Jones,et al.  An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. , 1987, Journal of neurophysiology.

[16]  Samuel Kaski,et al.  Self-Organized Formation of Various Invariant-Feature Filters in the Adaptive-Subspace SOM , 1997, Neural Computation.

[17]  Ch. von der Malsburg,et al.  A neural cocktail-party processor , 1986, Biological Cybernetics.

[18]  S. Lehmkuhle,et al.  Research Note Transient Visual Attention is Dominated by the Magnocellular Stream , 1997, Vision Research.

[19]  Toshio Aoyagi,et al.  A model for feature linking via collective oscillations in the primary visual cortex , 2004, Biological Cybernetics.

[20]  Alex M. Andrew.  THE HANDBOOK OF BRAIN THEORY AND NEURAL NETWORKS, edited by Michael A. Arbib, MIT Press, Cambridge, Mass. and London, England, 1998, xvp1118 pp, ISBN 0-262-51102-9, paperback, £49.95 (cloth-bound version also available, originally published in 1995, ISBN 262–01148–4, £147.95) , 1999 .

[21]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[22]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[23]  Samuel Kaski,et al.  Winner-take-all networks for physiological models of competitive learning , 1994, Neural Networks.

[24]  Teuvo Kohonen,et al.  Emergence of invariant-feature detectors in the adaptive-subspace self-organizing map , 1996, Biological Cybernetics.