A Neural Model of Visual Attention

van de Laar, Piërre; Heskes, Tom; Gielen, Stan

doi:10.1007/978-1-4471-3087-1_23

Piërre van de Laar^2,3,
Tom Heskes^2,3 &
Stan Gielen^2,3

198 Accesses
2 Citations

Abstract

We propose a biologically plausible neural model of selective covert visual attention. We show that this model is able to learn focussing on object-specific features. It has similar learning characteristics as humans in the learning and unlearning paradigm of Shiffrin and Schneider [8].

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References

C. Bundesen. A theory of visual attention. Psychological Review, 97 (4): 523–547, 1990.
Article Google Scholar
J. Johnides and H. Gleitman. A conceptual category effect in visual search: O as letter or as digit. Perception Psychophysics, 12 (6): 457–460, 1972.
Article Google Scholar
W. A. Johnston and V. J. Dark. Selective attention. Annual Review of Psychology, 37: 43–75, 1986.
Article Google Scholar
J. K. Kruschke. ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review, 99 (1): 22–44, 1992.
Article Google Scholar
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1: 541–551, 1990.
Article Google Scholar
B. A. Olshausen, C. H. Anderson, and D. C. Van Essen. A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information. The Journal of Neuroscience, 13(11):4700–4719, 1993.
Google Scholar
M. I. Posner and S. Dehaene. Attentional networks. Trends in Neurosciences, 17 (2): 75–79, 1994.
Article Google Scholar
R. M. Shiffrin and W. Schneider. Controlled and automatic human information processing: II. perceptual learning, automatic attending and a general theory. Psychological Review, 84 (2): 127–190, 1977.
Article Google Scholar
A. Treisman. Features and objects in visual processing. Scientific American, 255 (5): 106–115, 1986.
Article Google Scholar

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Authors and Affiliations

Department of Medical Physics and Biophysics, University of Nijmegen, Nijmegen, The Netherlands
Piërre van de Laar, Tom Heskes & Stan Gielen
RWCP Novel Functions SNN Laboratory, Nijmegen, The Netherlands
Piërre van de Laar, Tom Heskes & Stan Gielen

Authors

Piërre van de Laar
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Tom Heskes
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Stan Gielen
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Editor information

Editors and Affiliations

Dutch Foundation for Neural Networks (SNN), Geert Grooteplein Noord 21, 6525 EZ, Nijmegen, The Netherlands
Bert Kappen & Stan Gielen &

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van de Laar, P., Heskes, T., Gielen, S. (1995). A Neural Model of Visual Attention. In: Kappen, B., Gielen, S. (eds) Neural Networks: Artificial Intelligence and Industrial Applications. Springer, London. https://doi.org/10.1007/978-1-4471-3087-1_23

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DOI: https://doi.org/10.1007/978-1-4471-3087-1_23
Publisher Name: Springer, London
Print ISBN: 978-3-540-19992-2
Online ISBN: 978-1-4471-3087-1
eBook Packages: Springer Book Archive

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