Skip to main content

Neurobiological Models of Visual Attention

  • Chapter
Visual Attention Mechanisms

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. D. Broadbent. Perception and communication, Pergamon Press, NY. (1958).

    Book  Google Scholar 

  2. J. Deutsch, D. Deutsch. Attention: Some theoretical considerations, Psych. Review 70, 80–90.(1963).

    Article  Google Scholar 

  3. D. Norman. Toward a theory of memory and attention, Psych. Review 75, 522–536. (1968).

    Article  Google Scholar 

  4. Treisman. The effect of irrelevant material on the efficiency of selective listening American J. Psychology 77 533–546. (1964).

    Article  Google Scholar 

  5. P. Milner. A model for visual shape recognition, Psych. Rev. 81, 521–535. (1974).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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. Treisman, G. Gelade. A feature integration theory of attention, Cognitive Psychology 72:97–136. (1980).

    Article  Google Scholar 

  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. F. Crick. Function of the thalamic reticular complex: The searchlight hypothesis,Proc. Natl. Acad. Sci. USA 81, 4586–4590. (1984).

    Article  Google Scholar 

  11. Koch, S. Ullman. Shifts in selective visual attention: Towards the underlying neural circuitry, Human Neurobiology 4, 219–227. (1985).

    Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  14. J. Wolfe. Guided search 2.0: a revised model of visual search, Psychonomic Bulletin and Review, 1(2):202–238. (1994).

    Article  Google Scholar 

  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. P. Sandon. Simulating visual attention, J. Cognitive Neuroscience 2:213–231. (1990).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  18. J.K. Tsotsos. Analyzing Vision at the Complexity Level, Behavioral and Brain Sciences 13-3, p423– 445. (1990).

    Article  Google Scholar 

  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. 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).

    Article  Google Scholar 

  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. 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. 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. M. Mozer. The perception of multiple objects, MIT Press, Cambridge, MA. (1991).

    Google Scholar 

  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. Niebur, C. Koch, C. Rosin. An oscillation-based model for the neural basis of attention, Vision Research 33, 2789–2802. (1993).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  29. Postma et al. SCAN: a scalable model of attentional selection, Neural Networks 10(6): 993–1015. (1997).

    Article  Google Scholar 

  30. R. Desimone, J. Duncan. Neural mechanisms of selective visual attention,Annual Reviews of Neuroscience 18, 193–222. (1995).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  32. LaBerge. Attentional processing: The brain’s art of mindfulness. Cambridge, MA: Harvard University Press. (1995).

    Google Scholar 

  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).

    Article  Google Scholar 

  34. K. Cave. The FeatureGate model of visual selection, Psychological Res. 62: 182–194. (1999).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer Science+Business Media New York

About this chapter

Cite this chapter

Tsotsos, J.K. (2002). Neurobiological Models of Visual Attention. In: Cantoni, V., Marinaro, M., Petrosino, A. (eds) Visual Attention Mechanisms. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0111-4_21

Download citation

  • DOI: https://doi.org/10.1007/978-1-4615-0111-4_21

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-4928-0

  • Online ISBN: 978-1-4615-0111-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics