Saliency Map Models for Stimulus-Driven Mechanisms in Visual Search: Neural and Functional Accounts

  • Jun Saiki
  • Takahiko Koike
  • Matthew deBrecht
Conference paper


Saliency map models have been influential in neurocognitive modeling of visual attention. Despite recent applications to complex visual scenes, some basic characteristics of the model remain elusive. Here, we address two issues; neural plausibility of saliency computation, and functional account of search asymmetry phenomenon by a saliency map model. With some modifications, we showed that saliency can be computed by a neurally plausible way, and that search asymmetry can be accounted for only by stimulus-driven mechanisms.


Visual Search Visual Attention Visual Search Task Synaptic Depression Search Asymmetry 
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  1. 1.
    Itti, L., & Koch, C. (2000). A saliency-based machanism for overt and covert shifts of visual attention. Vision Research, 40, 1489–1506.PubMedCrossRefGoogle Scholar
  2. 2.
    Koch, C., & Ullman, S. (1985). Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiology, 4, 219–227.PubMedGoogle Scholar
  3. 3.
    deBrecht, M., & Saiki, J. (2006). Neural network implementation of saliency map. Neural Networks, 19, 1467–1474.CrossRefGoogle Scholar
  4. 4.
    Abbott, L. F., Varela, J. A., Sen, K., & Nelson, S. B. (1997). Synaptic depression and cortical gain control. Science, 275, 220–224.PubMedCrossRefGoogle Scholar
  5. 5.
    Yantis, S. (1993). Stimulus-driven attentional capture. Current Directions in Psychological Science, 2, 156–161.CrossRefGoogle Scholar
  6. 6.
    Treisman, A., & Souther, S. (1985). Search asymmetry: a diagnostic for preattentive processing of separable features. Journal of Experimental Psychology: General, 114, 285–310.CrossRefGoogle Scholar
  7. 7.
    Koike, T., & Saiki, J. (2006). Stochastic saliency-based search model for search asymmetry with uncertain targets., Neurocomputing, 69, 2112–2126.CrossRefGoogle Scholar
  8. 8.
    Navalpakkam, V., & Itti, L. (2005). Modeling the influence of task on attention. Vision Research, 45, 205–231.PubMedCrossRefGoogle Scholar
  9. 9.
    Saiki, J., Koike, T., Takahashi, K., & Inoue, T. (2005). Visual search asymmetry with uncertain targets. Journal of Experimental Psychology: Human Perception and Performance, 31, 1274–1287.PubMedCrossRefGoogle Scholar
  10. 10.
    Luck, S. J., & Vogel, E. K. (1997). The capacity of visual working memory for features and conjunctions. Nature, 390, 279–281.PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jun Saiki
    • 1
  • Takahiko Koike
  • Matthew deBrecht
  1. 1.Unit of Cognitive Science Graduate School of Human and Environmental StudiesKyoto University Yoshida-NihonmatsuchoSakyo-kuJapan

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