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Stochastic Guided Search Model for Search Asymmetries in Visual Search Tasks

  • Takahiko Koike
  • Jun Saiki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2525)

Abstract

We propose a stochastic guided search model for search asymmetries. Traditional saliency-based search model cannot account for the search asymmetry. Search asymmetry is likely to reflect changes in relative saliency between a target and distractors by the switch of target and distractor. However, the traditional models with a deterministic WTA always direct attention to the most salient location, regardless of relative saliency. Thus variation of the saliency does not lead to the variation of search efficiency in the saliency-based search models. We show that the introduction of a stochastic WTA enables the saliencybased search model to cause the variation of the relative saliency to change search efficiency, due to stochastic shifts of attention. The proposed model can simulate asymmetries in visual search.

Keywords

Visual Search False Detection Search Model Relative Saliency Search Asymmetry 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Takahiko Koike
    • 1
  • Jun Saiki
    • 1
  1. 1.Department of Intelligence Science and TechnologyGraduate School of Informatics, Kyoto UniversityKyotoJAPAN

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