@article{6075,
  abstract     = {{For almost three decades, the theory of visual attention (TVA) has been successful in mathematically describing and explaining a wide variety of phenomena in visual selection and recognition with high quantitative precision. Interestingly, the influence of feature contrast on attention has been included in TVA only recently, although it has been extensively studied outside the TVA framework. The present approach further develops this extension of TVA’s scope by measuring and modeling salience. An empirical measure of salience is achieved by linking different (orientation and luminance) contrasts to a TVA parameter. In the modeling part, the function relating feature contrasts to salience is described mathematically and tested against alternatives by Bayesian model comparison. This model comparison reveals that the power function is an appropriate model of salience growth in the dimensions of orientation and luminance contrast. Furthermore, if contrasts from the two dimensions are comb}},
  author       = {{Krüger, Alexander and Tünnermann, Jan and Scharlau, Ingrid}},
  issn         = {{1943-3921}},
  journal      = {{Attention, Perception, & Psychophysics}},
  keywords     = {{Salience, Visual attention, Bayesian inference, Theory of visual attention, Computational modeling, Inference, Object Recognition, Theories, Visual Perception, Visual Attention, Luminance, Perceptual Orientation, Statistical Probability, Stimulus Salience, Computational Modeling}},
  number       = {{6}},
  pages        = {{1593 -- 1614}},
  title        = {{{Measuring and modeling salience with the theory of visual attention.}}},
  doi          = {{10.3758/s13414-017-1325-6}},
  volume       = {{79}},
  year         = {{2017}},
}

@article{6071,
  abstract     = {{Particular differences between an object and its surrounding cause salience, guide attention, and improve performance in various tasks. While much research has been dedicated to identifying which feature dimensions contribute to salience, much less regard has been paid to the quantitative strength of the salience caused by feature differences. Only a few studies systematically related salience effects to a common salience measure, and they are partly outdated in the light of new findings on the time course of salience effects. We propose Bundesen’s Theory of Visual Attention (TV A) as a theoretical basis for measuring salience and introduce an empirical and modeling approach to link this theory to data retrieved from temporal-order judgments. With this procedure, TV A becomes applicable to a broad range of salience-related stimulus material. Three experiments with orientation pop-out displays demonstrate the feasibility of the method. A 4th experiment substantiates its applicability t}},
  author       = {{Krüger, Alexander and Tünnermann, Jan and Scharlau, Ingrid}},
  issn         = {{1895-1171}},
  journal      = {{Advances in Cognitive Psychology}},
  keywords     = {{salience, visual attention, Bayesian inference, theory of visual attention, computational modeling, Visual Attention, Computational Modeling, Inference, Judgment, Statistical Probability}},
  number       = {{1}},
  pages        = {{20 -- 38}},
  title        = {{{Fast and conspicuous? Quantifying salience with the theory of visual attention.}}},
  doi          = {{10.5709/acp-0184-1}},
  volume       = {{12}},
  year         = {{2016}},
}