Attention, Perception, & Psychophysics

, Volume 79, Issue 6, pp 1593–1614 | Cite as

Measuring and modeling salience with the theory of visual attention

  • Alexander Krüger
  • Jan Tünnermann
  • Ingrid Scharlau


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 combined, salience adds up additively.


Salience Visual attention Bayesian inference Theory of visual attention Computational modeling 



This work was supported by the German Research Foundation (DFG) via Grant SCHA 1515/6-1 to Ingrid Scharlau.


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Copyright information

© The Psychonomic Society, Inc. 2017

Authors and Affiliations

  • Alexander Krüger
    • 1
  • Jan Tünnermann
    • 1
  • Ingrid Scharlau
    • 1
  1. 1.Faculty of Arts and HumanitiesPaderborn UniversityPaderbornGermany

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