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Explaining brightness illusions using spatial filtering and local response normalization.

Alan E Robinson1, Paul S Hammon, Virginia R de Sa

  • 1Department of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0515, USA. robinson@cogsci.ucsd.edu

Vision Research
|April 27, 2007
PubMed
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We developed new computational models for brightness perception, improving predictions of visual illusions like White's Effect. These models enhance neural plausibility by refining response normalization techniques.

Area of Science:

  • Computational Neuroscience
  • Visual Perception
  • Psychophysics

Background:

  • Brightness perception is complex and prone to illusions.
  • Existing models like the ODOG model offer partial explanations.
  • Further refinement is needed for greater accuracy and neural plausibility.

Purpose of the Study:

  • Introduce novel computational models of brightness perception.
  • Enhance existing models to better predict visual illusions.
  • Improve the neural plausibility of computational models.

Main Methods:

  • Extended Blakeslee and McCourt's ODOG model.
  • Implemented multiscale oriented difference-of-Gaussian filters.
  • Modified response normalization for neural plausibility (nearby receptive fields and spatial frequencies).

Related Experiment Videos

Main Results:

  • The new models successfully account for a wide range of brightness illusions, including White's Effect.
  • Constraining normalization to nearby receptive fields and spatial frequencies improved model effectiveness.
  • Both modifications increased the models' predictive power for brightness illusions.

Conclusions:

  • The enhanced computational models provide a more accurate and neurally plausible account of brightness perception.
  • Refined response normalization is crucial for understanding visual illusions.
  • These models offer a valuable tool for studying the neural basis of visual perception.