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An exponential filter model predicts lightness illusions.

Astrid Zeman1, Kevin R Brooks2, Sennay Ghebreab3

  • 1Department of Cognitive Science, ARC Centre of Excellence in Cognition and its Disorders, Macquarie University Sydney, NSW, Australia ; Commonwealth Scientific and Industrial Research Organisation Marsfield, NSW, Australia ; Perception in Action Research Centre, Macquarie University Sydney, NSW, Australia.

Frontiers in Human Neuroscience
|July 10, 2015
PubMed
Summary
This summary is machine-generated.

A new image statistics model explains lightness illusions like simultaneous contrast and White's effect. It accurately predicts human perception without needing V1-style orientation selectivity, offering a simpler explanation for visual context effects.

Keywords:
ODOGassimilationcontrastexponentialfilterillusionlightnessmodel

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Area of Science:

  • Visual perception
  • Computational neuroscience
  • Image processing

Background:

  • Lightness perception is influenced by surrounding context, leading to phenomena like simultaneous contrast and assimilation.
  • Existing models like the oriented difference-of-Gaussian (ODOG) explain these illusions but can be complex.
  • There is a need for simpler, biologically plausible models that capture these visual effects.

Purpose of the Study:

  • To introduce and evaluate a novel computational model for lightness perception based on image statistics.
  • To determine if this model can account for both contrast and assimilation effects in lightness illusions.
  • To compare the model's predictive performance against established models and human data.

Main Methods:

  • Developed a model using a family of exponential filters with varying sizes and shapes, inspired by image statistics.
  • Incorporated an optional second stage of normalization based on contrast gain control.
  • Tested the model on a standard set of 27 lightness illusions and compared predictions to human data.

Main Results:

  • The best single filter predicted the direction of lightness effects for 21 out of 27 illusions.
  • Combining filters improved prediction accuracy, reaching 24 out of 27 illusions with asymptotic performance.
  • The model's performance matched the best ODOG variants but with greater parsimony, demonstrating V1-style orientation-selectivity is not required.

Conclusions:

  • A low-level model based on image statistics can effectively explain a wide range of lightness illusions, including contrast and assimilation.
  • The proposed model offers a parsimonious and effective alternative to more complex models for understanding visual context effects.
  • This work suggests that complex neural computations may not be necessary to explain basic lightness perception phenomena.