Brightness contrast-contrast induction model predicts assimilation and inverted assimilation effects

Area of Science:

• Visual perception
• Computational neuroscience
• Psychophysics

Background:

• Classical assimilation effects describe how intermediate luminance patches change perceived brightness based on their surrounds.
• Inverted assimilation effects occur when the inducing patches are darker or lighter than the target patch, reversing the effect.
• Existing models fail to predict the full range of assimilation and inverted assimilation effects.

Purpose of the Study:

• To propose a new compound brightness model based on contrast-contrast induction.
• To demonstrate the model's ability to predict various assimilation and inverted assimilation effects.
• To validate the model's performance using a Jacobi iteration process for edge integration.

Main Methods:

• Development of a compound brightness model incorporating second-order opponent receptive fields.
• Calculation of local and remote contrast within the model.
• Application of second-order adaptation (contrast-contrast induction) as a core mechanism.
• Utilizing a Jacobi iteration variation for edge integration analysis.

Main Results:

• The proposed compound brightness model successfully predicts classical assimilation effects.
• The model also accurately predicts inverted assimilation effects.
• The model's predictive power extends to various stimulus configurations and their resulting brightness illusions.

Conclusions:

• The compound brightness model, based on second-order adaptation, offers a unified explanation for diverse assimilation effects.
• This computational approach advances the understanding of visual brightness perception and contrast adaptation.
• The model provides a robust framework for future research into visual processing mechanisms.