A neural network model of 3-D lightness perception is presented which builds upon the FACADE Theory Boundary Contour Sys(cid:173) tem/Feature Contour System of Grossberg and colleagues. Early ratio encoding by retinal ganglion neurons as well as psychophysi(cid:173) cal results on constancy across different backgrounds (background constancy) are used to provide functional constraints to the theory and suggest a contrast negation hypothesis which states that ratio measures between coplanar regions are given more weight in the determination of lightness of the respective regions.

Two University of Washington professors described this phenomenon in a 2014 research paper. 'Accurate perception of surface reflectance poses a significant computational problem for the visual system,' professors Maria Pereverzeva and Scott O. Murray, who authored the study, explained. 'The amount of light reflected by a surface is affected by a combination of factors including the surface's reflectance properties and illumination conditions. Other factors, such as the amount of light reflected by a surface, the orientation of the surface and whether or not it's 3D can affect our lightness perception of a given image Optical illusions arrange a series of patterns, images and colors or play with the way an object is lit in order to trick our brains into thinking something is there – when it is not. When light hits our retina, it takes about one-tenth of a second for our brain to translate that signal into perception, reports Discovery News.

A neural network model of 3-D lightness perception is presented which builds upon the FACADE Theory Boundary Contour System/Feature Contour System of Grossberg and colleagues. Early ratio encoding by retinal ganglion neurons as well as psychophysical results on constancy across different backgrounds (background constancy) are used to provide functional constraints to the theory and suggest a contrast negation hypothesis which states that ratio measures between coplanar regions are given more weight in the determination of lightness of the respective regions.

A neural network model of 3-D lightness perception is presented which builds upon the FACADE Theory Boundary Contour System/Feature Contour System of Grossberg and colleagues. Early ratio encoding by retinal ganglion neurons as well as psychophysical results on constancy across different backgrounds (background constancy) are used to provide functional constraints to the theory and suggest a contrast negation hypothesis which states that ratio measures between coplanar regions are given more weight in the determination of lightness of the respective regions.

A neural network model of 3-D lightness perception is presented which builds upon the FACADE Theory Boundary Contour System/Feature ContourSystem of Grossberg and colleagues. Early ratio encoding by retinal ganglion neurons as well as psychophysical resultson constancy across different backgrounds (background constancy) are used to provide functional constraints to the theory and suggest a contrast negation hypothesis which states that ratio measures between coplanar regions are given more weight in the determination of lightness of the respective regions.