Opponent Receptor Noise Limited Model

While the subjective experience of color perception in animals is unknowable, we can leverage our understanding of their visual systems to make informed predictions about their ability to discriminate between different objects. Mathematical models of color discriminability are powerful tools in this endeavor. One such computational model is the Receptor Noise Limited (RNL) model, which has been extensively implemented to study color perception in various species.

 

However, the RNL model overlooks a crucial property of the visual system known as cone opponency, wherein photoreceptor signals are combined in an antagonistic manner within the retina and early visual processing stages. This property of the visual system may influence color perception in ways not captured by the existing model. To address this limitation, we have developed a novel model that seamlessly integrates cone opponency mechanisms into the RNL framework.

 

By incorporating this biologically relevant feature, our modified model aims to provide a more comprehensive and accurate representation of color discriminability in animals. This advancement not only refines our predictive capabilities but also paves the way for deeper insights into the intricate interplay between neural mechanisms and color perception across species.