Exploiting color space geometry for visual stimulus design across animals

Color vision represents a vital aspect of perception that ultimately enables a wide variety of species to thrive in the natural world. For humans, a suite of color management tools, developed since the early 20th century and used in our everyday devices, allow for a straightforward investigation of human color vision. However, unified methods for constructing chromatic visual stimuli in a laboratory setting for other animals are lacking. This has limited the study of visual perception in animals, where the spatiotemporal and chromatic components of vision are often treated separately, and results are difficult to compare across different hardware setups. We have developed stimulus design methods and an accompanying programming package to efficiently probe the color space of any species in which the photoreceptor spectral sensitivities are known. Our hardware-agnostic approach incorporates photoreceptor models within the framework of the principle of univariance. These models inherently represent the spectral distribution of light as a low-dimensional vector, similar to the tristimulus codes used to represent human colors. Using constrained fitting procedures, experimenters will be able to identify the most effective way to combine multiple light sources to create desired distributions of light, and thus easily reconstruct relevant stimuli for mapping the color space of an organism. We include methodology to transfer color stimuli between devices with different light sources. We also show how to incorporate uncertainty of photoreceptor spectral sensitivities as well as how to reconstruct natural scenes in the spatial and spectral domain using our approach and recent hardware advances. Our methods support broad applications in color vision science and provide a color management tool for uniform stimulus designs across experimental systems. More generally, our fitting procedures can also be applied to design stimuli for other sensory organs with a diverse set of filters.