My Research Projects
I approach the study of animal color vision through historical and theoretical perspectives. My research incorporates historical findings on the neural processing of color signals modern mathematical models of color perception.
Three Opponencies
The ways color signals are encoded in the retina are reflected in the signals we record from retinal neurons and in the ways we perceive colors. Each of these three steps are identified based on their antagonistic, or opponent responses. The mechanism is called cone opponency, the phenomena measured from neurons is spectral opponency, and the perceptual experience is color opponency. In this project, I reviewed the literature to determine which non-primate vertebrate species have been reported to possess cone opponent circuits. For all of those species, I also collected the spectrally opponent information. While color opponency is possible in other animals, we first need to understand more about color categorization to explore this further.
Opponent Receptor Noise Limited Model
We cannot know for sure how an animal experiences color. However, we can try to make predictions about what objects they can see, and what objects they can’t see, based on the features we know about their visual systems. To do this, scientists have generated mathematical models of color discriminability. The Receptor Noise Limited (RNL) model is a widely accepted and implemented computational model for understanding color perception in animals. However, it does not account for cone opponency, a property of the visual system which compares photoreceptor signals in an antagonistic manner in the retina and early visual processing stages. To address this limitation, we developed a new model that incorporates cone opponency into the RNL framework.
Modeling Avian Spectral Discrimination
Spectral discrimination is the ability to discriminate between colors across the light spectrum. The receptor noise-limited model (RNL) is a framework for predicting color discrimination of various species based on a set of visual parameters, predominantly the noise in the photoreceptors. In this study, we model the spectral discrimination abilities of two types of birds: ultraviolet sensitive (UVS) and violet sensitive (VS), utilizing a spectral discrimination curve derived from the output of the RNL. We concluded that VS species generally exhibit higher spectral discrimination compared to UVS species. This result contrasts with previous modeling studies, where they found that UVS birds are likely more sensitive to color differences due to modeling natural reflectances rather than spectral lights.
