Quantifying and predicting chromatic thresholds

Perceptual thresholds measured in the two-dimensional chromatic diagram are elliptical in shape. Across different parts of the chromatic diagram, these ellipses vary in their sizes, their tilting angles, and in how much they elongate. Overall, the chromatic thresholds exhibit intriguing patterns that were reflected in McAdams measurements in 1942. Previously, da Fonseca and Samengo (2016) used a neural model combined with Fisher information (a quantification of perceptual thresholds) to predict the pattern of chromatic thresholds measured in human observers. The model assumes linear cone responses paired with Poisson noise. I furthered the analysis, and studied two additional aspects of chromatic perception. First, I quantified how the pattern of chromatic thresholds vary when the proportion of three cone types (short-, mid-, and long-wavelength) varies. This analysis potentially leads to efficient estimation of thresholds across the chromatic diagram. Second, I analyzed to what extent the assumption of Poisson noise contributes to the threshold predictions. Surprisingly, eliminating Poisson noise betters the model prediction. So in addition to Poisson noise, I examined three alternative noise assumptions, and achieved improved predictions to MacAdams data. At last, I examined an application using the improved model-predictions. The total number of cones, as well as the proportion of S cone vary across retinal eccentricities. I showed that these two variations predict chromatic threshold patterns across retinal eccentricities are drastically different.