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 chromatic thresholds. 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 other types of noise assumptions, and achieved improved predictions to MacAdam’s data.

Last, I showed an application using model-predicted chromatic thresholds. The total number of cones, and the proportion of $S$ cone vary across retinal eccentricities. I examined how these variations predict drastically different chromatic threshold patterns across retinal eccentricities.