These analyses indicate a close correspondence between empirical color matching coefficients and factor models of environmental spectral data. This result is consistent with a hypothesis that the human visual system exploits the reduced rank of the spectral distributions found in the environment in order to reduce the redundancy inherent in the visual input stream. Furthermore, this correspondence indicates that the process of color perception may operate in a manner which is functionally equivalent to that of factor analysis; flexibly and quickly adducing statistical invariants from distributions of information in the environment.
A three factor maximum likelihood model where the factor loadings are fixed to be empirical color matching coefficients and fit to environmental spectral data provides an RMSEA fit which is significantly better than that of an unconstrained three factor analysis. This result suggests that the human visual system performs near to an optimum value for an ideal trichromatic system composed of three linear additive components.
A four factor maximum likelihood model fits the reflectance spectral distributions marginally better than a three factor maximum likelihood model. This suggests that representations of the perceptual space of color vision may be slightly improved by using a four dimensional model.
A fourth factor which is similar to the extracted factors can be calculated as a nonlinear combination of the first three extracted factors, suggesting that a four dimensional representational space could be neurally calculated from the outputs of trichromatic retinal sensors. A calculated fourth factor has the advantage of allowing each color to be a position in a four dimensional metric space, while preserving a more accurate estimate of the covariance in the spectral environment than would a linear three dimensional representation.