Perceptual color spacing derived from maximum likelihood multidimensional scaling.

The canonical application of multidimensional scaling (MDS) methods has been to color dissimilarities, visualizing these as distances in a low-dimensional space. Some questions remain: How well can the locations of stimuli in color space be recovered when data are sparse, and how well can systematic individual variations in perceptual scaling be distinguished from stochastic noise? We collected triadic comparisons for saturated and desaturated sets of Natural Color System samples, each set forming an approximate hue circle. Maximum likelihood MDS was used to reconstruct the configuration of stimuli more accurately than the standard "vote-count" approach.