Edelman suggests that any shape is encoded by an excitation vector with components corresponding to excitations of corresponding neuronal modules. This results in discrimination of stimuli in a shape space of low dimensionality. Similar vector encoding is present in color vision. Red-green, blue-yellow, bright and dark neurons are modules that represent a number of different color stimuli in color space of low dimensionality. Vector encoding allows effective computation of color differences and color similarities. Such a neuronal vector-encoding approach has also (...) been applied to the perception of visual movement, line orientation, and stereopsis. (shrink)

The perceptual circularity demonstrated by R. Shepard with respect to hue turns out to be a sphericity of color perception based on color excitation vectors of neuronal level. The spherical color model implicitly contains information concerning generalization under color learning. Subjective color differences are “computed” in neuronal nets being represented by amplitudes of evoked potentials triggered by color change. [Shepard].

Multidimensional scaling of subjective color differences has shown that color stimuli are located on a hypersphere in four-dimensional space. The semantic space of color names is isomorphic with perceptual color space. A spherical four-dimensional space revealed in monkeys and fish suggests the primacy of common neuronal basis.