Nonlinearities and Adaptation of Color Vision from Sequential Principal Curves Analysis

Here we address the simultaneous explanation of (i) the nonlinear behavior of achromatic and chromatic mechanisms in a fixed adaptation state, and (ii) the change of such behavior, i.e. adaptation, under the change of observation conditions. Both phenomena emerge directly from samples through a single method: the Sequential Principal Curves Analysis (SPCA) with local metric. SPCA is a new learning technique to derive a set of sensors adapted to the data using different optimality criteria (e.g. infomax or error minimization). Moreover, in order to reproduce the empirical adaptation reported under D65 and A illuminations, a new database of colorimetrically calibrated images of natural objects under these illuminants was gathered, thus overcoming the limitations of available databases.
The results obtained by applying SPCA show that the psychophysical behavior on color discrimination thresholds, discount of the illuminant and corresponding pairs in asymmetric color matching, emerge directly from realistic data regularities assuming no a priori functional form. These results provide stronger evidence for the hypothesis of a statistically driven organization of color sensors. Moreover, the obtained results suggest that the non-uniform resolution of color sensors at this low abstraction level may be guided by an error minimization strategy rather than by an information maximization goal.