Choosing a choice function: granting new capabilities to ART

New capabilities are granted to the adaptive resonance theory (ART) model by modifying its choice function, and hence the order of search through categories. These capabilities are achieved by: 1) allowing the choice function to depend on as many constant parameters under external control as desired, including possibly vigilance, 2) using multiple choice functions to separate categories into various subsets, and 3) dynamically varying the parameters between input presentations, without resetting the network weights. This is possible without interfering with the orienting subsystem, the vigilance test, nor the learning rule of the original model. It is shown that the main requirement for a choice function is that learning must increase its value for the selected category. If this requirement is met, and the learning rule is compatible with self-stabilization, then the value of the weight vector of each committed category is unique, and self-stabilization is guaranteed for an arbitrary sequence of analog inputs and parameters