RAP:: a new framework for visual categorization

Cognitive science might almost be defined as several disciplines communicating their different perspectives on the mind, the common object of study. However, domain-specific concepts and techniques can prevent, rather than foster, a communication of viewpoints. In this article, we develop a new framework for visual categorization in which the interaction between Represented (R) information and Available (A) information determine the Potent (P) information (symbolically, Rcircle timesAapproximate toP). We argue and illustrate that this framework helps to establish a common language to articulate issues common to low-, mid-, and high-level vision. More importantly, we present new techniques with which to visualize the so-far elusive constructs of representation and potent information.