Mixing Memory and Desire: Want and Will in Neural Modeling*

Values are critical for intelligent behavior, since values determine interests, and interests determine relevance. Therefore we address relevance and its role in intelligent behavior in animals and machines. Animals avoid exhaustive enumeration of possibilities by focusing on relevant aspects of the environment, which emerge into the (cognitive) foreground, while suppressing irrelevant aspects, which submerge into the background. Nevertheless, the background is not invisible, and aspects of it can pop into the foreground if background processing deems them potentially relevant. This illuminates the differences between representation in natural intelligence and (traditional) artificial intelligence. Traditionally artificial intelligence has started with simple, primitive features, and attempted to construct from them a representation of the environment. If too few features are used, then the processing is imprecise and crude. However, if sufficient features are used to permit precise processing in all contexts, then the system is defeated by the combinatorial explosion of features. In natural intelligence, in contrast, we begin with a nervous system that can process in real-time the “concrete space” represented by the interface between the animal’s nervous system and its environment. The separation of foreground from background then serves to increase the efficiency of this process. Instead of trying to construct the concrete world from abstract predicates, the brain projects the very highdimensional concrete world into lower dimensional subspaces; this projection is context-sensitive and rapidly adaptable. Therefore it is not vulnerable to the combinatorial explosion. We consider the connection between these ideas and the concepts of intentionality, as discussed by Brentano and Husserl, and information, as quantified by Shannon and Weaver. In particular, the ShannonWeaver measure ignores relevance, which is essential to biological * To appear in the proceedings of the Fifth Appalachian Conference on Behavioral Neurodynamics, October 18-20, 1996.