Determining which features in an environment are salient given a task, salience assignment, is a central problem in machine learning. A related phenomenon, contingency ( the conditions under which relative salience among environemental features is acquired), is central to learning and memory in animal psychology. This paper presents an analysis of a set of empirical data on contingency and an algorithm for the salience assignment problem. The algorithm presented is implmented in a working computer profram which interacts with a simulated environement to produce contingent asssociative learning corresponding to relevant behavioral data. The model also makes specific empirical predictions that can be experimentally tested.
