The authors present a theoretical framework for understanding the roles of the hippocampus and neocortex in learning and memory. This framework incorporates a theme found in many theories of hippocampal function: that the hippocampus is responsible for developing conjunctive representations binding together stimulus elements into a unitary representation that can later be recalled from partial input cues. This idea is contradicted by the fact that hippocampally lesioned rats can learn nonlinear discrimination problems that require conjunctive representations. The authors' framework accommodates this finding by establishing a principled division of labor, where the cortex is responsible for slow learning that integrates over multiple experiences to extract generalities whereas the hippocampus performs rapid learning of the arbitrary contents of individual experiences. This framework suggests that tasks involving rapid, incidental conjunctive learning are better tests of hippocampal function. The authors implement this framework in a computational neural network model and show that it can account for a wide range of data in animal learning.