This article describes a class of computational models that help us understand some of the most important characteristics of human memory. The computational models are called parallel distributed processing (PDP) models because memories are stored and retrieved in a system consisting of a large number of simple computational elements, all working at the same time and all contributing to the outcome. They are sometimes also called connectionist models because the knowledge that governs retrieval is stored in the strengths of the connections among the elements.

The article begins with a common metaphor for human memory, and shows why it fails to capture several key characteristics of memory that are captured by the PDP approach. Then a brief statement of the general characteristics of PDP systems is given. Following this, two specific models are presented that capture key characteristics...