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Abstract

Distributed connectionist models of mental representation (also termed PDP or parallel distributed processing, or ANN or artificial neural networks) constitute a fundamental alternative to the associative or schematic models that have been much more prevalent in social psychology. A connectionist model is made up of a large number of very simple processing units, richly interconnected and able to send signals to each other depending on their momentary activation levels. No individual processing unit represents a meaningful concept; instead, overall patterns of activation hold representational meaning. This article emphasizes the novel properties of connectionist representation that might appeal to theorists and researchers in social psychology, including their context sensitivity and flexibility, ability to represent prototypes and exemplars within a single network, and ability to determine whether a stimulus is familiar even before the stimulus can be identified or categorized.