Proceedings of the Annual Meeting of the Cognitive Science Society

We propose a new view of word priming in attractor networks, which involves deepening the basins of attraction for primed words. In a network that maps from orthographic to phonological word representations via semantics, this view of priming leads to novel predictions about the interactions between orthographically and/or semantically similar primes and targets, when compared on an orthographic versus a semantic retrieval task. W e confirm these predictions in computer simulations of long-term priming in a word recognition network. Connectionist models have strongly influenced current thinking about the nature of human memory storage and retrieval processes. One reason for their appeal is that they can account for a wide range of human performance on tasks such as word recognition (McClelland and Rumelhart, 1981), reading (Seidenberg and McClelland, 1989), and repetition priming (McClelland and Rumelhart, 1986). Further, because connectionist models make relatively specific assumptions about the mechanisms of cognitive processes, they can lead to novel predictions about human performance. One of the most exciting developments in the last decade of human memory research is the characterization of implicit memory (Graf &; Schacter, 1985; Schacter, 1985), a form of automatic, unconscious retrieval of previously encountered material. A widely used experimental method for testing implicit m e m ory is repetition priming, in which the accuracy or speed of processing is measured on successive presentations of a target stimulus. Evidence of implicit memory is observed when subjects are more accurate or efficient in responding to previously studied targets than to new targets. The priming literature is highly relevant to connectionist models of learning and memory for two reasons: 1) Priming effects can be extremely long-lasting, ranging from minutes to many hours, or even months, and apparently reflect fundamental automatic ("unsupervised") learning processes employed by the brain. 2) W h e n the prime and target are not identical, but have similar input and/or semantic features, the priming effects may range from facilitation to inhibition; these effects can shed light on the nature of h u m a n memory organization, and provide constraints on the representations employed in connectionist models. In this paper, w e first review the previous connectionist accounts of priming. W e then propose a new view of word priming in attractor networks with orthographic and semantic levels of representation, which involves deepening the basins of attraction for primed words. This leads to some novel predictions about the interactions between primes and targets, which we explore in computer simulations.