A Distributed Memory Model of the Associative Boost in Semantic Priming

Abstract

Evidence from priming studies indicates that both semantic and associative relations between pairs of words facilitate word recognition, and that pairs of words related in both ways (e.g. hammer-nail, cat-dog) produce an additional ‘associative boost’. We argue that while semantic priming may result from overlapping patterns of micro-features in a distributed memory model (e.g. Masson, 1991), associative priming is a result of frequent co-occurrence of words in the language. We describe a simple recurrent network, with distributed phonological and semantic representations, which is sensitive to the sequential occurrence of phonological patterns during training, and which produces associative facilitation of word recognition in a simulation of the priming task.

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