Unsettling questions about semantic ambiguity in connectionist models

S. Joordens and D. Besner (1994) described an attempt to simulate a semantic ambiguity advantage in lexical decision using a connectionist model (Masson, 1991) that was based on a Hopfield (1982) network. The question of the validity of the ambiguity advantage is briefly considered, and the assumptions behind the simulation results reported by Joordens and Besner are critically examined. The model used by Joordens and Besner is compared with other connectionist models, and alternative methods of simulating lexical decisions with this class of models are discussed. It is concluded that further empirical evidence is required and that a number of modeling alternatives need to be explored before strong conclusions can be made about the validity of the semantic ambiguity advantage and about the best way to model the effect. Representing and processing ambiguous words is a challenge for distributed memory models, which are also known as parallel distributed processing or connectionist models. This class of models represents lexical knowledge in weights associated with links that connect a set of processing units to one another and instantiates a known word by evoking its unique pattern of activation across the processing units. The instantiation of a word as a pattern of activation across an entire collection of units contrasts with the classic view of lexical representation in which each word (or word meaning) is represented by a single unit or node in a network (e.g.