An attractor model of lexical conceptual processing: Simulating semantic priming

An attractor network was trained to compute from word form to semantic representations that were based on subject-generated features. The model was driven largely by higher-order semantic structure. The network simu-lated two recent experiments that employed items included in its training set (McRae and Boisvert, 1998). In Simulation 1, short stimulus onset asyn-chrony priming was demonstrated for semantically similar items. Simulation 2 reproduced subtle effects obtained by varying degree of similarity. Two predictions from the model were then tested on human subjects. In Simula-tion 3 and Experiment 1, the items from Simulation 1 were reversed, and both the network and subjects showed minimally different priming effects in the two directions. In Experiment 2, consistent with attractor networks but con-trary to a key aspect of hierarchical spreading activation accounts priming was determined by featural similarity rather than shared superordinate cat-egory. It is concluded that semantic-similarity priming is due to featural overlap that is a natural consequence of distributed representations of word meaning. I