Factors underlying masked priming effects in competitive network models of visual word recognition. In S Kinoshita & SJ Lupker

This chapter develops a precise description of the factors underlying masked priming effects in a specific competitive network model--the interactive activation (IA) model (McClelland and Rumelhart, 1981). Because the resulting expression is formulated in terms of standard psycholinguistic variables, the analysis presented here helps to bridge the divide between purely computational accounts and verbal theories of visual word recognition and priming. This approach is assisted by a framework for partitioning the set of competitors of a target stimulus, and a graphical technique for depicting the course of the competitive process in competitive network models of visual word recognition. The development of simple regression models that are able to fully capture the effects of priming within a complex (interactive, nonlinear, and dynamic) network model is a valuable outcome that has broader implications for the computational model of cognition. The analysis of priming effects in the model also leads to a number of predictions that can be tested empirically.