Centre National de la Recherche Scientifique and Universite ́ de Provence

At least 3 different types of computational model have been shown to account for various facets of both normal and impaired single word reading: (a) the connectionist triangle model, (b) the dual-route cascaded model, and (c) the connectionist dual process model. Major strengths and weaknesses of these models are identified. In the spirit of nested incremental modeling, a new connectionist dual process model (the CDP model) is presented. This model builds on the strengths of 2 of the previous models while eliminating their weaknesses. Contrary to the dual-route cascaded model, CDP is able to learn and produce graded consistency effects. Contrary to the triangle and the connectionist dual process models, CDP accounts for serial effects and has more accurate nonword reading performance. CDP also beats all previous models by an order of magnitude when predicting individual item-level variance on large databases. Thus, the authors show that building on existing theories by combining the best features of previous models—a nested modeling strategy that is commonly used in other areas of science but often neglected in psychology—results in better and more powerful computational models