This article describes the Dual Route Cascaded (DRC) model, a computational model of visual word recognition and reading aloud. The DRC is a computational realization of the dual-route theory of reading, and is the only computational model of reading that can perform the 2 tasks most commonly used to study reading: lexical decision and reading aloud. For both tasks, the authors show that a wide variety of variables that influence human latencies influence the DRC model's latencies in exactly the same way. The DRC model simulates a number of such effects that other computational models of reading do not, but there appear to be no effects that any other current computational model of reading can simulate but that the DRC model cannot. The authors conclude that the DRC model is the most successful of the existing computational models of reading.

The development of reading depends on phonological awareness across all languages so far studied. Languages vary in the consistency with which phonology is represented in orthography. This results in developmental differences in the grain size of lexical representations and accompanying differences in developmental reading strategies and the manifestation of dyslexia across orthographies. Differences in lexical representations and reading across languages leave developmental "footprints" in the adult lexicon. The lexical organization and processing strategies that are characteristic of skilled reading in different orthographies are affected by different developmental constraints in different writing systems. The authors develop a novel theoretical framework to explain these cross-language data, which they label a psycholinguistic grain size theory of reading and its development.

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 CDPϩ 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, CDPϩ is able to learn and produce graded consistency effects. Contrary to the triangle and the connectionist dual process models, CDPϩ accounts for serial effects and has more accurate nonword reading performance. CDPϩ 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.

Alphabetic orthographies differ with respect to how consistently letters map onto sounds. For example, in Finnish, Italian, or Greek, a given letter is almost always pronounced the same in different words. These writing systems are referred to as consistent or transparent. In contrast, in English and to a lesser degree in French, a given letter is often pronounced differently in different words (e.g., a in cat, was, saw, made, and car). These writing systems are referred to as inconsistent or opaque. The orthographic consistency of a writing system has been shown to influence fundamental aspects of skilled reading, such as the importance of phonological information or the grain size of basic reading units (Frost, Katz, & Bentin, 1987;Ziegler, Perry, Jacobs, & Braun, 2001).Over the past decade, it has become clear that orthographic consistency is the key factor determining the rate of reading acquisition across different languages (for a review, see ). One of the most striking demonstrations comes from a cross-language investigation in which reading performance was measured at the end of Grade 1 in 14 European countries (Seymour, Aro, & Erskine, 2003). Whereas reading accuracy in most transparent languages (e.g., Italian, German, Greek, Spanish, and Finnish) reached ceiling at this time, accuracy in less transparent languages (e.g., Portuguese, French, and Danish) was lower, around 80%. However, reading performance in English, the least transparent of the orthographies studied, was only 34%. This basic finding has been replicated in a number of small-scale experiments (Bruck,