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The distinction between computing and retrieving word pronunciations has had an enduring influence on models of reading. Early expressions of dual route ideas (Baron & Strawson, 1976; Forster & Chambers, 1973) became formalized by Coltheart et al. (2001) in the Dual Route Cascaded (DRC) model: A reader can arrive at a word’s pronunciation in two ways: 1) Decoding its letters to phonemes and producing the aggregated results – the computed route (also called sublexical, assembled, indirect). 2) Retrieving the pronunciation stored with its orthographic word‐form – the retrieved route (also called lexical, addressed, direct).

For a skilled reader, the difference between the two routes escapes notice because reading experience has established familiar lexical representations for many words. Thus, with appropriate experience, a reader may pronounce choir as easily as chore, unaware that the first resulted from the retrieval of a stored pronunciation associated with its spelling, while the second might have resulted from either route depending on familiarity with the word.

Both the DRC and PDP models can simulate word reading performance. For PDP models, the structure of mental representations emerges from many cycles of pattern association and error‐reduction learning. The DRC model, in the tradition of classic models with fixed assumptions, predicts experimental data based on a fixed architecture. Coltheart et al. (2001) showed that dual route models provide many specific, correct predictions of experimental results. The fundamental difference between the two models is between a model that learns – without necessarily showing either the time course or the pattern of learning outcomes of an actual learner – and a model that has already learned and is now ready to read any word or letter string one can throw at it. Narrowing the gap between these models are approaches that add a learning component to the DRC model (Pritchard et al., 2016) and combine elements of connectionist and DRC modeling (Perry et al., 2007; see Seidenberg et al., this volume for discussion).