A review of computational models of word recognition and pronunciation

How do we recognize words and assign a pronunciation? Computational models provide a formal description of the mechanisms and principles that guide the reading process. I review and evaluate the Interactive-Activation Model (IAM), Dual Route Cascaded (DRC) model, the Parallel Distributed Processing (PDP) model, and the Connectionist Dual Processing (CDP) model, as well as LEX, a variant of the MINERVA model of memory. I evaluate each model’s ability to account for consistency effects, serial effects, syllable effects, and phonological effects. Consistency effects pose a problem for the rule-based pronunciation of the DRC. Serial effects pose a problem for the purely parallel PDP models. Phonological effects pose a problem for all models save CDP. All models suffer from the distribution problem, weakening each model’s ability to learn spelling-to-sound relationships. LEX is the only model that handles polysyllabic words. As none of the models provide a complete answer to the question of ‘how do we read?’, ‘how do we pronounce?’, or ‘how do we recognize words?’, I outline a set of principles as guidelines for future model development. Models of reading should learn, include a visual attention mechanism, be sensitive to phonology, and account for meaning and spelling in addition to recognizing words and pronouncing them.