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Computational modeling has tremendously advanced our understanding of the processes involved in normal and impaired reading. While previous research has mainly focused on simulating reading aloud of monosyllabic words in English, the present special issue highlights some new directions in the field of word recognition and reading aloud. These new lines of research include the learning orthographic and phonological representations in both supervised and unsupervised networks, and the extension of existing models to multi-syllabic word processing both in English and in other languages, such as Italian, French and German. The special issue also covers hotly debated issues concerning the front-end of the reading process, the neural plausibility of current models of word recognition and naming, the viability of Bayesian approaches to understanding reading, as well as the long-standing opposition between rule-based and statistical learning. Finally, this special issue includes simulation work on novel benchmark phenomena, such as the effects of fast phonology, masked onset priming and syllabic neighbourhood. Altogether, the present special issue provides a critical analysis and synthesis of current computational models of reading and cutting edge research concerning the next generation of computational models of word recognition and reading aloud.