Modelling word recognition and reading aloud

Abstract

Computational modelling has tremendously advanced our understanding of the processes involved in normal and impaired reading. 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 of orthographic and phonological representations in both supervised and unsupervised networks, the extension of existing models to multisyllabic word processing both in English and in other languages, such as Italian, French, and German, and the confrontation of these models with data from masked priming. Some of the contributors to the Special Issue also address hotly debated issues concerning the front-end of the reading process, the viability of Bayesian approaches to understanding masked and unmasked priming, as well as the longstanding debate about the role of rules versus statistics in language processing. Thus, 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.

Acknowledgements

Preparation of this Special Issue was partially funded by an ERC grant No. 230313 to JG and an Alexander-von-Humboldt fellowship to JCZ.