Models of skilled reading and learning to read

There was a substantial legacy of detailed theoretical and computational modelling of reading within the Reading Program. At the commencement of the CCD in 2011, we already had available to us the Dual Route Cascaded (DRC) computational model, developed by Emeritus Professor Max Coltheart over the preceding two decades (Coltheart, et al., 1993, 2001). This model is a full computational implementation of the processes involved in reading aloud, which most notably distinguishes between those reading processes required for reading new, unfamiliar words (the nonlexical route) and those required for reading known, familiar words (the lexical route). The model has been extremely successful in simulating a wide range of basic phenomena in reading, and in accounting for several different patterns of reading disorder.

However, one key feature of the DRC model was lacking at the commencement of the CCD: the model did not learn. This limited the usefulness of the model for addressing some basic questions about learning to read. Therefore, we set as a key aim of the Reading Program to build a version of DRC that had a learning component, and one that was consistent with current psychological theory. Of course, learning to read is extremely complex, involving honing multiple cognitive subskills, and developing a computational model of the acquisition of all of these processes at once would not be viable. Hence, we focussed on one key learning process: the self-teaching mechanism. This is the mechanism by which children who already have basic nonlexical decoding skills, and who are encountering novel printed words in texts, learn to read these new words without direct instruction from a teacher. The result of our work was ST-DRC: a computational model of the self-teaching hypothesis based on the DRC model of reading, published in the major journal Cognitive Science (Pritchard, Coltheart, Marinus, & Castles, 2018). This model represents a major legacy of the CCD, providing a rich theoretical and computational learning framework for the next generation of reading researchers.