A Computational Model of the Self-Teaching Hypothesis Based on the Dual-Route Cascaded Model of Reading

The self-teaching hypothesis describes how children progress toward skilled sight-word reading. It proposes that children do this via phonological recoding with assistance from contextual cues, to identify the target pronunciation for a novel letter string, and in so doing create an opportunity to self-teach new orthographic knowledge. We present a new computational implementation of self-teaching within the dual-route cascaded (DRC) model of reading aloud, and we explore how decoding and contextual cues can work together to enable accurate self-teaching under a variety of circumstances. The new model (ST-DRC) uses DRC's sublexical route and the interactivity between the lexical and sublexical routes to simulate phonological recoding. Known spoken words are activated in response to novel printed words, triggering an opportunity for orthographic learning, which is the basis for skilled sight-word reading. ST-DRC also includes new computational mechanisms for simulating how contextual information aids word identification, and it demonstrates how partial decoding and ambiguous context interact to achieve irregular-word learning. Beyond modeling orthographic learning and self-teaching, ST-DRC's performance suggests new avenues for empirical research on how difficult word classes such as homographs and potentiophones are learned.