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The processes underlying word reading are shaped by statistical properties of the writing system. According to some theoretical perspectives (e.g. Harm & Seidenberg, 2004) reading acquisition should be understood as an exercise in statistical learning. Statistical learning (SL) involves the extraction of organizing principles from a set of inputs. Several lines of research provide convergent evidence supporting the connection between SL and reading acquisition (e.g., Arciuli & Simpson, 2012; Frost et al., 2014; Bogaerts et al., 2015). An obstacle to fully appreciating the theoretical and educational implications of these findings is that SL is itself not well understood. In this paper, we review the current literature on SL with a particular focus on organizing this literature by grounding it in theories of learning and memory more generally. This approach can clarify the nature of SL and provide a framework for understanding its role in reading, reading acquisition, and reading disorders.

This paper reports two experiments in which the artificial orthography paradigm was used to investigate the mechanisms underlying learning to read. In each experiment, participants were taught the meanings and pronunications of words written in an unfamiliar orthography and the statistical structure of the mapping between written and spoken forms (O-P) was manipulated independently of the mapping between written forms and their meanings (O-S). Our results support three main conclusions. First, the statistical structure of O-P and O-S mappings determined how easily each of those mappings was learned, suggesting that the learning of both mappings engages a common statistical learning mechnism. Second, learning to read is a cooperative process, in that learning in any particular component of the reading system is influenced by knowledge stored in the rest of the system. Finally, knowledge of sublexical regularities can be acquired as the result of exposure to words embodying those regularities.

This paper provides a review of the connectionist perspective on the role of morphology in visual word recognition. Several computational models of morphological effects in reading are described and relationships between these models, models of past tense production, and models of other aspects of word recognition are traced. Limitations of extant models are noted, as are some of the technical challenges that must be solved to develop the next generation of models. Finally, some directions for future research are identified.