Simulating language-specific and language-general effects in a statistical learning model of Chinese reading

Many theoretical models of reading assume that different writing systems require different processing assumptions. For example, it is often claimed that print-to-sound mappings in Chinese are not represented or processed sub-lexically. We present a connectionist model that learns the print-to-sound mappings of Chinese characters using the same functional architecture and learning rules that have been applied to English. The model predicts an interaction between item frequency and print-to-sound consistency analogous to what has been found for English, as well as a language-specific regularity effect particular to Chinese. Behavioral naming experiments using the same test items as the model confirmed these predictions. Corpus properties and the analyses of internal representations that evolved over training revealed that the model was able to capitalize on information in “phonetic components” – sub-lexical structures of variable size that convey probabilistic information about pronunciation. The results suggest that adult reading performance across very different writing systems may be explained as the result of applying the same learning mechanisms to the particular input statistics of writing systems shaped by both culture and the exigencies of communicating spoken language in a visual medium.