Modelling the orthographic neighbourhood for Japanese kanji

Japanese kanji recognition experiments are typically narrowly focused, and feature only native speakers as participants. It remains unclear how to apply their results to kanji similarity applications, especially when learners are much more likely to make similarity-based confusion errors. We describe an experiment to collect authentic human similarity judgements from participants of all levels of Japanese proficiency, from non-speaker to native. The data was used to construct simple similarity models for kanji based on pixel difference and radical cosine similarity, in order to work towards genuine confusability data. The latter model proved the best predictor of human responses.