Evidence from a within-language comparison in Japanese for orthographic depth theory: Monte Carlo simulations, corpus-based analyses, neural networks, and human experiment

Abstract

The orthographic depth theory assumes that reading “deep” orthographies relies on lexical semantics more than “shallow” orthographies. Although Japanese kanji is a representative “deep” case, some scholars argue that kanji reading does not particularly recruit more lexical semantics than kana (the system of syllabic writing used for Japanese consisting of two forms). To reconcile this inconsistency, we ran a Monte Carlo simulation and found that orthographic neighbors in kanji had higher semantic similarities than those in kana. We further conducted a semantic space analysis (‘Word2Vec’) and showed that there was significant radical-level orthographic-semantic consistency in kanji characters. Furthermore, we demonstrated that this consistency had a positive effect on language performance in models (in terms of next-character prediction) and humans (in terms of semantic plausibility judgment). These findings suggest that radicals in kanji may help children to efficiently learn to use the vast number of characters present in Japanese.