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.

正字法深度理论假设阅读“深”正字法比“浅”正字法更依赖于词汇语义。尽管日语汉字是一个具有代表性的“深度”案例，但一些学者认为，汉字阅读并没有比假名（用于日语的音节书写系统，由两种形式组成）特别吸收更多的词汇语义。为了调和这种不一致，我们运行了蒙特卡罗模拟，发现汉字中的正字法邻居比假名中的正字法邻居具有更高的语义相似性。我们进一步进行了语义空间分析（'Word2Vec'）并表明汉字字符具有显着的部首级拼写语义一致性。此外，我们证明了这种一致性对模型（在下一个字符预测方面）和人类（在语义合理性判断方面）的语言表现有积极影响。这些发现表明，汉字中的部首可能有助于儿童有效地学习使用日语中存在的大量字符。