Investigating the effects of semantic radical consistency in chinese character naming with a corpus-based measure.

(SRC). Specifically, we utilized the Word2Vec models to construct a Chinese semantic space and quantified the SRC for 3,423 characters. To evaluate the SRC, we first conducted linear mixed-effect modeling analyses to verify the explanatory power of SRC on a large-scale Chinese character naming reaction times. Second, we investigated the SRC effect by conducting a word naming task based on traditional factorial designs. Both the linear mixed-effect modeling and factorial naming results demonstrated that SRC was a unique and reliable variable to account for the variance in traditional Chinese character naming reaction times. The results indicated this innovative, corpus-derived SRC was able to effectively reflect the semantic transparency level by measuring semantic distances among characters in the same semantic radical category. Further investigations on the interaction between SRC and phonetic radical consistency demonstrated the cooperative nature between phonological and semantic reading pathways. (PsycInfo Database Record (c) 2025 APA, all rights reserved).