Unveiling the intensity-ambiguity relationships among affective and lexico-semantic variables in Chinese characters and the character-word relationships in Chinese two-character words.

Abstract

Understanding the lexical characteristics of Chinese characters is crucial given their extensive usage and unique logographic structure. In this study, we normed affective ratings (valence and arousal) for 3971 Chinese characters. We investigated the relationships between intensity (mean rating) and ambiguity (rating variability) of these affective variables, alongside additional lexico-semantic variables from Su et al., Behavior Research Methods, 55(6), 2989-3008, (2022). Drawing on lexical data from 25,281 two-character words available in the Chinese Lexicon Project (Tse et al., Behavior Research Methods, 49(4), 1503-1519, 2017, Behavior Research Methods, 55(8), 4382-4402, 2023; Chan & Tse, Behavior Research Methods, 56(7), 7574-7601, 2024), we further explored cross-level relationships between character-level and word-level variables. Multiple regression analyses controlling for various lexical variables revealed several noteworthy patterns. First, we identified a quadratic valence-arousal relationship, such that characters with extreme valence ratings (either highly positive or highly negative) elicited higher arousal compared to neutral characters. This relationship was moderated by arousal ambiguity, partially consistent with previous findings (Brainerd et al. Journal of Experimental Psychology: General, 150(8), 1476-1499, 2021a), Second, we observed consistent quadratic intensity-ambiguity relationships across all variables, supporting the quadratic law proposed by Brainerd et al. Journal of Memory and Language, 121, 104286, (2021b). Finally, significant positive associations occurred between character-level variables and their corresponding word-level variables for both the first and second characters. The strength of these cross-level relationships varied across affective and lexico-semantic variables and may further be influenced by semantic transparency. Overall, our findings advance the understanding of affective and semantic features of Chinese characters and offer insights into the cross-level integration of characters' and words' lexical characteristics. The data reported in this paper are available at: https://osf.io/kh4yx .