Transitional probability between characters as a component of sentence processing in Chinese

Abstract

Abstract This study explores the role of transitional probability (TP) in sentence processing in Chinese, a writing system that presents unique challenges due to its character-based structure and lack of word boundaries. The research investigates how the statistical regularities of character meaning, as captured by TP, aid in word segmentation and impact reading comprehension. Utilizing a moving window task, the study examines the processing speed of characters in high versus low TP conditions. Findings reveal that characters in high TP bigram conditions (indicating a consistent semantic association within a bigram) are processed more quickly, underscoring the importance of this statistical property of characters in Chinese sentence reading. These findings challenge conventional notions in Chinese linguistics concerning the relationship between characters, morphemes, and semantics, and suggests an alternative perspective on (and the need for reevaluation of) character-level semantics. The study also highlights the influence of prosodic context on reading speed, indicating that anticipatory linguistic patterns shape reader processing.