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.