Cognitive processing of Chinese verbal irony: the role of reverse adjacency relations in reaction time and accuracy

Traditional theories of irony, such as Grice's Standard Pragmatic Model and Sperber and Wilson's echoic mention theory, inadequately explain culture-specific irony processing in Mandarin. This study, building on Huang's framework which posits that irony comprehension relies on recognizing gradient reverse adjacency relations within conceptual hierarchies, investigates the cognitive mechanisms underlying the comprehension of Chinese verbal irony by employing a self-paced reading and judgment task via an online platform (Wenjuanxing) with 75 native Mandarin speakers, focusing on the role of reverse adjacency relations in reaction time (RT) and accuracy. Participants were required to evaluate 60 sentences whether they are positive or negative comments. These sentences are divided into three conditions: experimental (irony with reverse adjacency relations), control (irony without such relations), and baseline (non-ironic). Results revealed statistically significant difference across conditions, for experimental ironic sentences (6,012 ms) compared to control (4,901 ms) and baseline sentences (3,987 ms all <i>p</i> < 0.001). Accuracy rates followed a similar pattern, with the experimental condition (64.1%) lower than the control (73.6%) and baseline (82.3%, all <i>p</i> < 0.01). These findings highlight the cognitive cost of resolving reverse adjacency relations, supporting Huang's framework, challenging universalist models and emphasizing the need for culture-sensitive frameworks in figurative language studies. Additionally, this study suggests un explored factors influencing irony comprehension in terms of a speed-accuracy tradeoff by a moderate positive correlation (<i>r</i> = 0.4, <i>p</i> < 0.001) between RT and accuracy cross all conditions and advances cognitive pragmatics by integrating relational hierarchies into irony theories and offers implications for cross-cultural communication, language pedagogy, and NLP systems.