🤖 AI Summary
Traditional theories of humor have emphasized semantic incongruity while overlooking the critical role of temporal structure in shaping humorous impact. This study introduces a Dual Prediction Violation (DPV) framework, leveraging large-scale natural language analysis of 828 Chinese stand-up comedy performances combined with quantitative measures of semantic surprise and temporal feature modeling. It systematically demonstrates, for the first time, the dominant influence of timing on humor appreciation. The findings reveal that strategic pauses commonly precede high-surprise punchlines, that temporally coupled semantic patterns effectively differentiate successful from unsuccessful performances, and that temporal features significantly outperform average semantic incongruity in predicting audience preference. By reconceptualizing humor as a temporally scaffolded cognitive process, this work extends predictive processing theory to naturalistic comedic contexts.
📝 Abstract
Humor is a fundamental cognitive phenomenon in which humans derive pleasure from the expectation violations and their resolution, exemplifying the brain's dynamic capacity for predictive processing. Classical humor theories emphasize semantic incongruity as the primary driver of amusement, yet overlook temporal dynamics despite comedians' intuition that "timing is everything." The extent to which temporal structure contributes to humor appreciation and how it interacts with semantic content remains poorly understood. Here, we propose the Dual Prediction Violation (DPV) framework to capture the interplay between content and timing. By analyzing 828 professional Chinese stand-up performances, we show that temporal features substantially outweigh semantic incongruity in predicting audience appreciation. Specifically, we find that peak semantic violations matter more than average incongruity levels, and pauses systematically lengthen before high-surprise punchlines--a strategic coupling that distinguishes successful from unsuccessful performances. These findings reframe humor as temporally scaffolded, where timing and semantic content operate in strategic coordination rather than independently. Our DPV framework bridges humor theory with predictive processing, demonstrating that temporal structure plays a central role in naturalistic humor appreciation with implications for understanding multi-scale prediction integration in linguistic processing.