Timing is Everything: Temporal Scaffolding of Semantic Surprise in Humor
For humor researchers and cognitive scientists, this work reframes humor as temporally scaffolded, showing that timing is a central factor in naturalistic humor appreciation, but it is an incremental advance over existing incongruity-resolution theories.
The paper proposes the Dual Prediction Violation (DPV) framework to study how timing and semantic content interact in humor. Analyzing 828 Chinese stand-up performances, they find that temporal features (e.g., pauses before punchlines) predict audience appreciation more strongly than semantic incongruity, with peak semantic violations mattering more than average levels.
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.