Timing is Everything: Temporal Scaffolding of Semantic Surprise in Humor
- URL: http://arxiv.org/abs/2605.00143v1
- Date: Thu, 30 Apr 2026 18:58:29 GMT
- Title: Timing is Everything: Temporal Scaffolding of Semantic Surprise in Humor
- Abstract summary: We show that temporal features substantially outweigh semantic incongruity in predicting audience appreciation.<n>These findings reframe humor as temporally scaffolded, where timing and semantic content operate in strategic coordination rather than independently.
- Score: 10.44838606556761
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Order Matters: LVLMs as Judges for Temporal Reasoning in Image Sequences [0.9841060883971746]
We argue that the reliance on Large Vision-Language Models as judges is fundamentally limited by architectural biases.<n>Our analysis reveals a profound performance dichotomy: while models appear competent in isolated pointwise scoring, they suffer a catastrophic collapse when required to perform pairwise discrimination of temporal order.<n>These biases, potentially rooted in causal masking and rotary embeddings, suggest that current transformer-based judges are inherently ill-equipped for long-form visual reasoning.
arXiv Detail & Related papers (2026-08-11T13:29:03Z) - From Prompts to Pavement Through Time: Temporal Grounding in Agentic Scene-to-Plan Reasoning [1.2777067998526854]
This work explores whether temporal conditioning within inter-agent communication can preserve or enhance coherence without introducing degradation in semantic or logical consistency.<n>Results show that while temporal conditioning reshapes reasoning style, it yields no statistically significant improvements in standard NLP-based correctness metrics.<n>These findings clarify the limits of prompt-based temporal grounding and establish the first empirical benchmark for temporal scene-to-plan reasoning.
arXiv Detail & Related papers (2026-05-19T13:18:35Z) - A Progressive Training Strategy for Vision-Language Models to Counteract Spatio-Temporal Hallucinations in Embodied Reasoning [49.61652671596548]
"Multi-image hallucination reasoning", where a massive performance drop between forward and temporal queries reveals a dependence on superficial shortcuts instead of genuine understanding.<n>To mitigate this, we develop a new dataset: Chain-of-temporal building on detailed reasoning into Chain steps and definitive judgments.<n>Our experiments demonstrate that this approach not only improves accuracy but also the forward-backward performance gap from over 70% to only 6.53%.
arXiv Detail & Related papers (2026-04-12T07:48:44Z) - Cognitive Pivot Points and Visual Anchoring: Unveiling and Rectifying Hallucinations in Multimodal Reasoning Models [65.57985131861399]
hallucinations are strongly correlated with cognitive bifurcation points that often exhibit high entropy states.<n>We propose V-STAR, a lightweight, holistic training paradigm designed to internalize visually aware reasoning capabilities.
arXiv Detail & Related papers (2026-04-11T13:59:05Z) - Overhang Tower: Resource-Rational Adaptation in Sequential Physical Planning [9.035799522683446]
We show that humans exhibit a dual transition under resource pressure, simultaneously shifting both physical prediction mechanism and planning strategy to match cognitive budget.<n>These findings reveal a hierarchical, resource-rational architecture that flexibly trades computational cost against predictive fidelity.
arXiv Detail & Related papers (2026-04-10T07:54:25Z) - Attention at Rest Stays at Rest: Breaking Visual Inertia for Cognitive Hallucination Mitigation [50.51650162235191]
We propose a training-free Inertia-aware Visual Excitation (IVE) method that breaks this inertial pattern by modeling cognitive inference as the dynamic responsiveness of visual attention.<n>IVE is effective across various base MLLMs and multiple hallucination benchmarks, particularly for cognitive hallucinations.
arXiv Detail & Related papers (2026-04-02T12:51:07Z) - Timing In stand-up Comedy: Text, Audio, Laughter, Kinesics (TIC-TALK): Pipeline and Database for the Multimodal Study of Comedic Timing [0.07777489763207261]
TIC-TALK is a multimodal resource with 5,400+ temporally aligned topic segments capturing language, gesture, and audience response.<n>The pipeline combines BERTopic for 60 s thematic segmentation with dense sentence embeddings, Whisper-AT for 0.8 s laughter detection, and YOLOv8s-pose for raw keypoint extraction at 1 fps.
arXiv Detail & Related papers (2026-03-23T10:46:15Z) - Stable Language Guidance for Vision-Language-Action Models [62.80963701282789]
Residual Semantic Steering is a probabilistic framework that disentangles physical affordance from semantic execution.<n> RSS achieves state-of-the-art robustness, maintaining performance even under adversarial linguistic perturbations.
arXiv Detail & Related papers (2026-01-07T16:16:10Z) - HUMORCHAIN: Theory-Guided Multi-Stage Reasoning for Interpretable Multimodal Humor Generation [13.49193658655368]
Humor, as both a creative human activity and a social binding mechanism, has long posed a major challenge for AI generation.<n>We propose HUMORCHAIN, a theory-guided multi-stage reasoning framework.<n>It integrates visual semantic parsing, humor- and psychology-based reasoning, and a fine-tuned discriminator for humor evaluation.
arXiv Detail & Related papers (2025-11-21T09:52:46Z) - Priors in Time: Missing Inductive Biases for Language Model Interpretability [58.07412640266836]
We show that Sparse Autoencoders impose priors that assume independence of concepts across time, implying stationarity.<n>We introduce a new interpretability objective -- Temporal Feature Analysis -- which possesses a temporal inductive bias to decompose representations at a given time into two parts.<n>Our results underscore the need for inductive biases that match the data in designing robust interpretability tools.
arXiv Detail & Related papers (2025-11-03T18:43:48Z) - Intention-Guided Cognitive Reasoning for Egocentric Long-Term Action Anticipation [52.6091162517921]
INSIGHT is a two-stage framework for egocentric action anticipation.<n>In the first stage, INSIGHT focuses on extracting semantically rich features from hand-object interaction regions.<n>In the second stage, it introduces a reinforcement learning-based module that simulates explicit cognitive reasoning.
arXiv Detail & Related papers (2025-08-03T12:52:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.