# Decoding Humor-Induced Amusement via Facial Expression Analysis: Toward Emotion-Aware Applications
## 4 Discussion
### Temporal Dynamics of Humor Appreciation  
Compared to the funniness rating provided at the end of the video, analyzing the proportion of smiles allowed us to capture amusement at each time point during the experiment. Therefore, we were able to predict with significant accuracy how amusement (neutral, funny, and very funny videos) was experienced by participants across successive trials. The propensity to smile reflected amusement from the beginning of the video (after a few seconds) until the middle of the subsequent trial, indicating that each expression of amusement remained present for at least 20 s. At the end of this time, the amusement generated by the first video faded away and was replaced by the amusement felt by the second video. These results confirmed that smiles are stronger predictors of amusement levels toward the end of the video, and this discriminative power persists over time, even after the video ends and into the following trial. This is consistent with our observation that the level of amusement for funny videos is generally stronger toward the end, and that a lingering smile reflects the persistence of the emotional state. Furthermore, our behavioral data show that when a funny or very funny video follows another amusing one, it tends to be rated as even funnier than when preceded by a neutral video. This potentiation effect highlights how humor-related affect can spill over across trials, both enhancing subjective ratings and boosting facial expression-based decoding. Together, these findings reveal both within-trial dynamics and cross-trial emotional carry-over, underscoring the temporal richness of amusement responses for modeling and intervention. It is worth noting that while we were able to use facial expressions to predict amusement intensity, each participant only provided a single holistic rating per video. This means that there is no \"ground truth\" or trusted label for the temporal dynamics analyses. However, the consistency of our results with previous studies, such as [PERSON] et al. 's [PERSON] et al. (2016) identification of brain activity linked to humor during 5 s resting periods after humorous stimuli, and our own study's confirmation of a time window for predicting humorous amusement intensity using electrophysiology recording (EEG) ([PERSON] et al., 2016), provide support for the validity of our approach. Strengths and Limitations. In this study, we used a large subset of pre-validated videos to ensure that every participant will experience different intensities of amusement. We implemented state-of-the-art techniques to predict humorous amusement intensity and tracked how it evolved during video viewing with a high temporal resolution. However, better decoding scores might have been achieved if additional physiological and psychological data were included in the ML pipeline. Furthermore, while using Google API Video Intelligence to generate keywords for our video is an easy-to-implement tool, having keywords identified by another independent group of participants could increase their relevance and lead to a better understanding of how semantics impact humor. Finally, the findings of this paper are constrained by the limited sample size and specific demographic characteristics of the participants. Given that humor is influenced by culture, interests, and community-specific experiences, generalizing the system's performance to broader or cross-cultural samples may be limited. Our findings demonstrate model robustness within a relatively homogeneous sample of young adults, but extending this framework would require training models on diverse samples tailored to specific groups or cultural norms. Future work should explore domain adaptation techniques or transfer learning strategies to improve model performance across subgroups (e.g., older adults, culturally distinct communities).