Modeling Induced Pleasure through Cognitive Appraisal Prediction via Multimodal Fusion

Computer Science > Artificial Intelligence arXiv:2604.23753 (cs) [Submitted on 26 Apr 2026] Title:Modeling Induced Pleasure through Cognitive Appraisal Prediction via Multimodal Fusion Authors:Nastaran Dab, Raziyeh Zall, Mohammadreza Kangavari View a PDF of the paper titled Modeling Induced Pleasure through Cognitive Appraisal Prediction via Multimodal Fusion, by Nastaran Dab and 2 other authors View PDF HTML (experimental) Abstract:Multimodal affective computing analyzes user-generated social media content to predict emotional states. However, a critical gap remains in understanding how visual content shapes cognitive interpretations and elicits specific affective experiences such as pleasure. This study introduces a novel computational model to infer video-induced pleasure via cognitive appraisal variables. The proposed model addresses four challenges: (1) noisy and inconsistent human labels, (2) the semantic gap between "positive emotions" and "pleasure," (3) the scarcity of pleasure-specific datasets, and (4) the limited interpretability of existing black-box fusion methods. Our approach integrates data-driven and cognitive theory-driven methods, using cognitive appraisal theory and a fuzzy model within an innovative framework. The model employs transformer-based architectures and attention mechanisms for fine-grained multimodal feature extraction and interpretable fusion to capture both inter- and intra-modal dynamics associated with pleasure. This enables the prediction of underlying appraisal variables, thereby bridging the semantic gap and enhancing model explainability beyond conventional statistical associations. Experimental results validate the efficacy of the proposed method in detecting video-induced pleasure, achieving a peak accuracy of 0.6624 in predicting pleasure levels. These findings highlight promising implications for affective content recommendation, intelligent media creation, and advancing our understanding of how digital media influences human emotions. Subjects: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG) Cite as: arXiv:2604.23753 [cs.AI] (or arXiv:2604.23753v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2604.23753 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Nastaran Dab [view email] [v1] Sun, 26 Apr 2026 15:07:41 UTC (593 KB) Full-text links: Access Paper: View a PDF of the paper titled Modeling Induced Pleasure through Cognitive Appraisal Prediction via Multimodal Fusion, by Nastaran Dab and 2 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-04 Change to browse by: cs cs.HC cs.LG References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?)…

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