Modeling Induced Pleasure through Cognitive Appraisal Prediction via Multimodal Fusion

• URL: http://arxiv.org/abs/2604.23753v1
• Date: Sun, 26 Apr 2026 15:07:41 GMT
• Title: Modeling Induced Pleasure through Cognitive Appraisal Prediction via Multimodal Fusion
• Abstract summary: This study introduces a novel computational model to infer video-induced pleasure via cognitive appraisal variables.<n>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.<n> 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.
• Score: 0.6397750672942423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Evaluating Cognitive-Behavioral Fixation via Multimodal User Viewing Patterns on Social Media [52.313084466769375]
  We propose a novel framework for assessing cognitive-behavioral fixation by analyzing users' multimodal social media engagement patterns.<n> Experiments on existing benchmarks and a newly curated multimodal dataset demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2025-09-05T05:50:00Z)
• Dynamic Programming Techniques for Enhancing Cognitive Representation in Knowledge Tracing [125.75923987618977]
  We propose the Cognitive Representation Dynamic Programming based Knowledge Tracing (CRDP-KT) model.<n>It is a dynamic programming algorithm to optimize cognitive representations based on the difficulty of the questions and the performance intervals between them.<n>It provides more accurate and systematic input features for subsequent model training, thereby minimizing distortion in the simulation of cognitive states.
  arXiv  Detail & Related papers  (2025-06-03T14:44:48Z)
• Bridging Cognition and Emotion: Empathy-Driven Multimodal Misinformation Detection [56.644686934050576]
  Social media has become a major conduit for information dissemination, yet it also facilitates the rapid spread of misinformation.<n>Traditional misinformation detection methods primarily focus on surface-level features, overlooking the crucial roles of human empathy in the propagation process.<n>We propose the Dual-Aspect Empathy Framework (DAE), which integrates cognitive and emotional empathy to analyze misinformation from both the creator and reader perspectives.
  arXiv  Detail & Related papers  (2025-04-24T07:48:26Z)
• Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training [0.850206009406913]
  MViTac is a novel methodology that leverages contrastive learning to integrate vision and touch sensations in a self-supervised fashion. By availing both sensory inputs, MViTac leverages intra and inter-modality losses for learning representations, resulting in enhanced material property classification and more adept grasping prediction.
  arXiv  Detail & Related papers  (2024-01-22T15:11:57Z)
• Probabilistic emotion and sentiment modelling of patient-reported experiences [0.04096453902709291]
  This study introduces a novel methodology for modelling patient emotions from online patient experience narratives. We employ metadata network topic modelling to analyse patient-reported experiences from Care Opinion. We develop a probabilistic, context-specific emotion recommender system capable of predicting both multilabel emotions and binary sentiments.
  arXiv  Detail & Related papers  (2024-01-09T05:39:20Z)
• Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach [61.04606493712002]
  Susceptibility to misinformation describes the degree of belief in unverifiable claims that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs. We propose a computational approach to model users' latent susceptibility levels.
  arXiv  Detail & Related papers  (2023-11-16T07:22:56Z)
• Interpretable Imitation Learning with Dynamic Causal Relations [65.18456572421702]
  We propose to expose captured knowledge in the form of a directed acyclic causal graph. We also design this causal discovery process to be state-dependent, enabling it to model the dynamics in latent causal graphs. The proposed framework is composed of three parts: a dynamic causal discovery module, a causality encoding module, and a prediction module, and is trained in an end-to-end manner.
  arXiv  Detail & Related papers  (2023-09-30T20:59:42Z)
• Interpretable Fake News Detection with Topic and Deep Variational Models [2.15242029196761]
  We focus on fake news detection using interpretable features and methods. We have developed a deep probabilistic model that integrates a dense representation of textual news. Our model achieves comparable performance to state-of-the-art competing models.
  arXiv  Detail & Related papers  (2022-09-04T05:31:00Z)
• The drivers of online polarization: fitting models to data [0.0]
  echo chamber effect and opinion polarization may be driven by several factors including human biases in information consumption and personalized recommendations produced by feed algorithms. Until now, studies have mainly used opinion dynamic models to explore the mechanisms behind the emergence of polarization and echo chambers. We provide a method to numerically compare the opinion distributions obtained from simulations with those measured on social media.
  arXiv  Detail & Related papers  (2022-05-31T17:00:41Z)
• Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization [73.89239820192894]
  We argue that automated counterfactual generation should regard several aspects of the produced adversarial instances. We present a novel framework for the generation of counterfactual examples.
  arXiv  Detail & Related papers  (2022-05-20T15:02:53Z)

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.