A4 Article in conference proceedings
Simulating Emotions With an Integrated Computational Model of Appraisal and Reinforcement Learning (2024)
Zhang, J. E., Hilpert, B., Broekens, J., & Jokinen, J. P.P. (2024). Simulating Emotions With an Integrated Computational Model of Appraisal and Reinforcement Learning. In F. F. Mueller, P. Kyburz, J. R. Williamson, C. Sas, M. L. Wilson, P. T. Dugas, & I. Shklovski (Eds.), CHI '24 : Proceedings of the CHI Conference on Human Factors in Computing Systems (Article 703). ACM. https://doi.org/10.1145/3613904.3641908
JYU authors or editors
Publication details
All authors or editors: Zhang, Jiayi Eurus; Hilpert, Bernhard; Broekens, Joost; Jokinen, Jussi P. P.
Parent publication: CHI '24 : Proceedings of the CHI Conference on Human Factors in Computing Systems
Parent publication editors: Mueller, Florian Floyd; Kyburz, Penny; Williamson, Julie R.; Sas, Corina; Wilson, Max L.; Dugas, Phoebe Toups; Shklovski, Irina
Conference:
- ACM SIGCHI annual conference on human factors in computing systems
Place and date of conference: Honolulu, USA, 11.-16.5.2024
eISBN: 979-8-4007-0330-0
Publication year: 2024
Publication date: 11/05/2024
Article number: 703
Publisher: ACM
Publication country: United States
Publication language: English
DOI: https://doi.org/10.1145/3613904.3641908
Publication open access: Openly available
Publication channel open access: Partially open access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/94912
Abstract
Predicting users’ emotional states during interaction is a long-standing goal of affective computing. However, traditional methods based on sensory data alone fall short due to the interplay between users’ latent cognitive states and emotional responses. To address this, we introduce a computational cognitive model that simulates emotion as a continuous process, rather than a static state, during interactive episodes. This model integrates cognitive-emotional appraisal mechanisms with computational rationality, utilizing value predictions from reinforcement learning. Experiments with human participants demonstrate the model’s ability to predict and explain the emergence of emotions such as happiness, boredom, and irritation during interactions. Our approach opens the possibility of designing interactive systems that adapt to users’ emotional states, thereby improving user experience and engagement. This work also deepens our understanding of the potential of modeling the relationship between reward processing, reinforcement learning, goal-directed behavior, and appraisal.
Keywords: simulation; modelling (representation); cognitive processes; computational science; machine learning; reinforcement learning; emotions; rewarding; appraisal
Contributing organizations
Ministry reporting: Yes
VIRTA submission year: 2024
Preliminary JUFO rating: 3