Simulating Emotions With an Integrated Computational Model of Appraisal and Reinforcement Learning
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
Toimittajat
Päivämäärä
2024Tekijänoikeudet
© 2024 Copyright held by the owner/author(s).
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.
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Julkaisija
ACMEmojulkaisun ISBN
979-8-4007-0330-0Konferenssi
ACM SIGCHI annual conference on human factors in computing systemsKuuluu julkaisuun
CHI '24 : Proceedings of the CHI Conference on Human Factors in Computing SystemsAsiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/213638278
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Lisätietoja rahoituksesta
This research has been supported by the Academy of Finland (grant 330347) and partially sponsored by the Hybrid Intelligence project (grant number 024.004.022).Lisenssi
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