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:

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

Preliminary JUFO rating: 3