Teleology-Driven Affective Computing: A Causal Framework for Sustained Well-Being
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| author | Yin, Bin Liu, Chong-Yi Fu, Liya Zhang, Jinkun |
| author_facet | Yin, Bin Liu, Chong-Yi Fu, Liya Zhang, Jinkun |
| contents | Affective computing has made significant strides in emotion recognition and generation, yet current approaches mainly focus on short-term pattern recognition and lack a comprehensive framework to guide affective agents toward long-term human well-being. To address this, we propose a teleology-driven affective computing framework that unifies major emotion theories (basic emotion, appraisal, and constructivist approaches) under the premise that affect is an adaptive, goal-directed process that facilitates survival and development. Our framework emphasizes aligning agent responses with both personal/individual and group/collective well-being over extended timescales. We advocate for creating a "dataverse" of personal affective events, capturing the interplay between beliefs, goals, actions, and outcomes through real-world experience sampling and immersive virtual reality. By leveraging causal modeling, this "dataverse" enables AI systems to infer individuals' unique affective concerns and provide tailored interventions for sustained well-being. Additionally, we introduce a meta-reinforcement learning paradigm to train agents in simulated environments, allowing them to adapt to evolving affective concerns and balance hierarchical goals - from immediate emotional needs to long-term self-actualization. This framework shifts the focus from statistical correlations to causal reasoning, enhancing agents' ability to predict and respond proactively to emotional challenges, and offers a foundation for developing personalized, ethically aligned affective systems that promote meaningful human-AI interactions and societal well-being. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17172 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Teleology-Driven Affective Computing: A Causal Framework for Sustained Well-Being Yin, Bin Liu, Chong-Yi Fu, Liya Zhang, Jinkun Human-Computer Interaction Artificial Intelligence Computers and Society Neurons and Cognition H.1.2, J.4 H.1.2; J.4 Affective computing has made significant strides in emotion recognition and generation, yet current approaches mainly focus on short-term pattern recognition and lack a comprehensive framework to guide affective agents toward long-term human well-being. To address this, we propose a teleology-driven affective computing framework that unifies major emotion theories (basic emotion, appraisal, and constructivist approaches) under the premise that affect is an adaptive, goal-directed process that facilitates survival and development. Our framework emphasizes aligning agent responses with both personal/individual and group/collective well-being over extended timescales. We advocate for creating a "dataverse" of personal affective events, capturing the interplay between beliefs, goals, actions, and outcomes through real-world experience sampling and immersive virtual reality. By leveraging causal modeling, this "dataverse" enables AI systems to infer individuals' unique affective concerns and provide tailored interventions for sustained well-being. Additionally, we introduce a meta-reinforcement learning paradigm to train agents in simulated environments, allowing them to adapt to evolving affective concerns and balance hierarchical goals - from immediate emotional needs to long-term self-actualization. This framework shifts the focus from statistical correlations to causal reasoning, enhancing agents' ability to predict and respond proactively to emotional challenges, and offers a foundation for developing personalized, ethically aligned affective systems that promote meaningful human-AI interactions and societal well-being. |
| title | Teleology-Driven Affective Computing: A Causal Framework for Sustained Well-Being |
| topic | Human-Computer Interaction Artificial Intelligence Computers and Society Neurons and Cognition H.1.2, J.4 H.1.2; J.4 |
| url | https://arxiv.org/abs/2502.17172 |