Teleology-Driven Affective Computing: A Causal Framework for Sustained Well-Being

2025-02-24

AI Summary

Contemporary affective computing predominantly focuses on short-term pattern recognition, lacking a theoretically grounded, causally driven framework for supporting long-term human well-being. This paper introduces the first teleological, causally grounded affective computing framework, unifying basic emotion theory, cognitive appraisal models, and constructivist perspectives by modeling individual and collective well-being as dynamically aligned, multi-scale goal systems. Our contributions are threefold: (1) constructing an individual-level “dataverse” of affective events via causal modeling and ecological momentary assessment; (2) designing a meta-reinforcement learning mechanism for adaptive trade-offs among hierarchical well-being objectives; and (3) integrating immersive VR with cross-theoretical representational learning to advance affective analysis from statistical association to causal inference. Simulation studies and preliminary empirical validation demonstrate significant improvements in long-horizon well-being prediction accuracy and personalization efficacy of behavioral interventions.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Knowledge Representation and Reasoning: Action, Change, and Causality
• Machine Learning: Causal Learning

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• long-term human well-being
• teleology-driven affective computing
• causal modeling for AI interventions

Innovation

• Teleology-driven affective computing framework
• Causal modeling for tailored interventions
• Meta-reinforcement learning in simulated environments