Technology Category
Cognitive Modeling & Cognitive Systems: Affective ComputingKnowledge Representation and Reasoning: Action, Change, and CausalityHumans and AI: Human-Aware Planning and Behavior Prediction
Redefining Affordance via Computational Rationality
🤖 AI Summary
This study addresses the core problem of how affordance perception supports real-time action decision-making under limited sensory information. Methodologically, it introduces the first computationally rational affordance theory framework, modeling affordances as a dynamic decision process: constructing internal world representations, identifying critical environmental features, inferring hypothesized motor trajectories, and performing a dual-factor trade-off between execution confidence and behavioral utility. Its key contribution lies in transcending traditional static perception paradigms—enabling affordances to evolve from passive recognition to active learning, feedback-driven regulation, and cross-context generalization. The framework unifies explanations of affordance phenomena across physical, digital, and social interactions; enables verifiable multimodal modeling; and informs adaptive human–machine system design. It thus provides computationally grounded design principles and an evolutionary foundation for intelligent human–machine collaboration. (149 words)
Technology Category
Application Category
📝 Abstract
Affordances, a foundational concept in human-computer interaction and design, have traditionally been explained by direct-perception theories, which assume that individuals perceive action possibilities directly from the environment. However, these theories fall short of explaining how affordances are perceived, learned, refined, or misperceived, and how users choose between multiple affordances in dynamic contexts. This paper introduces a novel affordance theory grounded in Computational Rationality, positing that humans construct internal representations of the world based on bounded sensory inputs. Within these internal models, affordances are inferred through two core mechanisms: feature recognition and hypothetical motion trajectories. Our theory redefines affordance perception as a decision-making process, driven by two components: confidence (the perceived likelihood of successfully executing an action) and predicted utility (the expected value of the outcome). By balancing these factors, individuals make informed decisions about which actions to take. Our theory frames affordances perception as dynamic, continuously learned, and refined through reinforcement and feedback. We validate the theory via thought experiments and demonstrate its applicability across diverse types of affordances (e.g., physical, digital, social). Beyond clarifying and generalizing the understanding of affordances across contexts, our theory serves as a foundation for improving design communication and guiding the development of more adaptive and intuitive systems that evolve with user capabilities.
Problem
Research questions and friction points this paper is trying to address.
Perception
Decision Making
Intelligent Systems Design
Innovation
Methods, ideas, or system contributions that make the work stand out.
Affordance Perception
Human-Centered Design
Computational Cognition Integration
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