🤖 AI Summary
This work proposes the Bayesian Expected Uncertainty Reduction (B-EUR) model, which formally defines the “epistemic value” of candidate actions in design exploration as the expected reduction in uncertainty about action–outcome relationships. Through a graph-structure guessing task integrating Bayesian inference, computational modeling, behavioral experiments, and simulation analyses, the study reveals that epistemic value follows an inverted-U relationship with environmental generalizability and increases monotonically with outcome discriminability. Moreover, human subjective willingness to explore and experienced pleasure also exhibit an inverted-U dependence on generalizability and jointly shape choice behavior. These findings provide a theoretical foundation for prototype set construction and feedback design in exploratory tasks.
📝 Abstract
This paper proposes the Bayesian Expected Uncertainty Reduction (B-EUR) model, which formalizes the value of trying a candidate design action as its expected reduction of epistemic uncertainty about action--outcome relations. The model addresses one part of the Uncertainty Driven Action (UDA) model's open question concerning how changes in uncertainty perception determine action selection. We examine two environmental properties: generalizability, or how far knowledge from one trial extends to neighboring candidates, and outcome discriminability, or how clearly differences among outcomes can be distinguished.
We tested the model through simulations and human experiments using a graph-shape guessing task that isolates learning about action--outcome relations under a limited trial budget. Epistemic value followed an inverted-U-shaped relationship with generalizability and increased with outcome discriminability in the simulations. In the human experiments, the subjective value of trying and enjoyment followed inverted-U-shaped relationships with generalizability, while choice behavior reflected both properties.
The B-EUR model provides a computational account of candidate-action evaluation within uncertainty-driven design activity and offers implications for constructing prototype sets, framing design problems, and organizing feedback to support informative exploration.