发表机构
The University of Tokyo; Arts et Métiers ParisTech（东京大学; 巴黎艺术与工艺学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出B-EUR模型，将设计动作价值形式化为认知不确定性预期减少量，经模拟与实验验证其能解释不确定性驱动的设计活动中候选动作评估，为原型集构建等提供启示。
AI 中文摘要
本文提出了贝叶斯预期不确定性减少（B-EUR）模型，该模型将尝试候选设计动作的价值形式化为其对动作-结果关系的认知不确定性的预期减少量。该模型解决了不确定性驱动动作（UDA）模型的一个开放性问题，即不确定性感知的变化如何决定动作选择。我们考察了两种环境属性：可推广性（即一次试验的知识延伸至相邻候选的程度）和结果判别力（即结果间差异可被区分的清晰程度）。我们通过模拟和人类实验测试了该模型，实验采用图形形状猜测任务，该任务在有限试验预算下隔离了对动作-结果关系的学习。模拟中，认知价值与可推广性呈倒U型关系，且随结果判别力的提高而增加；人类实验中，尝试的主观价值和享受感与可推广性呈倒U型关系，而选择行为则同时反映了这两种属性。B-EUR模型为不确定性驱动的设计活动中的候选动作评估提供了计算解释，并为构建原型集、构建设计问题以及组织反馈以支持信息丰富的探索提供了启示。
英文摘要
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.
