AI 中文总结
该研究针对认知参数推断的环境选择问题，将认知规划实验建模为贝叶斯实验设计问题，提出摊销贝叶斯实验设计框架，经Mouselab-MDP实验验证其高效性，揭示了环境选择的权衡关系。
AI 中文摘要
计算认知建模旨在从观测行为中推断潜在的认知机制，贝叶斯逆规划为此提供了原则性框架，但其成功关键取决于实验环境。现有方法通常将环境视为固定不变的，未解决哪些认知实验对认知参数推断最具信息量的问题。我们将认知规划实验的设计建模为贝叶斯实验设计（BED）问题，把实验环境作为设计变量。我们建立了精确蒙特卡洛贝叶斯实验设计基准，并引入摊销贝叶斯实验设计框架以实现高效的后验推断与设计评估。在Mouselab-MDP过程追踪范式上的实验表明，摊销贝叶斯实验设计与精确蒙特卡洛贝叶斯实验设计的环境排名高度吻合，同时大幅降低了计算成本。我们进一步发现，不存在对所有认知推断目标均最优的单一环境，揭示了预期信息增益、后验可恢复性与信息效率之间的权衡。这些结果为设计用于贝叶斯参数推断的有效认知实验提供了原则性框架。
英文摘要
Computational cognitive modeling seeks to infer latent cognitive mechanisms underlying observed behavior. Bayesian inverse planning provides a principled framework for such inference, but its success depends critically on the experimental environment. Existing approaches typically treat environments as fixed, leaving open the question of which cognitive experiments are most informative for cognition parameter inference. We formulate the design of cognitive planning experiments as a Bayesian Experimental Design (BED) problem, treating the experimental environment as the design variable. We establish an exact Monte Carlo BED benchmark and introduce an amortized Bayesian experimental design framework for efficient posterior inference and design evaluation. Experiments on the Mouselab-MDP process-tracing paradigm show that amortized BED closely matches the environment rankings of exact Monte Carlo BED while substantially reducing computational cost. We further show that no single environment is uniformly optimal across cognitive inference objectives, revealing trade-offs between expected information gain, posterior recoverability, and information efficiency. These results provide a principled framework for designing informative cognitive experiments for Bayesian parameter inference.
Comments14 pages, 16 tables, 3 figures
