We build a computational model of how humans actively infer hidden rules by doing experiments. The basic principles behind the model is that, even if the rule is deterministic, the learner considers a broader space of fuzzy probabilistic rules, which it represents in natural language, and updates its hypotheses online after each experiment according to approximately Bayesian principles. In the same framework we also model experiment design according to information-theoretic criteria. We find that the combination of these three principles -- explicit hypotheses, probabilistic rules, and online updates -- can explain human performance on a Zendo-style task, and that removing any of these components leaves the model unable to account for the data.

翻译：我们构建了一个计算模型，用以模拟人类如何通过实验主动推断隐藏规则。该模型的基本原理是：即使规则是确定性的，学习者仍会考虑一个更广泛的模糊概率规则空间，并以自然语言表征这些规则，同时根据近似贝叶斯原理在每次实验后在线更新其假设。在同一框架下，我们还根据信息论标准对实验设计进行了建模。研究发现，这三个原则——显式假设、概率规则与在线更新——的组合能够解释人类在Zendo类任务中的表现，而移除其中任一成分都会导致模型无法解释相关数据。