Relations between task elements often follow hidden underlying structural forms such as periodicities or hierarchies, whose inferences fosters performance. However, transferring structural knowledge to novel environments requires flexible representations that are generalizable over particularities of the current environment, such as its stimuli and size. We suggest that humans represent structural forms as abstract basis sets and that in novel tasks, the structural form is inferred and the relevant basis set is transferred. Using a computational model, we show that such representation allows inference of the underlying structural form, important task states, effective behavioural policies and the existence of unobserved state-trajectories. In two experiments, participants learned three abstract graphs during two successive days. We tested how structural knowledge acquired on Day-1 affected Day-2 performance. In line with our model, participants who had a correct structural prior were able to infer the existence of unobserved state-trajectories and appropriate behavioural policies.

任务元素之间的关系通常遵循隐藏的潜在结构形式，例如周期性或层次结构，其推断可促进绩效。然而，将结构知识转移到新环境需要灵活的表示，这些表示可以泛化当前环境的特殊性，例如其刺激和大小。我们建议人类将结构形式表示为抽象的基组，并且在新任务中，推断结构形式并转移相关的基组。使用计算模型，我们表明这种表示允许推断潜在的结构形式、重要的任务状态、有效的行为策略和未观察到的状态轨迹的存在。在两个实验中，参与者在连续两天内学习了三个抽象图形。我们测试了第 1 天获得的结构知识如何影响第 2 天的表现。与我们的模型一致，具有正确结构先验的参与者能够推断出未观察到的状态轨迹和适当的行为策略的存在。