The ability to acquire abstract knowledge is a hallmark of human intelligence and is believed by many to be one of the core differences between humans and neural network models. Agents can be endowed with an inductive bias towards abstraction through meta-learning, where they are trained on a distribution of tasks that share some abstract structure that can be learned and applied. However, because neural networks are hard to interpret, it can be difficult to tell whether agents have learned the underlying abstraction, or alternatively statistical patterns that are characteristic of that abstraction. In this work, we compare the performance of humans and agents in a meta-reinforcement learning paradigm in which tasks are generated from abstract rules. We define a novel methodology for building "task metamers" that closely match the statistics of the abstract tasks but use a different underlying generative process, and evaluate performance on both abstract and metamer tasks. We find that humans perform better at abstract tasks than metamer tasks whereas common neural network architectures typically perform worse on the abstract tasks than the matched metamers. This work provides a foundation for characterizing differences between humans and machine learning that can be used in future work towards developing machines with more human-like behavior.

翻译：获取抽象知识的能力是人类智能的标志，被许多人视为人类与神经网络模型之间的核心差异之一。通过元学习，智能体可以被赋予偏向抽象的归纳偏差——即在共享某种抽象结构的任务分布上进行训练，从而学习并应用该结构。然而，由于神经网络难以解释，我们很难判断智能体是学习了底层的抽象规则，还是仅仅掌握了该抽象特征对应的统计模式。在本研究中，我们比较了人类与智能体在元强化学习范式下的表现，其中的任务由抽象规则生成。我们提出了一种构建“任务元异构体”的新方法，这些元异构体在统计上紧密匹配抽象任务，但使用不同的底层生成过程，并评估了智能体在抽象任务与元异构任务上的表现。研究发现，人类在抽象任务上的表现优于元异构任务，而常见的神经网络架构在抽象任务上的表现通常不如匹配的元异构体。这项工作为描述人类与机器学习之间的差异奠定了基础，可用于未来开发更具人类行为特征的机器。