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，许多人认为这是人类与神经网络模型之间的核心差异之一。通过元学习，智能体可以被赋予对抽象化的归纳偏置，即在共享某些可学习并应用的抽象结构的任务分布上进行训练。然而，由于神经网络难以解释，我们很难判断智能体是否学会了底层抽象，还是仅仅掌握了该抽象特有的统计模式。在本工作中，我们比较了人类与智能体在元强化学习范式中的表现，该范式中的任务由抽象规则生成。我们定义了一种构建“任务元体”的新方法，这些元体在统计上与抽象任务高度匹配，但使用不同的底层生成过程，并评估其在抽象任务与元体任务上的表现。我们发现人类在抽象任务上的表现优于元体任务，而常见的神经网络架构在抽象任务上的表现通常劣于匹配的元体任务。本研究为刻画人类与机器学习之间的差异奠定了基础，可作为未来开发更具人类行为特征的机器的参考。