Department of Psychology and Center for Brain Science, Harvard University.
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology.
Cogn Sci. 2021 Jan;45(1):e12928. doi: 10.1111/cogs.12928.
Flexibility is one of the hallmarks of human problem-solving. In everyday life, people adapt to changes in common tasks with little to no additional training. Much of the existing work on flexibility in human problem-solving has focused on how people adapt to tasks in new domains by drawing on solutions from previously learned domains. In real-world tasks, however, humans must generalize across a wide range of within-domain variation. In this work we argue that representational abstraction plays an important role in such within-domain generalization. We then explore the nature of this representational abstraction in realistically complex tasks like video games by demonstrating how the same model-based planning framework produces distinct generalization behaviors under different classes of task representation. Finally, we compare the behavior of agents with these task representations to humans in a series of novel grid-based video game tasks. Our results provide evidence for the claim that within-domain flexibility in humans derives from task representations composed of propositional rules written in terms of objects and relational categories.
灵活性是人类解决问题的标志之一。在日常生活中，人们几乎无需额外培训就能适应常见任务中的变化。在人类解决问题的灵活性方面，现有大量工作都集中在人们如何通过借鉴先前学习领域的解决方案来适应新领域的任务。然而，在现实任务中，人们必须跨广泛的领域内变化进行泛化。在这项工作中，我们认为表示抽象在这种领域内泛化中起着重要作用。然后，我们通过展示同一基于模型的规划框架如何在不同类别的任务表示下产生不同的泛化行为，探索了在像视频游戏这样现实复杂任务中这种表示抽象的性质。最后，我们在一系列新的基于网格的视频游戏任务中，将具有这些任务表示的代理与人类的行为进行了比较。我们的结果为以下主张提供了证据，即人类的领域内灵活性源自由对象和关系类别术语表述的命题规则组成的任务表示。
