Many animals, and an increasing number of artificial agents, display sophisticated capabilities to perceive and manipulate objects. But human beings remain distinctive in their capacity for flexible, creative tool use—using objects in new ways to act on the world, achieve a goal, or solve a problem. To study this type of general physical problem solving, we introduce the Virtual Tools game. In this game, people solve a large range of challenging physical puzzles in just a handful of attempts. We propose that the flexibility of human physical problem solving rests on an ability to imagine the effects of hypothesized actions, while the efficiency of human search arises from rich action priors which are updated via observations of the world. We instantiate these components in the “sample, simulate, update” (SSUP) model and show that it captures human performance across 30 levels of the Virtual Tools game. More broadly, this model provides a mechanism for explaining how people condense general physical knowledge into actionable, task-specific plans to achieve flexible and efficient physical problem solving.

许多动物以及越来越多的人工制剂都具有感知和操纵物体的先进能力。但是，人类在灵活，创造性地使用工具方面仍然保持着与众不同的能力，即以新的方式使用对象来作用于世界，实现目标或解决问题。为了研究这种一般的物理问题解决方法，我们介绍了虚拟工具游戏。在此游戏中，人们只需几次尝试即可解决各种挑战性的物理难题。我们提出，人类身体问题解决的灵活性取决于想象假设行动的效果的能力，而人类搜索的效率则来自丰富的行动先验，而先验行动是通过对世界的观察而更新的。我们在“采样，模拟，更新”（SSUP）模型，并表明它在虚拟工具游戏的30个级别中都捕获了人类的表现。更广泛地说，该模型提供了一种机制，用于解释人们如何将一般的物理知识浓缩为可操作的，针对特定任务的计划，以实现灵活有效的物理问题解决方案。