PDF(Pubmed)
Abstract：
The fact that objects without proper support will fall to the ground is not only a natural phenomenon, but also common sense in mind. Previous studies suggest that humans may infer objects\' stability through a world model that performs mental simulations with a priori knowledge of gravity acting upon the objects. Here we measured participants\' sensitivity to gravity to investigate how the world model works. We found that the world model on gravity was not a faithful replica of the physical laws, but instead encoded gravity\'s vertical direction as a Gaussian distribution. The world model with this stochastic feature fit nicely with participants\' subjective sense of objects\' stability and explained the illusion that taller objects are perceived as more likely to fall. Furthermore, a computational model with reinforcement learning revealed that the stochastic characteristic likely originated from experience-dependent comparisons between predictions formed by internal simulations and the realities observed in the external world, which illustrated the ecological advantage of stochastic representation in balancing accuracy and speed for efficient stability inference. The stochastic world model on gravity provides an example of how a priori knowledge of the physical world is implemented in mind that helps humans operate flexibly in open-ended environments.
摘要：
没有适当支撑的物体会掉到地上，这不仅是一种自然现象，还有常识。先前的研究表明，人类可以通过一个世界模型来推断物体的稳定性，该模型通过对重力作用在物体上的先验知识进行心理模拟。在这里，我们测量了参与者对重力的敏感度，以研究世界模型是如何工作的。我们发现关于重力的世界模型并不是物理定律的忠实复制品，而是将重力的垂直方向编码为高斯分布。具有这种随机特征的世界模型很好地符合参与者的“主观物体感”的稳定性，并解释了认为较高物体更有可能跌倒的错觉。此外，具有强化学习的计算模型表明，随机特征可能源于内部模拟形成的预测与外部世界观察到的现实之间的经验依赖比较，说明了随机表示在平衡准确性和速度以进行有效稳定性推断方面的生态优势。关于重力的随机世界模型提供了一个示例，说明如何实现物理世界的先验知识，以帮助人类在开放式环境中灵活操作。
