A stochastic world model on gravity for stability inference

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 gravitys 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. Significance StatementHumans possess an exceptional capacity for inferring the stability of objects, a skill that has been crucial to the survival of our predecessors and continues to facilitate our daily interactions with the natural world. The present study elucidates that our representation of gravitational direction adheres to a Gaussian distribution, with the vertical orientation as the maximum likelihood. This stochastic representation is likely to have originated from our interactions with the physical world, conferring an ecological advantage of balancing accuracy with speed. Therefore, the world model on gravity in the mind is a distorted replica of the natural world, enabling adaptive functionality in open-ended environments and thus shedding light on developing machines imbued with embodied intelligence.