Modeling human intuitions about liquid flow with particle-based simulation
This work addresses the problem of understanding human physical scene perception for fluid dynamics, extending prior research from rigid objects to liquids, but it is incremental as it builds on existing simulation frameworks.
The paper tackled the problem of modeling human intuitions about liquid flow by proposing a particle-based simulation model analogous to a 'game engine in the head', and found that it accurately captured people's predictions about liquid dynamics among obstacles, outperforming heuristic and neural network alternatives.
Humans can easily describe, imagine, and, crucially, predict a wide variety of behaviors of liquids--splashing, squirting, gushing, sloshing, soaking, dripping, draining, trickling, pooling, and pouring--despite tremendous variability in their material and dynamical properties. Here we propose and test a computational model of how people perceive and predict these liquid dynamics, based on coarse approximate simulations of fluids as collections of interacting particles. Our model is analogous to a "game engine in the head", drawing on techniques for interactive simulations (as in video games) that optimize for efficiency and natural appearance rather than physical accuracy. In two behavioral experiments, we found that the model accurately captured people's predictions about how liquids flow among complex solid obstacles, and was significantly better than two alternatives based on simple heuristics and deep neural networks. Our model was also able to explain how people's predictions varied as a function of the liquids' properties (e.g., viscosity and stickiness). Together, the model and empirical results extend the recent proposal that human physical scene understanding for the dynamics of rigid, solid objects can be supported by approximate probabilistic simulation, to the more complex and unexplored domain of fluid dynamics.