Title: Modeling human intuitions about liquid flow with particle-based simulation,
Abstract: 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.