Computational models reveal intuitive physics and statistical cues separately contribute to the visual perception of liquids.

We are intimately familiar with liquids in our visual experience, yet the computational basis of liquid perception remains underexplored. This is an important knowledge gap because liquids, with their mutable shapes and complex intrinsic dynamics, differ remarkably from the commonly studied categories in computational vision, such as rigid objects or non-rigid solids. To understand the computational basis of liquid perception, we implemented different models of this ability and tested them in a new behavioral study. The models realize two distinct theoretical possibilities for the visual perception of liquid viscosity. The first possibility, and the focus of most existing work, explains the representation of liquid viscosity as a consequence of high-level image and motion statistics discriminative of the gradations of this physical property. A second, much different possibility is that the perceptual representations of liquids functionally map the physical processes of how viscosity and external forces (e.g., gravity, rigid surfaces) shape the way liquids move. We task these models and humans in a new behavioral task: making similarity judgments of liquid viscosity across pairs of animations depicting qualitatively different scenarios - e.g., a metal ball falling into a liquid container vs. liquid pouring over a non-flat surface. We find that a new model, Ripple, which builds and manipulates physics-based representations of liquid viscosity from sensory inputs, explains substantial variance in human judgments beyond powerful, previously behaviorally validated, statistical representations of viscosity. Moreover, statistical representations of viscosity across vastly different model architectures - a task-specific DNN and a general video foundation model - converge with one another, while remaining equally differentiated from Ripple. These results suggest that liquid perception extends beyond image statistics to also involve simulation-based intuitive physics.