Seeing liquids from static snapshots

- Observers can distinguish fluids from static shape cues alone.
- A parameter-free model predicts the visual perception of viscosity.
- The model uses simple shape statistics of the 2D images presented to our observers.
- Our model predicts the observers' viscosity perception almost perfectly.
- The brain might distinguish liquids using such statistical generative models.

Perceiving material properties can be crucial for many tasks-such as determining food edibility, or avoiding getting splashed-yet the visual perception of materials remains poorly understood. Most previous research has focussed on optical characteristics (e.g., gloss, translucency). Here, however, we show that shape also provides powerful visual cues to material properties. When liquids pour, splash or ooze, they organize themselves into characteristic shapes, which are highly diagnostic of the material's properties. Subjects viewed snapshots of simulated liquids of different viscosities, and rated their similarity. Using maximum likelihood difference scaling (Maloney & Yang, 2003), we reconstructed perceptual scales for perceived viscosity as a function of the physical viscosity of the simulated fluids. The resulting psychometric function revealed a distinct sigmoidal shape, distinguishing runny liquids that flow easily from viscous gels that clump up into piles. A parameter-free model based on 20 simple shape statistics predicted the subjects' data surprisingly well. This suggests that when subjects are asked to compare the viscosity of static snapshots of liquids that differ only in terms of viscosity, they rely primarily on relatively simple measures of shape similarity.