Shape features underlying the perception of liquids

Visually identifying and estimating properties of materials is critical for many tasks, from reaching for a steaming kettle to walking on a wet marble staircase. From a computational point of view, liquids represent a particularly interesting class of materials because of their highly mutable shapes and physical complexity. The ways liquids move and change shape is affected both by external forces (e.g. object interactions, gravity) and intrinsic properties (e.g. viscosity, density). Estimating viscosity therefore requires disentangling the relative contributions of external and internal factors. Previous studies have shown that despite the wide spectrum of possible liquid shapes, observers are surprisingly good at estimating viscosity across variations in the external conditions. In this study we test the extent to which perceived viscosity can be predicted using mid-level shape and motion features (e.g. ‘spread’, ‘piling up’, ‘angularity’).