Inferring the stiffness of unfamiliar objects from optical, shape, and motion cues

Visually inferring the stiffness of objects is important for many tasks but is challenging because, unlike optical properties (e.g., gloss), mechanical properties do not directly affect image values. Stiffness must be inferred either (a) by recognizing materials and recalling their properties (associative approach) or (b) from shape and motion cues when the material is deformed (estimation approach). Here, we investigated interactions between these two inference types. Participants viewed renderings of unfamiliar shapes with 28 materials (e.g., nickel, wax, cork). In Experiment 1, they viewed nondeformed, static versions of the objects and rated 11 material attributes (e.g., soft, fragile, heavy). The results confirm that the optical materials elicited a wide range of apparent properties. In Experiment 2, using a blue plastic material with intermediate apparent softness, the objects were subjected to physical simulations of 12 shape-transforming processes (e.g., twisting, crushing, stretching). Participants rated softness and extent of deformation. Both correlated with the physical magnitude of deformation. Experiment 3 combined variations in optical cues with shape cues. We find that optical cues completely dominate. Experiment 4 included the entire motion sequence of the deformation, yielding significant contributions of optical as well as motion cues. Our findings suggest participants integrate shape, motion, and optical cues to infer stiffness, with optical cues playing a major role for our range of stimuli.