Seeing Soft Materials Draped Over Objects: A Case Study of Intuitive Physics in Perception, Attention, and Memory.

We typically think of intuitive physics in terms of high-level cognition, but might aspects of physics also be extracted during lower-level visual processing? Might we not only think about physics, but also see it? We explored this using multiple tasks in online adult samples with objects covered by soft materials-as when you see a chair with a blanket draped over it-where you must account for the physical interactions between cloth, gravity, and object. In multiple change-detection experiments (n = 200), observers from an online testing marketplace were better at detecting image changes involving underlying object structure versus those involving only the superficial folds of cloths-even when the latter were more extreme along several dimensions. And in probe-comparison experiments (n = 100), performance was worse when both probes (vs. only one) appeared on image regions reflective of underlying object structure (equating visual properties). This work collectively shows how vision uses intuitive physics to recover the deeper underlying structure of scenes.

Perception of 3D shape integrates intuitive physics and analysis-by-synthesis.

Many surface cues support three-dimensional shape perception, but humans can sometimes still see shape when these features are missing-such as when an object is covered with a draped cloth. Here we propose a framework for three-dimensional shape perception that explains perception in both typical and atypical cases as analysis-by-synthesis, or inference in a generative model of image formation. The model integrates intuitive physics to explain how shape can be inferred from the deformations it causes to other objects, as in cloth draping. Behavioural and computational studies comparing this account with several alternatives show that it best matches human observers (total n = 174) in both accuracy and response times, and is the only model that correlates significantly with human performance on difficult discriminations. We suggest that bottom-up deep neural network models are not fully adequate accounts of human shape perception, and point to how machine vision systems might achieve more human-like robustness.