The utility of shape attributes in deciphering movements of non-rigid objects.

Most moving objects in the world are non-rigid, changing shape as they move. To disentangle shape changes from movements, computational models either fit shapes to combinations of basis shapes or motion trajectories to combinations of oscillations but are biologically unfeasible in their input requirements. Recent neural models parse shapes into stored examples, which are unlikely to exist for general shapes. We propose that extracting shape attributes, e.g., symmetry, facilitates veridical perception of non-rigid motion. In a new method, identical dots were moved in and out along invisible spokes, to simulate the rotation of dynamically and randomly distorting shapes. Discrimination of rotation direction measured as a function of non-rigidity was 90% as efficient as the optimal Bayesian rotation decoder and ruled out models based on combining the strongest local motions. Remarkably, for non-rigid symmetric shapes, observers outperformed the Bayesian model when perceived rotation could correspond only to rotation of global symmetry, i.e., when tracking of shape contours or local features was uninformative. That extracted symmetry can drive perceived motion suggests that shape attributes may provide links across the dorsal-ventral separation between motion and shape processing. Consequently, the perception of non-rigid object motion could be based on representations that highlight global shape attributes.