  To effectively perceive and process observations in our environment, feature
binding and perspective taking are crucial cognitive abilities. Feature binding
combines observed features into one entity, called a Gestalt. Perspective
taking transfers the percept into a canonical, observer-centered frame of
reference. Here we propose a recurrent neural network model that solves both
challenges. We first train an LSTM to predict 3D motion dynamics from a
canonical perspective. We then present similar motion dynamics with novel
viewpoints and feature arrangements. Retrospective inference enables the
deduction of the canonical perspective. Combined with a robust mutual-exclusive
softmax selection scheme, random feature arrangements are reordered and
precisely bound into known Gestalt percepts. To corroborate evidence for the
architecture's cognitive validity, we examine its behavior on the silhouette
illusion, which elicits two competitive Gestalt interpretations of a rotating
dancer. Our system flexibly binds the information of the rotating figure into
the alternative attractors resolving the illusion's ambiguity and imagining the
respective depth interpretation and the corresponding direction of rotation. We
finally discuss the potential universality of the proposed mechanisms.
