Flexible computation of object motion and depth based on viewing geometry inferred from optic flow.

We move our eyes and head to sample the visual environment. While these movements are essential for survival, they greatly complicate the analysis of retinal image motion. Our brain must account for the visual consequences of self-motion to perceive the 3D layout and motion of objects in a scene. We show that traditional models of visual compensation for eye movements fail when the eye both translates and rotates, and we propose a theory that computes both motion and depth in more natural viewing geometries. Consistent with our theoretical predictions, humans exhibit distinct perceptual biases when different viewing geometries are simulated by optic flow, and these biases occur without training or feedback. A neural network model trained to perform the same tasks suggests that viewing geometry modulates the joint tuning of neurons for retinal and eye velocity to mediate these adaptive computations. Our findings unify previously separate bodies of work by demonstrating that the brain adaptively perceives the dynamic 3D environment according to viewing geometry inferred from optic flow.