Visual motion perception as online hierarchical inference

Johannes Bill, Samuel J. Gershman, Jan Drugowitsch

Abstract:

Identifying the structure of motion relations in the environment is critical for
navigation, tracking, prediction, and pursuit. Yet, little is known about the
mental and neural computations that allow the visual system to infer this
structure online from a volatile stream of visual information. We propose
online hierarchical Bayesian inference as a principled solution for how the
brain might solve this complex perceptual task. We derive an online
Expectation-Maximization algorithm that explains human percepts qualita-
tively and quantitatively for a diverse set of stimuli, covering classical psy-
chophysics experiments, ambiguous motion scenes, and illusory motion
displays. We thereby identify normative explanations for the origin of human
motion structure perception and make testable predictions for future psy-
chophysics experiments. The proposed online hierarchical inference model
furthermore affords a neural network implementation which shares properties
with motion-sensitive cortical areas and motivates targeted experiments to
reveal the neural representations of latent structure