Hierarchical Vector Analysis of Visual Motion Perception.

Abstract

Visual scenes are often populated by densely layered and complex patterns of motion. The problem of motion parsing is to break down these patterns into simpler components that are meaningful for perception and action. Psychophysical evidence suggests that the brain decomposes motion patterns into a hierarchy of relative motion vectors. Recent computational models have shed light on the algorithmic and neural basis of this parsing strategy. We review these models and the experiments that were designed to test their predictions. Zooming out, we argue that hierarchical motion perception is a tractable model system for understanding how aspects of high-level cognition such as compositionality may be implemented in neural circuitry.