Topic: middle temporal area (MT)

Visual decisions typically require accumulation of uncertain sensory information to make inferences about the state of the world. This inference process is complicated by the fact that the state of the world can change, which often requires adjustments in how sensory information is accumulated. Previous work has modeled optimal inference in these changing environments [1-2]. One key feature of these models is a leakage of accumulated evidence, where the leak rate depends on environmental stability. For example, in more unstable environments, an increased leak rate helps remove pre-change evide

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 continually updates an estimate of a visual scene’s underlying structure while using this inferred structure