Table of Contents

Low-Level Cortical Dynamic Motion Processing.- From Moving Contours to Object Motion: Functional Networks for Visual Form/Motion Processing.- Temporal Dynamics of Motion Integration.- Dynamics of Pattern Motion Computation.- Multiscale Functional Imaging in V1 and Cortical Correlates of Apparent Motion.- Stimulus Localization by Neuronal Populations in Early Visual Cortex: Linking Functional Architecture to Perception.- Second-Order Motion Stimuli: A New Handle to Visual Motion Processing.- Active Vision, Pursuit and Motion Perception.- Motion Detection for Reflexive Tracking.- When the Brain Meets the Eye: Tracking Object Motion.- Interactions Between Perception and Smooth Pursuit Eye Movements.- Perception of Intra-saccadic Motion.- Intrasaccadic Motion: Neural Evidence for Saccadic Suppression and Postsaccadic Enhancement.- Modeling Dynamic Processing.- Maximizing Causal Information of Natural Scenes in Motion.- Neural Models of Motion Integration, Segmentation, and Probabilistic Decision-Making.- Features in the Recognition of Emotions from Dynamic Bodily Expression.

About the Author

Guillaume S. Masson is Director of Research at the Institut de Neurosciences Cognitives de la Mediterranee (CNRS & Universite de la Mediterranee) where he is the head of the team Dynamics of Vision and Action. His research is devoted to understanding the sensory mechanisms involved in controlling our actions, in particular the link between visual motion processing and tracking eye movements. Behavioral studies conducted in both humans and animals are combined with physiological studies conducted at the population level. Uwe J. Ilg is Professor at the Hertie-Institute for Clinical Brain Research (HIH) and the Werner Reichardt Centre for Integrative Neuroscience (CIN) in Tuebingen in the beautiful south of Germany. He tries to gain deeper insights into the fundamentals of sensorimotor integration underlying vision by a combination of perceptual and behavioral studies of humans and animals together with the analysis of single-unit response properties.