For the successful recognition of objective, `real' motion based on visual cues it is necessary to take self-induced motion signals into account, such as those induced by eye-movements. During a series of fMRI studies we measured responses of visual and parietal regions to motion cues derived from (a) retinal motion, (b) eyemovements (visual pursuit) and (c) objective, (real) motion. We show that the recently described cingulate sulcus visual area (CSv) is not, as implied before, primarily driven by 3D self-motion cues but favoured 2D translational coherent motion over 3D expanding flow fields. Further, we found that V3A is capable of integrating retinal motion with eye-movements, thus allowing V3A to respond to object motion independent of retinal motion. This allowed us to define a new functional localizer for area V3A. Finally, we showed that activity in the foveal representation of the early visual cortex is driven by a combination of retinal input and by error signals as hypothesized by of Rao and Ballard (1999) for predictive coding. Taken together, this work provides evidence that regions V3A and CSv are key regions concerning visual self-motion processing and that early visual regions might be modulated by feedback from higher motion processing regions.

Motion processing is an essential piece of the complex brain machinery that allows us to reconstruct the 3D layout of objects in the environment, to break camouflage, to perform scene segmentation, to estimate the ego movement, and to control our action. Although motion perception and its neural basis have been a topic of intensive research and modeling the last two decades, recent experimental evidences have stressed the dynamical aspects of motion integration and segmentation. This book presents the most recent approaches that have changed our view of biological motion processing. These new experimental evidences call for new models emphasizing the collective dynamics of large population of neurons rather than the properties of separate individual filters. Chapters will stress how the dynamics of motion processing can be used as a general approach to understand the brain dynamics itself.

When we walk, drive a car, or fly an airplane, visual motion is used to control and guide our movement. Optic flow describes the characteristic pattern of visual motion that arises in these situations. This book is the first to take an in-depth look at the neuronal processing strategies that underlie the brain's ability to analyze and use optic flow for the control of self-motion. It does so in a variety of species which use optic flow in different behavioral contexts. The spectrum ranges from flying insects to birds, higher mammals and man. The contributions cover physiological and behavioral studies as well as computational models. Neuronal Processing of Optic Flow provides an authoritative and comprehensive overview of the current state of research on this topic written by a group of authors who have made essential contributions to shaping this field of research over the last ten years. Provides the first detailed overview of the analysis of complex visual motion patterns in the brain Includes physiological, behavioral, and computational aspects of optic flow processing Highlights similarities and differences between different animal species and behavioral tasks Covers human patients with visual motion deficits Enhances the reader's understanding with many illustrations

Both visual area V1 and the medial temporal (MT) region of the human brain are involved in motion perception. V1 is thought to process “local motion,” such as the movement of a bird flying across a relatively small part of space, while MT is thought to process “global motion,” such as the movement of a flock of birds flying across the sky. However, recent studies using fMRI to measure human brain activity have identified signals in V1 that appear to be global motion signals, although it is unclear whether these are related to global motion processing or some other process. In two experiments, a series of stimulus manipulations were conducted to determine the extent to which these signals in V1 reflect global motion. Although initial results have so far proven inconclusive, they highlight discrepancies between previous results, suggesting that V1 motion signals may be more interesting than researchers have assumed.

Current theories of visual change detection emphasize the importance of conscious attention to detect unexpected changes in the visual environment. However, an increasing body of studies shows that the human brain is capable of detecting even small visual changes, especially if such changes violate non-conscious probabilistic expectations based on repeating experiences. In other words, our brain automatically represents statistical regularities of our visual environmental. Since the discovery of the auditory mismatch negativity (MMN) event-related potential (ERP) component, the majority of research in the field has focused on auditory deviance detection. Such automatic change detection mechanisms operate in the visual modality too, as indicated by the visual mismatch negativity (vMMN) brain potential to rare changes. VMMN is typically elicited by stimuli with infrequent (deviant) features embedded in a stream of frequent (standard) stimuli, outside the focus of attention. In this research topic we aim to present vMMN as a prediction error signal. Predictive coding theories account for phenomena such as mismatch negativity and repetition suppression, and place them in a broader context of a general theory of cortical responses. A wide range of vMMN studies has been presented in this Research Topic. Twelve articles address roughly four general sub-themes including attention, language, face processing, and psychiatric disorders. Additionally, four articles focused on particular subjects such as the oblique effect, object formation, and development and time-frequency analysis of vMMN. Furthermore, a review paper presented vMMN in a hierarchical predictive coding framework. Each paper in this Research Topic is a valuable contribution to the field of automatic visual change detection and deepens our understanding of the short term plasticity underlying predictive processes of visual perceptual learning.

The brain's ability to detect movement within the retinal image is crucial not only for determining the trajectories of moving objects, but also for identifying and interpreting image motion resulting from eye and head movements. This book summarizes our knowledge of how information about image motion is encoded in the brain. Key Features * Valuable reference source for those involved in the rapidly expanding area of motion perception * Strong emphasis on integration of physiological, computation, and psychophysical approaches * Topics include: * Principles of local motion detection * Inputs to local motion detectors * Integration of motion signals * Higher-order interpretation of motion * Motion detection and eye movements