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.

Everyday life requires humans to move through the environment, while completing crucial tasks such as retrieving nourishment, avoiding perils or controlling motor vehicles. Success in these tasks largely relies in a correct perception of self-motion, i.e. the continuous estimation of one's body position and its derivatives with respect to the world. The processes underlying self-motion perception have fascinated neuroscientists for more than a century and large bodies of neural, behavioural and physiological studies have been conducted to discover how the central nervous system integrates available sensory information to create an internal representation of the physical motion. The goal of this PhD thesis is to extend current knowledge on self-motion perception by focusing on conditions that closely resemble typical aspects of everyday life. In the works conducted within this thesis, I isolate different components typical of everyday life motion and employ psychophysical methodologies to systematically investigate their effect on human self-motion sensitivity. Particular attention is dedicated to the human ability to discriminate between motions of different intensity. How this is achieved has been a fundamental question in the study of perception since the seminal works of Weber and Fechner. When tested over wide ranges of rotations and translations, participants' sensitivity (i.e. their ability to detect motion changes) is found to decrease with increasing motion intensities, revealing a nonlinearity in the perception of self-motion that is not present at the level of ocular reflexes or in neural responses of sensory afferents. The relationship between the stimulus intensity and the smallest intensity change perceivable by the participants can be mathematically described by a power law, regardless on the sensory modality investigated (visual or inertial) and on whether visual and inertial cues were presented alone or congruently combined, such as during natural movements. Individual perceptual law parameters were fit based on experimental data for upward and downward translations and yaw rotations based on visual-only, inertial-only and combined visual-inertial motion cues. Besides wide ranges of motion intensities, everyday life scenarios also provide complex motion patterns involving combinations of rotational and translational motion, visual and inertial sensory cues and physical and mental workload. The question of how different combinations of these factors affect motion sensitivity was experimentally addressed within the framework of driving simulation and revealed that sensitivity might strongly decrease in more realistic conditions, where participants do not only focus on perceiving a 'simple' motion stimulus (e.g. a sinusoidal profile at a specific frequency) but are, instead, actively engaged in a dynamic driving simulation. Applied benefits of the present thesis include advances in the field of vehicle motion simulation, where knowledge on human self-motion perception supports the development of state-of-the-art algorithms to control simulator motion. This allows for reproducing, within a safe and controlled environment, driving or flying experiences that are perceptually realistic to the user. Furthermore, the present work will guide future research into the neural basis of perception and action.

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

Motion is an important cue in the everyday lives of visual creatures. Motion information facilitates the separation of figure from background, aides in seeing objects that would otherwise be effectively camouflaged and surfaces that would be otherwise imperceptible. The research presented is an investigation of the neural correlates of complex motion stimuli. Experiment 1 is a psychophysical investigation of a complex motion phenomenon, called biological motion. Previous research has shown the resilience of this stimulus under highly degraded conditions, but by creating stimuli that favor the "form system", we measured the reliance of biological motion perception on the "motion system". We challenge form-based biological motion research, and we conclude that motion perception is necessary (but not sufficient) for perceiving biological motion. We conjecture that this insufficiency is due to another mechanism, in addition to those involved in simple motion discriminations. Experiment series 2 is a neuroimaging investigation of biological motion as a function of contrast modulations, which seeks to find the neural correlate of the effect found in Experiment 1. We specifically targeted the human middle temporal complex (hMT+, the motion-sensitive human homologue to monkey MT), a region implicated in motion perception and historically important in neuroscience research. We find the responses in hMT+ to be stimulus-dependent and to be a part of network of brain regions supporting complex motion perception. Experiment series 3 is a neuroimaging investigation of another form of complex motion perception, a phenomenon called motion transparency. When the visual system encounters two overlapping motion vectors, it resolves them by segmenting them into different surfaces (or objects). We attempt to uncover the neural basis of object segmentation defined by motion vectors. We find the hMT to house competing motion vectors with mutual inhibition, including a local competition between motion vectors as well as a global competition between motion-defined surfaces. These results add to the expansive literature on motion processing and depart from a more traditional depiction of the neural underpinnings of motion perception.

Self-motion describes the motion of our body through the environment and is an essential part of our everyday life. The aim of this thesis is to improve our understanding of how humans perceive self-motion, mainly focusing on the role of the vestibular system. Following a cybernetic approach, this is achieved by systematically gathering psychophysical data and then describing it based on mathematical models of the vestibular sensors. Three studies were performed investigating perceptual thresholds for translational and rotational motions and reaction times to self-motion stimuli. Based on these studies, a model is introduced which is able to describe thresholds for arbitrary motion stimuli varying in duration and acceleration profile shape. This constitutes a significant addition to the existing literature since previous models only took into account the effect of stimulus duration, neglecting the actual time course of the acceleration profile. In the first and second study model parameters were identified based on measurements of direction discrimination thresholds for translational and rotational motions. These models were used in the third study to successfully predict differences in reaction times between varying motion stimuli proving the validity of the modeling approach. This work can allow for optimizing motion simulator control algorithms based on self-motion perception models and developing perception based diagnostics for patients suffering from vestibular disorders.