In six parts, this book considers the extent to which computational, neural, and ecological constraints have shaped the mechanisms underlying motion vision: - Early Motion Vision - Motion Signals for Local and Global Analysis - Optical Flow Patterns - Motion Vision in Action - Neural Coding of Motion - Motion in Natural Environments Each topic is introduced by a keynote chapter which is accompanied by several companion articles. Written by an international group of experts in neurobiology, psychophysics, animal behaviour, machine vision, and robotics, the book is designed to explore as comprehensively as possible the present state of knowledge concerning the principal factors that have guided the evolution of motion vision.

Extraction of local motion signals is crucial for our survival. Lack of information from local motion signals will significantly reduce our ability to discriminate objects from background, avoid obstacles, and navigate. Despite the apparent effortlessness with which we perceive visual motion, there are indications that the underlying neural computations are complex. Three kinds of local motion signals have been distinguished, based on the kinds of spatiotemporal correlations that generate them: Fourier (F), based on 2-point correlations [1]; non-Fourier (NF), based on 4-point correlations [2]; and glider (G), based on 3-point correlations [3]. G signals have two subtypes, expansion and contraction, associated with objects that are looming and receding, respectively. Detection of isolated G and NF signals cannot be mediated by a purely multiplicative cross-correlator or a purely quadratic motion energy model. G signals have recently attracted substantial attention, following the demonstration that a wide range of species (human [3], macaque [4, 5], zebrafish [6], dragonfly [5], and fruitfly [7]) respond to them in similar ways suggesting that there are advantages to using these signals in visual tasks. This work expands the above lines of research in several respects. First, our computational work shows that these motion signals appear in natural scenes and characterizes the basic statistical relationships between them [8]. Second, we report neurophysiological recordings in two distinct visual-speciaist species (macaques and dragonflies) that demonstrate that at the neuronal level, cells response in a similar manner to motion signals in many respects, although there are subtle differences in responses between the species. This convergence at the algorithmic and neural-implementation levels indicate the fundamental biological importance of using the many kinds of motion signals to guide behavior. Finally, we carried out a psychophysical experiment to probe human ability to use multiple kinds of local motion signals simultaneously to solve simple directional task. We found that humans can combine different kinds of motion signals to solve this task, and, interestingly, that sensitivity to different kinds of motion signals is context-dependent.

Motion perception lies at the heart of the scientific study of vision. The motion aftereffect (MAE) is the appearance of directional movement in a stationary object or scene after the viewer has been exposed to viusal motion in the opposite direction. For example, after one has looked at a waterfall for a period of time, the scene beside the waterfall may appear to move upward when one's gaze is transfered to it. Although the phenomenon seems simple, research has revealed copmlexities in the underlying mechanisms, and offered general lessons about how the brain processes visual information. In the 1990s alone, more than 200 papers have been published on MAE, largely inspired by improved techniques for examining brain electrophysiology and by emerging new theories of motion perception.

It has become accepted in the neuroscience community that perception and performance are quintessentially multisensory by nature. Using the full palette of modern brain imaging and neuroscience methods, The Neural Bases of Multisensory Processes details current understanding in the neural bases for these phenomena as studied across species, stages of development, and clinical statuses. Organized thematically into nine sub-sections, the book is a collection of contributions by leading scientists in the field. Chapters build generally from basic to applied, allowing readers to ascertain how fundamental science informs the clinical and applied sciences. Topics discussed include: Anatomy, essential for understanding the neural substrates of multisensory processing Neurophysiological bases and how multisensory stimuli can dramatically change the encoding processes for sensory information Combinatorial principles and modeling, focusing on efforts to gain a better mechanistic handle on multisensory operations and their network dynamics Development and plasticity Clinical manifestations and how perception and action are affected by altered sensory experience Attention and spatial representations The last sections of the book focus on naturalistic multisensory processes in three separate contexts: motion signals, multisensory contributions to the perception and generation of communication signals, and how the perception of flavor is generated. The text provides a solid introduction for newcomers and a strong overview of the current state of the field for experts.

The contributors to this book focus on such key aspects of motion processing as interaction and integration between locally measured motion units, structure from motion, heading in an optical flow, and second-order motion. They also discuss the interaction of motion processing with other high-level visual functions such as surface representation and attention.

Designed for students, scientists and engineers interested in learning about the core ideas of vision science, this volume brings together the broad range of data and theory accumulated in this field.