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Vision

From Laboratory to Theory

Vision is one of the most active areas in biomedical research, and visual psychophysical techniques are a foundational methodology for this research enterprise. Visual psychophysics, which studies the relationship between the physical world and human behavior, is a classical field of study that has widespread applications in modern vision science. Bridging the gap between theory and practice, this textbook provides a comprehensive treatment of visual psychophysics, teaching not only basic techniques but also sophisticated data analysis methodologies and theoretical approaches. It begins with practical information about setting up a vision lab and goes on to discuss the creation, manipulation, and display of visual images; timing and integration of displays with measurements of brain activities and other relevant techniques; experimental designs; estimation of behavioral functions; and examples of psychophysics in applied and clinical settings.

The book's treatment of experimental designs presents the most commonly used psychophysical paradigms, theory-driven psychophysical experiments, and the analysis of these procedures in a signal-detection theory framework. The book discusses the theoretical underpinnings of data analysis and scientific interpretation, presenting data analysis techniques that include model fitting, model comparison, and a general framework for optimized adaptive testing methods. It includes many sample programs in Matlab with functions from Psychtoolbox, a free toolbox for real-time experimental control. Once students and researchers have mastered the material in this book, they will have the skills to apply visual psychophysics to cutting-edge vision science.

How We Perceive the World

In this accessible and engaging introduction to modern vision science, James Stone uses visual illusions to explore how the brain sees the world. Understanding vision, Stone argues, is not simply a question of knowing which neurons respond to particular visual features, but also requires a computational theory of vision. Stone draws together results from David Marr’s computational framework, Barlow’s efficient coding hypothesis, Bayesian inference, Shannon’s information theory, and signal processing to construct a coherent account of vision that explains not only how the brain is fooled by particular visual illusions, but also why any biological or computer vision system should also be fooled by these illusions.

This short text includes chapters on the eye and its evolution, how and why visual neurons from different species encode the retinal image in the same way, how information theory explains color aftereffects, how different visual cues provide depth information, how the imperfect visual information received by the eye and brain can be rescued by Bayesian inference, how different brain regions process visual information, and the bizarre perceptual consequences that result from damage to these brain regions. The tutorial style emphasizes key conceptual insights, rather than mathematical details, making the book accessible to the nonscientist and suitable for undergraduate or postgraduate study.

The Computational Approach to Biological Vision

Seeing has puzzled scientists and philosophers for centuries and it continues to do so. This new edition of a classic text offers an accessible but rigorous introduction to the computational approach to understanding biological visual systems. The authors of Seeing, taking as their premise David Marr’s statement that “to understand vision by studying only neurons is like trying to understand bird flight by studying only feathers,” make use of Marr’s three different levels of analysis in the study of vision: the computational level, the algorithmic level, and the hardware implementation level. Each chapter applies this approach to a different topic in vision by examining the problems the visual system encounters in interpreting retinal images and the constraints available to solve these problems; the algorithms that can realize the solution; and the implementation of these algorithms in neurons.Seeing has been thoroughly updated for this edition and expanded to more than three times its original length. It is designed to lead the reader through the problems of vision, from the common (but mistaken) idea that seeing consists just of making pictures in the brain to the minutiae of how neurons collectively encode the visual features that underpin seeing. Although it assumes no prior knowledge of the field, some chapters present advanced material, This makes it the only textbook suitable for both undergraduate and graduate students that takes a consistently computational perspective, offering a firm conceptual basis for tackling the vast literature on vision. It covers a wide range of topics, including aftereffects, the retina, receptive fields, object recognition, brain maps, Bayesian perception, motion, color, and stereopsis. MatLab code is available on the book’s Web site, which includes a simple demonstration of image convolution.

Downloadable instructor resources available for this title: file of figures in the book

Selected Readings in the Philosophy of Perception

The philosophy of perception is a microcosm of the metaphysics of mind. Its central problems—What is perception? What is the nature of perceptual consciousness? How can one fit an account of perceptual experience into a broader account of the nature of the mind and the world?—are at the heart of metaphysics. Rather than try to cover all of the many strands in the philosophy of perception, this book focuses on a particular orthodoxy about the nature of visual perception.

The central problem for visual science has been to explain how the brain bridges the gap between what is given to the visual system and what is actually experienced by the perceiver. The orthodox view of perception is that it is a process whereby the brain, or a dedicated subsystem of the brain, builds up representations of relevant figures of the environment on the basis of information encoded by the sensory receptors. Most adherents of the orthodox view also believe that for every conscious perceptual state of the subject, there is a particular set of neurons whose activities are sufficient for the occurrence of that state. Some of the essays in this book defend the orthodoxy; most criticize it; and some propose alternatives to it. Many of the essays are classics. The contributors include, among others, G.E.M. Anscombe, Dana Ballard, Daniel Dennett, Fred Dretske, Jerry Fodor, H.P. Grice, David Marr, Maurice Merleau-Ponty, Zenon Pylyshyn, Paul Snowdon, and P.F. Strawson.

Photons to Phenomenology

This book revolutionizes how vision can be taught to undergraduate and graduate students in cognitive science, psychology, and optometry. It is the first comprehensive textbook on vision to reflect the integrated computational approach of modern research scientists. This new interdisciplinary approach, called "vision science," integrates psychological, computational, and neuroscientific perspectives.The book covers all major topics related to vision, from early neural processing of image structure in the retina to high-level visual attention, memory, imagery, and awareness. The presentation throughout is theoretically sophisticated yet requires minimal knowledge of mathematics. There is also an extensive glossary, as well as appendices on psychophysical methods, connectionist modeling, and color technology. The book will serve not only as a comprehensive textbook on vision, but also as a valuable reference for researchers in cognitive science, psychology, neuroscience, computer science, optometry, and philosophy.

Downloadable instructor resources available for this title: file of figures in the book