Includes bibliographical references and index.

In this introduction to modern vision science, the author uses visual illusions to explore how the brain sees the world. Understanding vision, he argues, is not simply a question of knowing which neurons respond to particular visual features, but also requires a computational theory of vision. He 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 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. It emphasizes key conceptual insights, rather than mathematical details.

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