The environments in which an organism lives and the tasks it performs to survive and reproduce shape the design of its perceptual, cognitive and motor systems through evolution and experience. This is an obvious statement, but it implies four fundamental components of research we must have if we are going to gain a deep understanding of these biological systems, and of how to design similar artificial systems:(i) methods for identifying and characterizing natural tasks,(ii) a science devoted to measuring and characterizing natural environments and stimuli (iii) mathematical and computational methods for understanding how intelligent systems could use knowledge of environmental and motor constraints to perform natural tasks, and (iv) experimental methods and technology that allow rigorous measurement of behavioral and neural responses, either in natural tasks or in artificial tasks that capture the essence of the natural tasks. These components are at the core of natural systems analysis, a scientific approach that has been gaining momentum in recent years and is revolutionizing research in behavioral science, neuroscience, and computational science. The articles in this special issue exemplify this emerging scientific approach. What are natural environments and tasks? Natural environments generally refer to the outdoor environments (forests, grasslands, mountains) in which our species and other species evolved, but they also include human-made indoor or outdoor environments. In humans, natural tasks refer to everyday tasks that we perform (or our ancestors performed) to survive, such as navigating through an environment, searching a crowd for a face or a voice that we know, picking fruits or vegetables from a plant, and making tools. These seem like simple tasks but they are extraordinarily complex and difficult, especially in natural environments, and thus behavioral scientists and systems neuroscientists have traditionally focused on simplified tasks carried out in laboratory environments with simple stimuli. Although there is no substitute for carefully-controlled laboratory experiments, scientific progress can be held back by not having a detailed understanding of natural environments and tasks. As just one example, consider the task of finding a familiar face in a crowd. To perform this task the brain must find faces using peripheral vision, direct the eyes towards each of them, and then compare the features of that face with stored representations of the faces it has seen before. To do these things well requires detailed knowledge of the relationship between the images that are formed in the eye and the actual three dimensional faces that occur in the environment. This relationship is extremely complex because of the enormous variation in the images produced by exactly the same face from one occasion to the next. The sources of variation include the distance and orientation of the head, the lighting that forms shadows and shading on the face, the presence of other objects that may partially block the face, and so on. The brain has learned about all these sources of variation through evolution and experience and exploits this knowledge to obtain remarkably accurate perception, recognition and control of eye movements. Unfortunately, we are still a long way from understanding how the brain accomplishes such tasks. A major reason for the slow progress may be the traditional scientific approach, which focuses on neural mechanisms and computational algorithms without first obtaining a detailed understanding of the relevant properties of the environment and the requirements of the task. In the traditional approach, the scientist thinks about the …