We see things around us effortlessly. For this reason it is often difficult for us to appreciate the enormous complexity involved in visual information processing in the brain. Researchers have been trying to build machines that can mimic various human brain functions. It turns out that seeing is a much more difficult task to emulate than, for example, playing chess or solving college physics problems. In fact, while there is already a chess machine, IBM's Deep Blue, that can challenge the world chess champion, we are still far from having a vision machine that approaches the visual capabilities of an average three-year old.
To find out how the brain achieves the remarkable feat of seeing, researchers have been studying various aspects of the visual system using a variety of techniques. The projects proposed in this application center around one of the most important functions of our visual system: It constantly recalibrates and improves itself by learning from experience. This so-called visual perceptual learning phenomenon has been observed in human subjects of all ages. Both theoretical and experimental methods will be applied in order to synthesize a large body of empirical findings into a coherent and logical framework, with the ultimate goal of understanding the neural mechanisms of perceptual improvement in the visual system. This synthesis cannot be achieved automatically through data accumulation alone, quantitative theories are required for understanding and relating different pieces of experimental data. (Analogously, how a computer works cannot be understood only through measuring connectivity and activity of the transistors and other components inside the computer; theoretical concepts such as operating system and data structure are required.) The focus of the proposed projects is to construct such quantitative theories for visual perceptual learning based on existing physiological and anatomical data. Results from these projects will not only advance our understanding of the brain mechanisms of visual perception and plasticity but will also provide important knowledge that might have practical engineering applications.
