Perceptual learning refers to improvements in one's sensory abilities that occur through experience with a particular environment, and it is thought to help us to become more efficient in processing sensory information. Recently Drs. Aaron Seitz and Takeo Watanabe have found that viewers can improve their perceptual abilities through sub-threshold exposure to visual stimuli. These improvements in perception have been shown to involve the primary visual areas of the brain and are thought to be an indicator of basic mechanisms of learning that are common throughout the nervous system. With support from the National Science Foundation, Drs. Watanabe and Seitz will conduct research that will clarify two important aspects of perceptual learning. In particular, their work will identify neural circuits that are modified during perceptual learning, and it will help to better characterize the processes that lead to this phenomenon. This research will be conducted by combining innovative psychophysical methods with functional Magnetic Resonance Imaging (fMRI) in order to systematically measure activity in different areas of the brain before during, and after such learning has occurred.
This research will have broad impacts. The conduct of the studies will help to train graduate and undergraduate students in research methodology and the use of brain imaging technology. The results from the studies will help illuminate the processes by which perceptual skills can be improved; such knowledge could lead to enhanced training techniques for those afflicted with disorders that affect sensory processing, as well as to improvements in training methods for experts who perform work that requires highly acute sensory abilities, such as pilots and radiologists. This work also has important implications for clinical and rehabilitative medicine as it has the potential to lead to improvements in medical prostheses and tools for the diagnosis and treatment of sensory disorders. Finally, improvements in the understanding of human sensory and learning processes could help to guide the development of technologies for automating such abilities in the context of a wide variety of industrial applications.
