Humans are highly visual animals and our daily experiences, memories, and dreams are dominated by our visual sense. To create the continuous and seamless visual experience, humans explore their environment by making three to four eye fixations every second, each followed by a rapid saccadic eye movement to the next object of interest. However, most experiments on vision are conducted with research participants trained to keep their eyes still to remove these saccadic eye movements. Such an approach may not tell us in full how vision works in its naturalistic context where eyes are freely moving. In this project, Dr. Michael Paradiso of Brown University will use single unit recording from monkeys when they make visual decisions about objects appearing during or just after saccadic eye movements. The data are expected to reveal how eye movements alter visual processing and how the brain is able to parse the neural continuum into discrete perceptual block using signals associated with saccades. These experiments are innovative in their integration of behavioral testing in naturalistic paradigms with brain recordings using leading edge multi-electrode technology. The researcher aims to bridge the gap between basic research and our understanding of human visual experience in the real world.
Numerous disorders ranging from autism, to dyslexia and schizophrenia exhibit abnormal eye movements. At the present it is unclear how the abnormal eye movements are involved in the disorder, but the proposed project will provide critical data that can serve as a foundation for further studies targeted at specific disorders. Through the project, the researcher will continue to give lectures to K-12 students, collaborate with K-12 teachers on science instruction, and host a discussion group in Brown's Catalyst Program for incoming minority students at Brown University. Finally, Dr. Paradiso will participate in the big data effort by making the data available to support other coordinated NSF efforts that aim to make use of real data in the teaching of STEM related courses and to enable participation in discovery science by those who would otherwise have no access to such data.
