Project Summary Vision at the center of gaze (fovea) has high sensitivity and resolution, facilitating good performance in many tasks. But performance worsens with increasing distance from fovea–eccentricity. At any given eccentricity stimuli can fall anywhere along the circle –polar angle. Both eccentricity and polar angle have pronounced effects on perception in human adults. These factors present an ideal opportunity for establishing tight quantitative links between behavior and neural representations of visual information. Our long-term goal is to understand how visual performance varies across the visual field, to develop a theory of spatial vision that includes the neural and computational mechanisms underlying performance variation with eccentricity, polar angle and individuals. Such a theory will be applicable to basic and translational research in perceptual and cognitive neuroscience. We propose to investigate whether and how neural and computational factors distinctly limit discriminability across observers, eccentricity and polar angle. Our overall hypothesis is that variability in cortical magnification limits discriminability as a function of eccentricity, polar angle and observer, and that these effects are mediated by different combinations of noise, efficiency and sensory tuning. The proposed psychophysical [Aim 1] and neuroimaging [Aim 2] measures will characterize internal/neural noise and sensory/neural tuning across eccentricity and around polar angle, and will constrain a computational observer model of contrast sensitivity and acuity tasks [Aim 3]. In addition to advancing our knowledge of visual perception and cognitive neuroscience, the proposed research will enable us to make predictions about human performance. The characterization of eccentricity and polar angle has significant implications for ergonomic and human factors applications as well as for public health. For example, it is of critical importance for user-interfaces that present information at different locations of the visual field. We can extend our knowledge to real-world displays, such as navigation and cockpit alerting systems, control panel layouts in cars, computer-aided detection systems, and software for presenting radiological images. Furthermore, the gained knowledge can aid the design of artificial image recognition systems. In addition, understanding the underlying neural and computational mechanisms of performance differences across the visual field will improve our models of visual dysfunction (e.g., macular degeneration, retinitis pigmentosa), as well as the diagnosis of these disorders.