System-Level Computations Underlie Visual Field Heterogeneity
Visual perception varies with eccentricity and polar angle. We investigated whether and how system-level computations, which transform visual input into perception, underlie these heterogeneities. Using the equivalent noise method and perceptual template model, we estimated gain, internal noise, and nonlinearity for orientation discrimination across eccentricity (fovea, parafovea and perifovea) and around polar angle. Performance declined with eccentricity due to decreased gain and nonlinearity and increased internal noise. Observers with stronger eccentricity effects showed greater gain decrease. Only gain varied with polar angle, higher along the horizontal than vertical meridian, and lower than upper vertical meridian, paralleling performance asymmetries. This dissociation aligns with known variations in neuronal count and tuning, suggesting that neural correlations and neural noise contribute to these system-level computations. By revealing distinct system-level computations underlying the eccentricity effect and polar angle asymmetries, our findings provide a link between perceptual heterogeneity across the visual field and neural architecture.