Classification images for simple detection and discrimination tasks in correlated noise

We use the classification image technique to investigate the effect of white noise and various correlated Gaussian noise textures (low-pass, high-pass, and band-pass) on observer performance in detection and discrimination tasks. For these tasks, performance is generally enhanced by an observer's ability to "prewhiten" correlated noise as part of the formation of a decision variable. We find that observer efficiency in these tasks is well represented by the measured classification images and that human observers show strong evidence of adaptation to different correlated noise textures. This adaptation is captured in the frequency weighting of the classification images.