Spatial ensemble statistics are efficient codes that can be represented with reduced attention.

There is a great deal of structural regularity in the natural environment, and such regularities confer an opportunity to form compressed, efficient representations. Although this concept has been extensively studied within the domain of low-level sensory coding, there has been limited focus on efficient coding in the field of visual attention. Here we show that spatial patterns of orientation information ("spatial ensemble statistics") can be efficiently encoded under conditions of reduced attention. In our task, observers monitored for changes to the spatial pattern of background elements while they were attentively tracking moving objects in the foreground. By using stimuli that enable us to dissociate changes in local structure from changes in the ensemble structure, we found that observers were more sensitive to changes to the background that altered the ensemble structure than to changes that did not alter the ensemble structure. We propose that reducing attention to the background increases the amount of noise in local feature representations, but that spatial ensemble statistics capitalize on structural regularities to overcome this noise by pooling across local measurements, gaining precision in the representation of the ensemble.