Unlocking crowding by ensemble statistics.

In crowding, objects that can be easily recognized in isolation appear jumbled when surrounded by other elements. Traditionally, crowding is explained by local pooling mechanisms, but many findings have shown that the global configuration of the entire stimulus display, rather than local aspects, determines crowding. However, understanding global configurations is challenging because even slight changes can lead from crowding to uncrowding and vice versa. Unfortunately, the number of configurations to explore is virtually infinite. Here, we show that one does not need to know the specific configuration of flankers to determine crowding strength but only their ensemble statistics, which allow for the rapid computation of groups within the stimulus display. To investigate the role of ensemble statistics in (un)crowding, we used a classic vernier offset discrimination task in which the vernier was flanked by multiple squares. We manipulated the orientation statistics of the squares based on the following rationale: a central square with an orientation different from the mean orientation of the other squares stands out from the rest and groups with the vernier, causing strong crowding. If, on the other hand, all squares group together, the vernier is the only element that stands out, and crowding is weak. These effects should depend exclusively on the perceived ensemble statistics, i.e., on the mean orientation of the squares and not on their individual orientations. In two experiments, we confirmed these predictions.