Capacity limit of ensemble perception of multiple spatially intermixed sets

The visual system is remarkably efficient at extracting summary statistics from the environment. Yet at any given time, the environment consists of many groups of objects distributed over space. Thus, the challenge for the visual system is to summarize over multiple groups. The current study investigates the capacity and computational efficiency of ensemble perception, in the context of perceiving mean sizes of multiple spatially intermixed groups of circles. In a series of experiments, participants viewed an array of one to eight sets of circles. Each set contained four circles in the same colors, but with different sizes. Participants estimated the mean size of a probed set. The set that would be probed was either known before onset of the array (pre-cue condition) or afterwards (post-cue condition). By comparing estimation error in the pre-cue and post-cue conditions, we found that participants could reliably estimate mean sizes for approximately two sets (Experiment 1). Importantly, this capacity was robust against attention bias toward individual objects in the sets (Experiment 2). Varying the exposure time to stimulus arrays did not increase the capacity limit, suggesting that ensemble perception could be limited by an internal resource constraint, rather than the speed of information encoding (Experiment 3). Moreover, we found that the visual system could not encode and hold more individual items than ensemble representations (Experiment 4). Taken together, these results suggest that ensemble perception provides an efficient way of information processing but with constraints.