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 sets distributed across space. My thesis work investigates the capacity constraints and computational efficiency of ensemble perception, in the context of perceiving multiple spatially intermixed groups of objects. First, in three experiments, participants viewed an array of 1 to 8 intermixed 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. Which set would be probed was either known before onset of the array (pre-cue), or after that (post-cue). Fitting a uniform-normal mixture model to the error distribution, I found participants could reliably estimate mean sizes for maximally four sets (Experiment 1). Importantly, their performance was unlikely to be driven by a subsampling strategy (Experiment 2). Allowing longer exposure to the stimulus array did not increase the capacity, suggesting ensemble perception was limited by an internal resource constraint, rather than an information encoding rate (Experiment 3). Second, in two experiments, I showed that the visual system could hold up to four ensemble representations, or up to four individual items (Experiment 4), and an ensemble representation had an information uncertainty (entropy) level similar to that of an individual representation (Experiment 5). Taken together, ensemble perception provides a compact and efficient way of information processing.Medicine, Faculty ofGraduat