(Dept. of Psychology Faculty Candidate)
The human visual system has a massively parallel architecture, and yet it can only accurately represent a handful of objects at once. A vast literature has focused on ways to explain these severe capacity limitations (slots, resources, central bottlenecks, brain juice, neural competition), but today I will focus on understanding how the visual system copes with these limitations. Specifically, I will focus on the possibility that the visual system does what we (scientists) would do with a bunch of noisy measurements: it averages them and combines them to represent a statistical summary, or ensemble representation. I will describe recent experiments which show that ensemble representations can be extracted at early processing stages (prior to object or scene recognition) and that these statistical representations enhance downstream processes, including attentional guidance, object recognition, and perhaps categorization as well. Combined, these results suggest that ensemble representations are efficiently computed and useful for many stages of visual processing, making ensemble coding a crucial mechanism for coping with the severe capacity limits on higher-level visual cognition.