Scenes are complex, but not random. We know that keyboards tend to be found below monitors, and that chimneys are not found on the lawn. While many vision scientists believe that this contextual knowledge aids recognition, we cannot understand the extent to which it helps without first measuring how much redundancy there is. Though mining a large, fully-labeled scene database, I have provided a first set of contextual statistics, including object frequency and conditional frequencies based on scene category (Greene, Frontiers in Perception Science, 2013).
The next logical step is to understand how human observers internalize these contextual statistics. I asked observers to rate the frequency with which various objects could be found in various scenes. Across six experiments, I found that object frequency was systematically over-estimated by an average of 32% (Greene, Cognition, 2016).