Multivariate summary of a complex scene

The current study investigated how people summarize and represent objects with multiple features to cope with the complexity due to the number of objects and feature dimensions. We presented a set of circles whose color and size were either correlated perfectly (r = 1) or not correlated at all (r = 0). Using a membership identification task, we found that participants formed a statistical representation that included information about conjunctions as well as each color and size dimensions. In addition, we found that participants represented different set boundaries depending on the correlation between features of a set. Lastly, a pair-matching task revealed that participants predicted one feature value from the other feature value based on the correlation between features of a set. Our findings suggest that people represent a multi-feature ensemble statistically as a multivariate feature distribution, which is an efficient strategy to cope with scene complexity.