Individuals can rapidly and precisely judge the average of a set of similar items, including both low-level (Ariely, ) and high-level objects (Haberman & Whitney, ). However, to date, it is unclear whether ensemble perception is based on viewpoint-invariant object representations. Here, we tested this question by presenting participants with crowds of sequentially presented faces. The number of faces in each crowd and the viewpoint of each face varied from trial to trial. This design required participants to integrate information from multiple viewpoints into one ensemble percept. Participants reported the mean identity of crowds (e.g., family resemblance) using an adjustable, forward-oriented test face. Our results showed that participants accurately perceived the mean crowd identity even when required to incorporate information across multiple face orientations. Control experiments showed that the precision of ensemble coding was not solely dependent on the length of time participants viewed the crowd. Moreover, control analyses demonstrated that observers did not simply sample a subset of faces in the crowd but rather integrated many faces into their estimates of average crowd identity. These results demonstrate that ensemble perception can operate at the highest levels of object recognition after 3-D viewpoint-invariant faces are represented.
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