The visual system discounts emotional deviants when extracting average expression

There has been a recent surge in the study of ensemble coding, the idea that the visual system represents a set of similar items using summary statistics (Alvarez & Oliva, 2008; Ariely, 2001; Chong & Treisman, 2003; Parkes, Lund, Angelucci, Solomon, & Morgan, 2001). We previously demonstrated that this ability extends to faces and thus requires a high level of object processing (Haberman & Whitney, 2007, 2009). Recent debate has centered on the nature of the summary representation of size (e.g., Myczek & Simons, 2008) and whether the perceived average simply reflects the sampling of a very small subset of the items in a set. In the present study, we explored this further in the context of faces, asking observers to judge the average expressions of sets of faces containing emotional outliers. Our results suggest that the visual system implicitly and unintentionally discounts the emotional outliers, thereby computing a summary representation that encompasses the vast majority of the information present. Additional computational modeling and behavioral results reveal that an intentional cognitive sampling strategy does not accurately capture observer performance. Observers derive precise ensemble information given a 250-msec exposure, suggesting a rapid and flexible system not bound by the limits of serial attention.