Averaging multiple facial expressions through subsampling*

When perceivers view multiple facial expressions shown concurrently, they can quickly and precisely extract the mean emotion from the set. Yet it is not clear how many faces in the set contribute to summary judgments, and how the variance among them influences this process. To address these questions, we used the subset manipulation and varied emotion variance of faces in the sets across three experiments. Sets containing sixteen faces, or a subset of faces randomly selected from the sixteen-face display were presented, and participants judged the average emotion of each face set on a continuous scale. Results showed that when emotion variance was relatively large (Experiments 1 & 2), only two faces in the set contributed to ensemble representations. In Experiment 3 where the emotion variance was smaller, around three to four faces were likely sampled. However, when directly comparing results from Experiments 2 and 3, there was no strong evidence supporting the impact of variance in averaging efficiency. Altogether, these new results suggest that the process of averaging multiple emotional facial expressions can be explained by capacity-limited subsampling. The claim that ensemble representations are capacity unlimited or can overcome the bottlenecks in visual perception might need to be reconsidered