Averaging Multiple Facial Expressions Through subsampling

Abstract

When perceivers view multiple facial expressions shown concurrently, they can quickly andprecisely extract the mean emotion from the set. Yet it is not clear how many faces in the setcontribute 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 offaces in the sets across three experiments. Sets containing sixteen faces, or a subset offaces randomly selected from the sixteen-face display were presented, and participants judgedthe average emotion of each face set on a continuous scale. Results showed that when emotionvariance was relatively large (Experiments 1 & 2), only two faces in the set contributed toensemble representations. In Experiment 3 where the emotion variance was smaller, aroundthree to four faces were likely sampled. However, when directly comparing results fromExperiments 2 and 3, there was no strong evidence supporting the impact of variance inaveraging efficiency. Altogether, these new results suggest that the process of averagingmultiple emotional facial expressions can be explained by capacity-limited subsampling. Theclaim that ensemble representations are capacity unlimited or can overcome the bottlenecks invisual perception might need to be reconsidered.