Capacity and architecture of emotional face-ensemble coding

The ability to process emotion in ensembles of faces is essential for social functioning and survival. This study investigated the efficiency and underlying architecture of this ability in two contrasting tasks: (a) extracting the mean emotion from a set of faces, and (b) visually searching for a single, redundant-target face within an ensemble. I asked whether these tasks rely on similar or distinct processing mechanisms. To address this, I applied the capacity coefficient—a rigorous measure based on the entire response time distribution. In Experiment 1, participants judged the average emotion of face ensembles. In Experiments 2 and 3, participants searched for a predefined emotional target among multiple faces. In both tasks, workload was manipulated by varying the number of faces in the display. Results revealed that ensemble averaging is a super-capacity process that improves with increased workload, while visual search is capacity-limited and impaired by greater workload. These findings suggest that averaging is a preattentive process supported by a coactive, summative architecture, whereas visual search is attention-dependent and governed by a serial or parallel architecture with inhibitory interactions between display items.