Judging the mood of the crowd: Attention is focused on happy faces.

Previous research on valence biases in face perception revealed inconsistent findings either proposing angry or happy faces to be detected more efficiently. We argue that the typical experimental task in this field, the face-in-the-crowd (FiC) paradigm, leads to ambiguous results. In the present paper, we introduce a new task, the mood-of-the-crowd (MoC) paradigm that can complement existing FiC findings. In the new task, participants have to decide which expression is shown by most faces in a crowd. In Experiment 1, photographs were used as stimuli, whereas computer-generated faces were presented in Experiments 2 and 3. While in the Experiments 1 and 2 crowds consisted of faces showing either happy and neutral expressions or angry and neutral expressions, in Experiment 3, crowds were composed of both angry and happy faces. Attentional processes were assessed with gaze recordings. Across the first two experiments, results indicate that happy faces are attended to with higher probability, and that the predominance of happy faces is assessed more accurately compared to the predominance of angry faces. In the last experiment, happy faces were focused on longer compared to angry expressions. Moreover, gender of presented faces was found to be an important moderator: There was a clear bias to classify female crowds as emotional (happy or angry). Additionally, the emotionality of female crowds was assessed more accurately. (PsycINFO Database Record (c) 2019 APA, all rights reserved).