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

Abstract

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, lacks ecological validity and 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 Experiment 2. While in the first experiments either happy and neutral or angry and neutral emotions were shown within one crowd, in Experiment 3, crowds were composed of angry and happy faces. Gaze position was recorded to gain further insights on attentional processes. In the first experiment, happy moods were identified faster and with a higher accuracy. Although we could only find higher accuracy rates for happy moods in experiment 2, happy faces were fixated more frequently indicating faster processing of more information in the same amount of time. A higher proportion of fixations and first fixation on happy faces occurred in experiment 3. Moreover, target gender was found to be an important moderator. In female crowds, emotion was generally assessed more accurately. Additionally, while emotional faces were overall focused more often than neutral faces, this effect was more pronounced in female crowds. The same pattern emerged in the comparison of happy vs. angry faces: Female happy faces had the highest probability to attract attention. The close connection between femininity and emotionality or rather happiness is discussed as a possible reason for these findings.