Pain E-motion Faces Database (PEMF): Pain-related micro-clips for emotion research

Abstract

AbstractA large number of publications have focused on the study of pain expressions. Despite the growing knowledge, the availability of pain-related face databases is still very scarce compared with other emotional facial expressions. The Pain E-Motion Faces Database (PEMF) is a new open-access database currently consisting of 272 micro-clips of 68 different identities. Each model displays one neutral expression and three pain-related facial expressions: posed, spontaneous-algometer and spontaneous-CO2 laser. Normative ratings of pain intensity, valence and arousal were provided by students of three different European universities. Six independent coders carried out a coding process on the facial stimuli based on the Facial Action Coding System (FACS), in which ratings of intensity of pain, valence and arousal were computed for each type of facial expression. Gender and age effects of models across each type of micro-clip were also analysed. Additionally, participants’ ability to discriminate the veracity of pain-related facial expressions (i.e., spontaneous vs posed) was explored. Finally, a series of ANOVAs were carried out to test the presence of other basic emotions and common facial action unit (AU) patterns. The main results revealed that posed facial expressions received higher ratings of pain intensity, more negative valence and higher arousal compared with spontaneous pain-related and neutral faces. No differential effects of model gender were found. Participants were unable to accurately discriminate whether a given pain-related face represented spontaneous or posed pain. PEMF thus constitutes a large open-source and reliable set of dynamic pain expressions useful for designing experimental studies focused on pain processes.