Garrido Margarida V, Prada Marília
Instituto Universitário de Lisboa (ISCTE-IUL), CIS - IUL, Lisboa, Portugal.
Front Psychol. 2017 Dec 19;8:2181. doi: 10.3389/fpsyg.2017.02181. eCollection 2017.
The Karolinska Directed Emotional Faces (KDEF) is one of the most widely used human facial expressions database. Almost a decade after the original validation study (Goeleven et al., 2008), we present subjective rating norms for a sub-set of 210 pictures which depict 70 models (half female) each displaying an angry, happy and neutral facial expressions. Our main goals were to provide an additional and updated validation to this database, using a sample from a different nationality ( = 155 Portuguese students, = 23.73 years old, = 7.24) and to extend the number of subjective dimensions used to evaluate each image. Specifically, participants reported emotional labeling (forced-choice task) and evaluated the emotional intensity and valence of the expression, as well as the attractiveness and familiarity of the model (7-points rating scales). Overall, results show that happy faces obtained the highest ratings across evaluative dimensions and emotion labeling accuracy. Female (vs. male) models were perceived as more attractive, familiar and positive. The sex of the model also moderated the accuracy of emotional labeling and ratings of different facial expressions. Each picture of the set was categorized as low, moderate, or high for each dimension. Normative data for each stimulus (hits proportion, means, standard deviations, and confidence intervals per evaluative dimension) is available as supplementary material (available at https://osf.io/fvc4m/).
卡罗林斯卡定向情感面孔(KDEF)是使用最为广泛的人类面部表情数据库之一。在最初的验证研究(Goeleven等人,2008年)开展近十年后,我们给出了210张图片子集的主观评分规范,这些图片描绘了70个模特(一半为女性),每个模特分别展示愤怒、快乐和中性的面部表情。我们的主要目标是使用来自不同国籍的样本(155名葡萄牙学生,平均年龄23.73岁,标准差7.24)为该数据库提供额外的更新验证,并扩展用于评估每张图片的主观维度数量。具体而言,参与者报告情感标签(强制选择任务),并评估表情的情感强度和效价,以及模特的吸引力和熟悉度(7点量表)。总体而言,结果表明快乐面孔在各个评估维度和情感标签准确性方面获得了最高评分。女性(与男性相比)模特被认为更具吸引力、更熟悉且更积极。模特的性别也调节了情感标签的准确性和不同面部表情的评分。该集合中的每张图片在每个维度上都被分类为低、中或高。每个刺激的规范数据(每个评估维度的命中比例、均值、标准差和置信区间)可作为补充材料获取(可在https://osf.io/fvc4m/上获取)。