Department of Psychology, School of Education, Soochow University, Suzhou, Jiangsu, 215123, China.
College of Arts and Sciences, Syracuse University, Syracuse, NY, 13244, USA.
Behav Res Methods. 2024 Mar;56(3):2581-2594. doi: 10.3758/s13428-023-02168-4. Epub 2023 Aug 1.
Affective picture databases with a single facial expression or body posture in one image have been widely applied to investigate emotion. However, to date, there was no standardized database containing the stimuli which involve multiple emotional signals in social interactive scenarios. The current study thus developed a pictorial set comprising 274 images depicting two Chinese adults' interactive scenarios conveying emotions of happiness, anger, sadness, fear, disgust, and neutral. The data of the valence and arousal ratings of the scenes and the emotional categories of the scenes and the faces in the images were provided in the present study. Analyses of the data collected from 70 undergraduate students suggested high reliabilities of the valence and arousal ratings of the scenes and high judgmental agreements in categorizing the scene and facial emotions. The findings suggested that the present dataset is well constructed and could be useful for future studies to investigate the emotion recognition or empathy in social interactions in both healthy and clinical (e.g., ASD) populations.
具有单一面部表情或身体姿势的情感图片数据库已广泛应用于情感研究。然而,迄今为止,还没有包含在社会互动场景中涉及多种情感信号的标准化数据库。因此,本研究开发了一个包含 274 张图片的图片集,描绘了两个中国成年人传达快乐、愤怒、悲伤、恐惧、厌恶和中性情绪的互动场景。本研究提供了场景的效价和唤醒评分以及场景和图片中面部的情绪类别数据。对 70 名本科生收集的数据进行的分析表明,场景的效价和唤醒评分具有较高的可靠性,对场景和面部情绪进行分类的判断一致性也较高。研究结果表明,本数据集构建良好,可用于未来的研究,以调查健康人群和临床人群(如自闭症谱系障碍)在社会互动中的情绪识别或同理心。