Mende-Siedlecki Peter, Qu-Lee Jennie, Lin Jingrun, Drain Alexis, Goharzad Azaadeh
University of Delaware, Newark, Delaware, USA.
New York University, New York City, New York, USA.
Pain Rep. 2020 Oct 21;5(6):e853. doi: 10.1097/PR9.0000000000000853. eCollection 2020 Nov-Dec.
Facial expressions of pain serve an essential social function by communicating suffering and soliciting aid. Accurate visual perception of painful expressions is critical because the misperception of pain signals can have serious clinical and social consequences. Therefore, it is essential that researchers have access to high-quality, diverse databases of painful expressions to better understand accuracy and bias in pain perception.
This article describes the development of a large-scale face stimulus database focusing on expressions of pain.
We collected and normed a database of images of models posing painful facial expressions. We also characterized these stimuli in terms of the presence of a series of pain-relevant facial action units. In addition to our primary database of posed expressions, we provide a separate database of computer-rendered expressions of pain that may be applied to any neutral face photograph.
The resulting database comprises 229 unique (and now publicly available) painful expressions. To the best of our knowledge, there are no existing databases of this size, quality, or diversity in terms of race, gender, and expression intensity. We provide evidence for the reliability of expressions and evaluations of pain within these stimuli, as well as a full characterization of this set along dimensions relevant to pain such as perceived status, strength, and dominance. Moreover, our second database complements the primary set in terms of experimental control and precision.
These stimuli will facilitate reproducible research in both experimental and clinical domains into the mechanisms supporting accuracy and bias in pain perception and care.
疼痛的面部表情通过传达痛苦和寻求帮助发挥着重要的社会功能。准确视觉感知痛苦表情至关重要,因为对疼痛信号的错误感知可能会产生严重的临床和社会后果。因此,研究人员能够获取高质量、多样化的痛苦表情数据库对于更好地理解疼痛感知的准确性和偏差至关重要。
本文描述了一个专注于疼痛表情的大规模面部刺激数据库的开发。
我们收集并规范了一组模特摆出痛苦面部表情的图像数据库。我们还根据一系列与疼痛相关的面部动作单元的存在情况对这些刺激进行了特征描述。除了我们的主要摆拍表情数据库外,我们还提供了一个单独的计算机渲染疼痛表情数据库,该数据库可应用于任何中性面部照片。
最终的数据库包含229种独特的(现已公开可用的)痛苦表情。据我们所知,在种族、性别和表情强度方面,不存在如此规模、质量或多样性的现有数据库。我们提供了这些刺激中表情和疼痛评估可靠性的证据,以及沿着与疼痛相关的维度(如感知状态、强度和主导性)对这组表情的全面特征描述。此外,我们的第二个数据库在实验控制和精度方面对主要数据集起到了补充作用。
这些刺激将促进实验和临床领域对支持疼痛感知和护理准确性及偏差机制的可重复研究。