Renier Laetitia Aurelie, Schmid Mast Marianne, Dael Nele, Kleinlogel Emmanuelle Patricia
Department of Organizational Behavior - Faculty of Business and Economics (HEC), University of Lausanne, Lausanne, Switzerland.
Front Psychol. 2021 Jul 27;12:606548. doi: 10.3389/fpsyg.2021.606548. eCollection 2021.
The study of nonverbal behavior (NVB), and in particular kinesics (i.e., face and body motions), is typically seen as cost-intensive. However, the development of new technologies (e.g., ubiquitous sensing, computer vision, and algorithms) and approaches to study social behavior [i.e., social signal processing (SSP)] makes it possible to train algorithms to automatically code NVB, from action/motion units to inferences. Nonverbal social sensing refers to the use of these technologies and approaches for the study of kinesics based on video recordings. Nonverbal social sensing appears as an inspiring and encouraging approach to study NVB at reduced costs, making it a more attractive research field. However, does this promise hold? After presenting what nonverbal social sensing is and can do, we discussed the key challenges that researchers face when using nonverbal social sensing on video data. Although nonverbal social sensing is a promising tool, researchers need to be aware of the fact that algorithms might be as biased as humans when extracting NVB or that the automated NVB coding might remain context-dependent. We provided study examples to discuss these challenges and point to potential solutions.
对非语言行为(NVB)的研究,尤其是身势学(即面部和身体动作)的研究,通常被认为成本高昂。然而,新技术(如普适传感、计算机视觉和算法)以及研究社会行为的方法(即社会信号处理(SSP))的发展,使得训练算法自动对非语言行为进行编码成为可能,从动作/运动单元到推理。非语言社会传感是指利用这些技术和方法,基于视频记录来研究身势学。非语言社会传感似乎是一种以降低成本来研究非语言行为的鼓舞人心且令人振奋的方法,使其成为一个更具吸引力的研究领域。然而,这一承诺能否兑现呢?在介绍了非语言社会传感是什么以及能做什么之后,我们讨论了研究人员在对视频数据使用非语言社会传感时所面临的关键挑战。尽管非语言社会传感是一种很有前景的工具,但研究人员需要意识到,在提取非语言行为时算法可能会像人类一样存在偏差,或者自动的非语言行为编码可能仍然依赖于上下文。我们提供了研究实例来讨论这些挑战并指出潜在的解决方案。