Specialty Grand Challenge Article- Social Neuroergonomics

F. Krueger, E. Wiese
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引用次数: 2

Abstract

Social Neuroergonomics (SNE) is a transdisciplinary field (including psychology, human factors, engineering, social neuroscience) devoted to the application of knowledge of the neurobiological underpinnings of social processes and behaviors (ranging from neural to hormonal to cellular and genetic mechanisms) to the design, engineering, and evaluation of human-machine systems. The tremendous advances that have been accomplished in each of these fields can inspire data-driven hypotheses, foster experimental designs, and increase neuroergonomic theories’ relevance, which is not achievable from only a single-field perspective. Based on a profound understanding of both the social nature of human brains and the principles of human-centered design, SNE has the unique potential to advance our understanding of the psychoneurobiological basis of whether and how humans engage in social interactions with technology (ranging from automated technical systems to autonomous robots) and to use these insights to foster more efficient and satisfying human-machine interactions (HMIs) (e.g., reduction of human errors, increase of trust and productivity, enhancement of safety) in everyday settings (Hancock et al., 2011). Like any emerging research field, SNE will face challenges in the upcoming years that have to be acknowledged, addressed, and resolved. This Special Grand Challenge highlights some of these challenges to ensure the success of SNE as a multi-level integrative field with high relevance and impact on our everyday lives as well as practical implications in diverse sectors such as healthcare, education, therapy, and entertainment (Parasuraman, 2011; Ayaz and Dehais, 2019).
专业大挑战文章-社会神经工效学
社会神经工效学(SNE)是一个跨学科领域(包括心理学、人因、工程学、社会神经科学),致力于将社会过程和行为的神经生物学基础知识(从神经到激素到细胞和遗传机制)应用于人机系统的设计、工程和评估。这些领域取得的巨大进步可以激发数据驱动的假设,促进实验设计,并增加神经人体工程学理论的相关性,这是仅从单一领域的角度无法实现的。基于对人类大脑的社会本质和以人为本的设计原则的深刻理解,SNE具有独特的潜力,可以促进我们对人类是否以及如何与技术(从自动化技术系统到自主机器人)进行社会互动的心理神经生物学基础的理解,并利用这些见解促进更有效和更令人满意的人机交互(hmi)(例如,减少人为错误,在日常环境中增加信任和生产力,增强安全性)(Hancock et al., 2011)。与任何新兴研究领域一样,SNE在未来几年将面临挑战,必须承认、应对和解决这些挑战。这个特别的大挑战突出了其中的一些挑战,以确保SNE作为一个多层次的综合领域的成功,对我们的日常生活具有高度的相关性和影响,并在医疗保健、教育、治疗和娱乐等不同部门具有实际意义(Parasuraman, 2011;Ayaz and Dehais, 2019)。
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