Social Cognitive and Affective Neuroscience in Human-Machine Systems: A Roadmap for Improving Training, Human-Robot Interaction, and Team Performance
Abbreviated Journal Title
IEEE T. Hum.-Mach. Syst.
Accelerated learning; human-robot interaction; joint action; social; cognition; social cognitive and affective neuroscience; team performance; BRAIN; EEG; EMOTION; MIND; PERSPECTIVE; MECHANISMS; EDUCATION; Computer Science, Artificial Intelligence; Computer Science, Cybernetics
This paper augments recent advances in social cognitive and affective neuroscience (SCAN) and illustrates their relevance to the development of novel human-machine systems. Advances in this area are crucial for understanding and exploring the social, cognitive, and neural processes that arise during human interactions with complex sociotechnological systems. Overviews of the major areas of SCAN research, including emotion, theory of mind, and joint action, are provided as the basis for describing three applications of SCAN to human-machine systems research and development. Specifically, this paper provides three examples to demonstrate the broad interdisciplinary applicability of SCAN and the ways it can contribute to improving a number of human-machine systems with the pursuit of further research in this vein. These include applying SCAN to learning and training, informing the field of human-robot interaction (HRI), and, finally, for enhancing team performance. The goal is to draw attention to the insights that can be gained by integrating SCAN with ongoing human-machine system research and to provide guidance to foster collaborations of this nature. Toward this end, we provide a systematic set of notional research questions for each detailed application within the context of the three major emphases of SCAN research. In turn, this study serves as a roadmap for preliminary investigations that integrate SCAN and human-machine system research.
Ieee Transactions on Human-Machine Systems
"Social Cognitive and Affective Neuroscience in Human-Machine Systems: A Roadmap for Improving Training, Human-Robot Interaction, and Team Performance" (2014). Faculty Bibliography 2010s. 6289.