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Virtual Environments
Published in Julie A. Jacko, The Human–Computer Interaction Handbook, 2012
Kay M. Stanney, Joseph V. Cohn
Augmented cognition is an emerging computing paradigm in which users and computers are tightly coupled via physiological gauges that measure the cognitive state of users and adapt interaction to optimize human performance (Stanney et al. 2009). If incorporated into VE applications, augmented cognition could provide a means of evaluating their engaging and compelling nature. For example, neuroscience studies have established that differential aspects of the brain are engaged when learning different types of materials, and the areas in the brain that are activated change with increasing competence (Carroll et al. 2010; Kennedy et al. 2005). Thus, if VE users were immersed in an educational experience, augmented cognition technology could be used to gauge if targeted areas of the brain were being activated and dynamically modify the content of a VE learning curriculum if desired activation patterns were not being generated. Physiological measures could also be used to detect the onset of cybersickness (see Section 28.4.1) and to assess the engagement, awareness, and anxiety of VE users, thereby potentially providing much more robust measures of immersion and presence (see Section 28.5.2). Such techniques could prove invaluable to entertainment VE applications (cf., Badiqué et al. [2002]) that seek to provide the ultimate experience, military training VE applications (cf., Knerr et al. [2002]) that seek to emulate the “violence of action” found during combat, medical training applications (Wiecha et al. 2010) that seek to enhance traditional laboratory-based and classroom training practices, and therapeutic VE applications (cf., North, North, and Coble 2002; Strickland et al. [1997]) that seek to overcome disorders such as fear of heights or flying.
Towards augmenting cyber-physical-human collaborative cognition for human-automation interaction in complex manufacturing and operational environments
Published in International Journal of Production Research, 2020
Jianxin (Roger) Jiao, Feng Zhou, Nagi Z. Gebraeel, Vincent Duffy
Augmenting human cognition has attracted much attention over the last decade, focusing on computational systems that sense, infer, and take action based on detailed knowledge of the capabilities and limitations of human cognition (Horvitz 2018). The major focus of DARPA’s augmented cognition programme has been developing more robust tools for monitoring cognitive states and integrating them with automation systems (Schmorrow and Kruse 2002). Augmented cognition is a form of human-system interaction in which a tight coupling between human and the system is achieved via physiological and neurophysiological sensing of a worker's cognitive state (Barker et al. 2004; Siddhartha and Dagli 2013). This interactive paradigm seeks to revolutionise the manner, in which human engages with automation by leveraging this knowledge of cognitive states to precisely adapt human-system interaction in real time (Diethe 2005). Augmented cognition enables the human to gain conception for adaptation to his particular needs and to derive explanations (Engelbart 2001). There are three main components of an augmented cognition system: cognitive state sensors, adaptation strategies, and control systems (Stanney et al. 2009). Augmented cognition research generally focuses on tasks and environments to develop applications that capture the human’s cognitive state in order to drive real-time automation systems (Reeves, Schmorrow, and Stanney 2007). In doing so, these systems are able to provide operational data specifically targeted for human in a given context (Schmorrow and Kruse 2002). There are generally three major areas of research in the field: cognitive state assessment, mitigation strategies, and robust controllers, in order to enhance the ability of a team to remember, think, and reason (Schmorrow, Estabrooke, and Grootjen 2009).