Course Contents This course focuses on the various aspects of actual and future flight deck and air traffic control human-machine interfaces. It provides an extensive theoretical as well as practical knowledge on the specific characteristics of human behavior, such as human control behavior (i.e., cybernetics), human perception (visual & haptic), human mental processing, cognitive factors, and human-automation interaction in manual and supervisory control tasks. Study Goals Overall, the student will have a working knowledge of human operator (pilot) characteristics that are relevant for the design and evaluation of human-machine systems. Specific study goals: The student: * Is able to classify different types of human behaviour according to Rasmussen's rule-skill and knowledge taxonomy * Is able to predict performance and human behaviour in manual control tasks, using McRuer's cross-over theory, and is able to reason on the effect of task variables (motion, haptics, etc.) * Knows the physiology and characteristics of human sensory systems and actuation processes (visual, vestibular, propioceptive senses and neuromuscular system) as relevant for human behaviour, and is able to predict the implications of these properties for human perception and behaviour, and relate this to design choices for displays and manipulators. * can describe the pitfalls of automation (i.e., ironies of automation) and is familiar with various taxonomies on levels & stages of automation. * can analyse accident and incident reports, find latent and active errors and classify these with Rasmussen's SRK taxonomy and identify Reason's error shaping factor. The student understands the wider context of human error (Dekker's "new view"). * is familiar with workload and situation awareness, and knows which methods are used to measure these properties * understands the nature of human cognition, can distinguish between different views and models of cognition and knows when these are applicable * can describe the differences and similarities between interface design approaches that aim to support both system and human (control & cognitive) performances * is familiar with workload theory, knows different metrics for workload, and when these are applicable
