Man–machine Integration Design and Analysis System (MIDAS) v5: Augmentations, Motivations, and Directions for Aeronautics Applications

As automation and advanced technologies are introduced into transport systems ranging from the Next Generation Air Transportation System termed NextGen, to the advanced surface vehicle Intelligent Transportations Systems, to future systems designed for space exploration, there is an increased need to validly predict how the future systems will be vulnerable to error given the demands imposed by assisted technologies. One formalized method to study the impact of assisted technologies on the human operator in a safe and non-obtrusive manner is through the use of human performance models (HPMs). HPMs play an integral role when complex human–system designs are proposed, developed, and tested. One HPM tool termed the Man–machine Integration Design and Analysis System (MIDAS) is a NASA Ames Research Center HPM software tool that has been applied to predict human–system performance in various domains since 1986. MIDAS is a dynamic, integrated HPM environment that facilitates the design, visualization, and computational evaluation of complex man–machine system concepts in simulated operational environments. A range of aviation specific applications including an approach used to model human error for NASA’s Aviation Safety Program, and “what-if” analyses to evaluate flight deck technologies for NextGen operations will be discussed. This chapter will culminate by raising two challenges for the field of predictive HPMs for complex human–system designs that evaluate assisted technologies: that of (1) model transparency and (2) model validation.

[1]  Brian F Gore Human Performance Cognitive-Behavioral Modeling: A Benefit for Occupational Safety , 2002, International journal of occupational safety and ergonomics : JOSE.

[2]  Savita Verma,et al.  PRELIMINARY GUIDELINES ON FLIGHT DECK PROCEDURES FOR VERY CLOSELY SPACED PARALLEL APPROACHES , 2008 .

[3]  Peter A. Jarvis,et al.  New Integrated Modeling Capabilities: MIDAS’ Recent Behavioral Enhancements , 2005 .

[4]  Christopher D. Wickens,et al.  Attention-Situation Awareness (A-SA) Model of Pilot Error , 2007 .

[5]  Robert G. Sargent Validation of simulation models , 1979, WSC '79.

[6]  Jeffrey D. Smith,et al.  Risk assessment and human performance modelling: the need for an integrated systems approach , 2006 .

[7]  Vaishnav The need for an integrated approach , 1994 .

[8]  Christopher D. Wickens,et al.  Attentional Models of Multitask Pilot Performance Using Advanced Display Technology , 2003, Hum. Factors.

[9]  Brian F. Gore,et al.  Meeting the Challenge of Cognitive Human Performance Model Interpretability Through Transparency: MIDAS v5.x , 2008 .

[10]  J. Shaoul Human Error , 1973, Nature.

[11]  Beverly M. Huey,et al.  Human Performance Models for Computer-Aided Engineering , 1990 .

[12]  Brian F. Gore,et al.  A Computational Implementation of a Human Attention Guiding Mechanism in MIDAS v5 , 2009, HCI.

[13]  Karen A. Harper,et al.  Towards a Common Ontology for Improved Traceability of Human Behavior Models , 2004 .

[14]  Brian F. Gore,et al.  Modeling Pilot Situation Awareness , 2011 .

[15]  Becky L. Hooey,et al.  Advancing the State of the Art of Human Performance Models to Improve Aviation Safety , 2007 .

[16]  M. D’Esposito Working memory. , 2008, Handbook of clinical neurology.

[17]  Gavriel Salvendy,et al.  Handbook of Human Factors and Ergonomics , 2005 .

[18]  Christopher D. Wickens,et al.  Identifying Black Swans in NextGen: Predicting Human Performance in Off-Nominal Conditions , 2009, Hum. Factors.

[19]  C. Wickens,et al.  Applied Attention Theory , 2007 .

[20]  K. J. Craik THEORY OF THE HUMAN OPERATOR IN CONTROL SYSTEMS , 1948 .

[21]  Sher ry Folsom-Meek,et al.  Human Performance , 2020, Nature.

[22]  Brian Gore Human Performance: Evaluating the Cognitive Aspects , 2008 .

[23]  Erik Hollnagel,et al.  Human Reliability Analysis: Context and Control , 1994 .

[24]  J H McCracken,et al.  Analyses of Selected LHX Mission Functions: Implications for Operator Workload and System Automation Goals , 1984 .

[25]  Erik Hollnagel,et al.  Joint Cognitive Systems: Foundations of Cognitive Systems Engineering , 2005 .

[26]  Kevin A. Gluck,et al.  Modeling Human Behavior With Integrated Cognitive Architectures : Comparison, Evaluation, and Validation , 2006 .

[27]  Becky L. Hooey,et al.  Human performance modeling in aviation , 2007 .

[28]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[29]  D. Norman Categorization of action slips. , 1981 .

[30]  Diane K Mitchell,et al.  Mental Workload and ARL Workload Modeling Tools. , 2000 .

[31]  Anthony D. Andre,et al.  Situation Awareness in an Augmented Reality Cockpit: Design, Viewpoints and Cognitive Glue , 2005 .

[32]  Brian F. Gore,et al.  The Study of Distributed Cognition in Free Flight: A Human Performance Modeling Tool Structural Comparison , 2000 .

[33]  Sherman W. Tyler Man-machine integration design and analysis system (MIDAS) , 1994, CHI '94.

[34]  Erik Hollnagel Modelling the orderliness of human action , 2000 .

[35]  Jerry Banks,et al.  Handbook of simulation - principles, methodology, advances, applications, and practice , 1998, A Wiley-Interscience publication.

[36]  K. J. W. Craik Theory of the human operator in control systems; the operator as an engineering system. , 1947 .

[37]  D. Woods,et al.  Automation Surprises , 2001 .