Mental Workload in the Explanation of Automation Effects on ATC Performance

Automation has been introduced more and more into the role of air traffic control (ATC). As with many other areas of human activity, automation has the objective of reducing the complexity of the task so that performance is optimised and safer. However, automation can also have negative effects on cognitive processing and the performance of the controllers. In this paper, we present the progress made at AUTOPACE, a European project in which research is carried out to discover what these negative effects are and to propose measures to mitigate them. The fundamental proposal of the project is to analyse, predict, and mitigate these negative effects by assessing the complexity of ATC in relation to the mental workload experienced by the controller. Hence, a highly complex situation will be one with a high mental workload and a low complex situation will be one in which the mental workload is low.

[1]  Laura E. Reardon,et al.  The Utility of Testing Noncognitive Aptitudes as Additional Predictors of Graduation from U.S. Air Force Air Traffic Controller Training , 2015 .

[2]  Christopher D. Wickens,et al.  Mental Workload: Assessment, Prediction and Consequences , 2017, H-WORKLOAD.

[3]  Mica R. Endsley,et al.  From Here to Autonomy , 2017, Hum. Factors.

[4]  Nora Balfe,et al.  Workload Differences Between On-road and Off-road Manoeuvres for Motorcyclists , 2017, H-WORKLOAD.

[5]  Arnab Majumdar,et al.  A method to estimate air traffic controller mental workload based on traffic clearances , 2014 .

[6]  D. Kahneman,et al.  Attention and Effort , 1973 .

[7]  Raja Parasuraman,et al.  Automation in Future Air Traffic Management: Effects of Decision Aid Reliability on Controller Performance and Mental Workload , 2005, Hum. Factors.

[8]  Raja Parasuraman,et al.  Humans and Automation: Use, Misuse, Disuse, Abuse , 1997, Hum. Factors.

[9]  Erik Hollnagel,et al.  Principles for modelling function allocation , 2000, Int. J. Hum. Comput. Stud..

[10]  S S Stevens,et al.  HUMAN ENGINEERING FOR AN EFFECTIVE AIR-NAVIGATION AND TRAFFIC-CONTROL SYSTEM, AND APPENDIXES 1 THRU 3 , 1951 .

[11]  Luca Longo,et al.  Modeling Mental Workload Via Rule-Based Expert System: A Comparison with NASA-TLX and Workload Profile , 2016, AIAI.

[12]  Jonathan Histon,et al.  Mitigating complexity in Air Traffic Control : the role of structure-based abstractions , 2008 .

[13]  Luca Longo.  Designing Medical Interactive Systems Via Assessment of Human Mental Workload , 2015, 2015 IEEE 28th International Symposium on Computer-Based Medical Systems.

[14]  Christopher D. Wickens,et al.  Multiple resources and performance prediction , 2002 .

[15]  M. Osman.  Controlling uncertainty: a review of human behavior in complex dynamic environments. , 2010, Psychological bulletin.

[16]  Fernand Gobet,et al.  Computational Scientific Discovery , 2017 .

[17]  Tom Kontogiannis,et al.  Cognitive Engineering and Safety Organization in Air Traffic Management , 2017 .

[18]  Luca Longo,et al.  Human Mental Workload: Models and Applications , 2018, Communications in Computer and Information Science.

[19]  Mark S. Young,et al.  Malleable Attentional Resources Theory: A New Explanation for the Effects of Mental Underload on Performance , 2002, Hum. Factors.

[20]  Tamsyn Edwards,et al.  The Relationship between Workload and Performance in Air Traffic Control , 2017, H-WORKLOAD.

[21]  John Sweller,et al.  Cognitive Load During Problem Solving: Effects on Learning , 1988, Cogn. Sci..

[22]  Mica R. Endsley,et al.  Toward a Theory of Situation Awareness in Dynamic Systems , 1995, Hum. Factors.

[23]  C. Trevarthen.  Review: Cognition and Reality: Principles and Implications of Cognitive Psychology , 1977 .

[24]  D. Broadbent.  Levels, Hierarchies, and the Locus of Control* , 1977 .

[25]  Christopher D. Wickens,et al.  Effort in Human Factors Performance and Decision Making , 2014, Hum. Factors.

[26]  Luca Longo,et al.  Mental Workload in Medicine: Foundations, Applications, Open Problems, Challenges and Future Perspectives , 2016, 2016 IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS).

[27]  Shayne Loft,et al.  Modeling and Predicting Mental Workload in En Route Air Traffic Control: Critical Review and Broader Implications , 2007, Hum. Factors.

[28]  P. Frensch,et al.  Complex problem solving : the European perspective , 1995 .

[29]  M R Endsley,et al.  Level of automation effects on performance, situation awareness and workload in a dynamic control task. , 1999, Ergonomics.

[30]  Aidan Byrne,et al.  Mental Workload as an Outcome in Medical Education , 2017, H-WORKLOAD.

[31]  Anson Rabinbach,et al.  The human motor : energy, fatigue, and the origins of modernity , 1992 .

[32]  Fedja Netjasov,et al.  Developing a generic metric of terminal airspace traffic complexity , 2011 .

[33]  Luca Longo,et al.  Estimation of Train Driver Workload: Extracting Taskload Measures from On-Train-Data-Recorders , 2017, H-WORKLOAD.

[34]  D. Dörner,et al.  Complex Problem Solving: What It Is and What It Is Not , 2017, Front. Psychol..

[35]  G. R. J. Hockey,et al.  Applied Attention Theory , 2009 .

[36]  Daniel Gopher,et al.  Workload: An examination of the concept. , 1986 .

[37]  Nicolás Suárez,et al.  Quantifying Air Traffic Controller Mental Workload , 2014 .

[38]  Melanie Mitchell,et al.  Complexity - A Guided Tour , 2009 .

[39]  Peter A. Hancock.  Whither Workload? Mapping a Path for Its Future Development , 2017, H-WORKLOAD.

[40]  Luca Longo,et al.  Human Mental Workload: Models and Applications , 2017, Communications in Computer and Information Science.

[41]  Kai Liu,et al.  Terminal airspace sector capacity estimation method based on the ATC dynamical model , 2016, Kybernetes.