Cognition, Technology & Work

, Volume 18, Issue 3, pp 511–528 | Cite as

SRK as a framework for the development of training for effective interaction with multi-level automation

  • Elizabeth FlemingEmail author
  • Amy Pritchett
Original Article


This paper examines the development of training for effective interaction with multi-level automation, i.e., a system that switches between functions and roles corresponding to different levels of automation. These interactions need to span not only the nominal procedures and skills expected of the operator, but also effective reasoning about when and whether the automation should be employed. This suggests framing the operator’s tasks using Rasmussen’s classical categorization of human behavior as skill based, rule based, or knowledge based (SRK), thereby providing appropriate insights into appropriate training objectives and methods. This paper uses the aircraft traffic alert and collision avoidance system as a case study, demonstrating the application of the SRK framework to develop pilot training. Comparison of pilot behavior with and without this modified training highlights the training’s ability to improve interaction with the automation, leading to recommendations for broader application of the SRK framework in training development.


Training Human–automation interaction TCAS Skill-, rule-, and knowledge-based behavior (SRK) 



The authors are grateful for the time of the pilots who participated in these studies. Additionally, the authors thank Jonathan Zoetrum, William Cleveland, Jelle Wissink, Dhruv Thakkar, Thomas Legrande, Vlad Popescu, Henry Tran, Jack Ridderhof, Alyssa Whitlock, Rachel Haga, and Justin Mullins for their assistance in preparing and running the flight simulator studies and in data analysis. The authors also gratefully acknowledge the time and expertise provided by Wes Olson of MIT Lincoln Labs and Tom McCloy of the FAA. This work was supported in part by a Cooperative Agreement (DTFAWA-10-C-00084) with the Federal Aviation Administration (FAA) Human Factors Research and Engineering Group. Since completing this study and during preparation of this manuscript, the first author has been supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-0644493.


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Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  1. 1.School of Aerospace EngineeringGeorgia Institute of TechnologyAtlantaUSA

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