Behavior Research Methods

, Volume 41, Issue 1, pp 118–127 | Cite as

ATC-labAdvanced: An air traffic control simulator with realism and control

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Abstract

ATC-labAdvanced is a new, publicly available air traffic control (ATC) simulation package that provides both realism and experimental control. ATC-labAdvanced simulations are realistic to the extent that the display features (including aircraft performance) and the manner in which participants interact with the system are similar to those used in an operational environment. Experimental control allows researchers to standardize air traffic scenarios, control levels of realism, and isolate specific ATC tasks. Importantly, ATC-labAdvanced also provides the programming control required to cost effectively adapt simulations to serve different research purposes without the need for technical support. In addition, ATC-labAdvanced includes a package for training participants and mathematical spreadsheets for designing air traffic events. Preliminary studies have demonstrated that ATC-labAdvanced is a flexible tool for applied and basic research.

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References

  1. Ackerman, P. L. (1992). Predicting individual differences in complex skill acquisition: Dynamics of ability determinants. Journal of Applied Psychology, 77, 598–614.CrossRefPubMedGoogle Scholar
  2. Athenes, S., Averty, P., Puechmorel, S., Delahaye, D., & Collet, C. (2002). Complexity and controller workload: Trying to bridge the gap. In Proceedings of the 2002 International Conference on Human-Computer Interaction in Aeronautics (HCI-Aero 2002) (pp. 56–60). Cambridge, MA: MIT.Google Scholar
  3. Berkowitz, L., & Donnerstein, E. (1982). External validity is more than skin deep: Some answers to criticisms of laboratory experiments. American Psychologist, 37, 245–257.CrossRefGoogle Scholar
  4. Bliese, P. D., & Ployhart, R. E. (2002). Growth modeling using random coefficient models: Model building, testing, and illustration. Organizational Research Methods, 5, 362–387.CrossRefGoogle Scholar
  5. Bolland, S., Fothergill, S., & Humphreys, M. (2007). Modelling the human operator: Part II. Emulating controller intervention. In R. Jensen (Ed.), Proceedings of the 14th International Symposium on Aviation Psychology (pp. 57–62). Dayton, OH: Association for Aviation Psychology.Google Scholar
  6. Boring, E. G. (1954). The nature and history of experimental control. American Journal of Psychology, 67, 573–589.CrossRefPubMedGoogle Scholar
  7. Brehmer, B., & Dorner, D. (1993). Experiments with computer-simulated microworlds: Escaping both the narrow straights of the laboratory and the deep blue sea of the field study. Computers in Human Behavior, 9, 171–184.CrossRefGoogle Scholar
  8. Brunswick, E. (1956). Perception and the representative design of psychological experiments. Berkeley: University of California Press.Google Scholar
  9. Callantine, T. J. (2002). CATS-based air traffic controller agents (NASA Technical Memorandum 211856). Moffett Field, CA: NASA Ames Research Center.Google Scholar
  10. Cox, M. (1994). Task analysis of selected operating positions within UK air traffic control (Rep. No. 749). Farnborough, U.K.: DRA/Institute of Aviation Medicine.Google Scholar
  11. DiFonzo, N., Hantula, D. A., & Bordia, P. (1998). Microworlds for experimental research: Having your (control and collection) cake, and realism too. Behavior Research Methods, Instruments, & Computers, 30, 278–286.CrossRefGoogle Scholar
  12. Dismukes, R. K. (2008). Prospective memory in aviation and everyday settings. In M. Kliegel, M. A. McDaniel, & G. O. Einstein (Eds.), Prospective memory: Cognitive, neuroscience, developmental, and applied perspectives (pp. 411–428). Mahwah, NJ: Erlbaum.Google Scholar
  13. Ehret, B. D., Gray, W. D., & Kirschenbaum, S. S. (2000). Contending with complexity: Developing and using a scaled world in applied cognitive research. Human Factors, 42, 8–23.CrossRefPubMedGoogle Scholar
  14. Einstein, G. O., & McDaniel, M. A. (1990). Normal aging and prospective memory. Journal of Experimental Psychology: Learning, Memory, & Cognition, 16, 717–726.Google Scholar
  15. Ericsson, K. A., & Williams, A. M. (2007). Capturing naturally occurring superior performance in the laboratory: Translational research on expert performance. Journal of Experimental Psychology: Applied, 13, 115–123.PubMedGoogle Scholar
  16. Fothergill, S., & Neal, A. (2005). Managing the airspace: A task analysis of Australian air traffic control. Australian Journal of Psychology Supplement, 57, 109–110.Google Scholar
  17. Fothergill, S., & Neal, A. (2006). Decision making in air traffic control: How contextual factors influence conflict resolution choices. Australian Journal of Psychology Supplement, 59, 3.Google Scholar
  18. Fothergill, S., & Neal, A. (2008). An evaluation of the effect of workload on conflict decision making in air traffic control. Australian Journal of Psychology Supplement, 60, 4.Google Scholar
  19. Gray, W. D. (2002). Simulated task environments: The role of highfidelity simulations, scaled worlds, synthetic environments, and microworlds in basic and applied cognitive research. Cognitive Science Quarterly, 2, 205–227.Google Scholar
  20. Gronlund, S. D., Ohrt, D. D., Dougherty, M. R. P., Perry, J. L., & Manning, C. A. (1998). Role of memory in air traffic control. Journal of Experimental Psychology: Applied, 4, 263–280.Google Scholar
  21. Hendy, K. C., Liao, J., & Milgram, P. (1997). Combining time and intensity effects in assessing operator information processing load. Human Factors, 39, 30–47.CrossRefPubMedGoogle Scholar
  22. Jeppesen (2007). TAAM solutions. Retrieved April 8, 2008, from www.preston.net/products/TAAM.htm. Jones, D. G., & Endsley, M. R. (2000). Overcoming representational errors in complex environments. Human Factors, 42, 367-378.Google Scholar
  23. Kopardekar, P., & Magyarits, S. (2003, June). Measurement and prediction of dynamic density. Paper presented at the 5th USA/Europe ATM Research and Development Seminar, Budapest.Google Scholar
  24. Kwantes, P. J., Neal, A., & Loft, S. (2004). Developing a formal model of human memory in a simulated air traffic control conflict detection task. In Proceedings of the Human Factors and Ergonomics Society 48th Annual Meeting (pp. 391–395). Santa Monica, CA: Human Factors & Ergonomics Society.Google Scholar
  25. Lamoureux, T. (1999). The influence of aircraft proximity data on the subjective mental workload of controllers in the air traffic control task. Ergonomics, 42, 1482–1491.CrossRefPubMedGoogle Scholar
  26. Laudeman, I., Shelden, S., Branstrom, R., & Brasil, C. (1998). Dynamic density: An air traffic management metric (NASA-TM-1988-11226). Moffett Field, CA: NASA Ames Research Center.Google Scholar
  27. Loft, S., Bolland, S., & Humphreys, M. (2007). Modelling the human air traffic controller. Expert-trainee differences in conflict detection. In R. Jensen (Ed.), Proceedings of the 14th International Symposium on Aviation Psychology (pp. 409–414). Dayton, OH: Association for Aviation Psychology.Google Scholar
  28. Loft, S., Campbell, L. & Remington, R. W. (2008, March). Failure to deviate from routine and task interference in an air traffic control task. Paper presented at the 34th Australasian Experimental Psychology Conference, Perth, Australia.Google Scholar
  29. Loft, S., Hill, A., Neal, A., Humphreys, M., & Yeo, G. (2004). ATClab: An air traffic control simulator for the laboratory. Behavior Research Methods, Instruments, & Computers, 36, 331–338.CrossRefGoogle Scholar
  30. Loft, S., Humphreys, M., & Neal, A. (2004). The influence of memory for prior instances on performance in a conflict detection task. Journal of Experimental Psychology: Applied, 10, 173–187.PubMedGoogle Scholar
  31. Loft, S., Neal, A., & Humphreys, M. (2007). The development of a general associative learning account of skill acquisition in a conflict detection task. Journal of Experimental Psychology: Human Perception & Performance, 33, 938–959.Google Scholar
  32. Loft, S., Sanderson, P., Neal, A., & Mooij, M. (2007). Modeling and predicting mental workload in en route air traffic control: Critical review and broader implications. Human Factors, 49, 376–399.CrossRefPubMedGoogle Scholar
  33. Metzger, U., & Parasuraman, R. (2001). The role of the air traffic controller in future air traffic management: An empirical study of active control versus passive monitoring. Human Factors, 43, 519–528.CrossRefPubMedGoogle Scholar
  34. Mook, D. G. (1983). In defense of external validity. American Psychologist, 38, 379–387.CrossRefGoogle Scholar
  35. Rantanen, E. M., & Nunes, A. (2005). Hierarchical conflict detection in air traffic control. International Journal of Aviation Psychology, 15, 339–362.CrossRefGoogle Scholar
  36. Raytheon (2005). FIRSTplus tower and radar simulator. Retrieved April 8, 2008, from www.ray.ca/external/home.nsf /(Webpages)/Products_FIRSTplus? OpenDocument.Google Scholar
  37. Remington, R. W., Johnston, J. C., Ruthruff, E., Gold, M., & Romera, M. (2000). Visual search in complex displays: Factors affecting conflict detection by air traffic controllers. Human Factors, 42, 349–366.CrossRefPubMedGoogle Scholar
  38. Rodgers, M. D., & Drechsler, G. K. (1993). Conversion of the CTA Inc, En Route operations concepts database into a formal sentence outline job task taxonomy (FAA Rep. DOT/FAA/AM-93/1). Washington, DC: FAA Office of Aviation Medicine.Google Scholar
  39. Schiff, W., Arnone, W., & Cross, S. (1994). Driving assessment with computer-video scenarios: More is sometimes better. Behavior Research Methods, Instruments, & Computers, 26, 192–194.CrossRefGoogle Scholar
  40. Simon, H. A. (1956). Rational choice and the structure of environments. Psychological Review, 63, 129–138.CrossRefPubMedGoogle Scholar
  41. Späth, O., & Eyferth, K. (2001). Conflict resolution in en route traffic: A draft concept for an assistance system compatible with solutions of air traffic controllers. MMI-Interaktiv, 5, 1–11.Google Scholar
  42. Stone, M., Dismukes, K., & Remington, R. (2001). Prospective memory in dynamic environments: Effects of load, delay, and phonological rehearsal. Memory, 9, 165–176.CrossRefPubMedGoogle Scholar
  43. UFA Inc. (n.d.). The experts in air traffic control simulation: Products. Retrieved on April 8, 2008, from www.atcoach.com/products1.html.Google Scholar
  44. Yeo, G., & Neal, A. (2004). A multilevel analysis of effort, practice and performance: Effects of ability, conscientiousness and goal orientation. Journal of Applied Psychology, 89, 231–247.CrossRefPubMedGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2009

Authors and Affiliations

  1. 1.University of Western AustraliaPerthAustralia
  2. 2.School of PsychologyUniversity of QueenslandBrisbaneAustralia

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