Advertisement

ATC-lab: An air traffic control simulator for the laboratory

  • Shayne LoftEmail author
  • Andrew Hill
  • Andrew Neal
  • Michael Humphreys
  • Gillian Yeo
Article

Abstract

Air Traffic Control Laboratory Simulator (ATC-lab) is a new low- and medium-fidelity task environment that simulates air traffic control. ATC-lab allows the researcher to study human performance of tasks under tightly controlled experimental conditions in a dynamic, spatial environment. The researcher can create standardized air traffic scenarios by manipulating a wide variety of parameters. These include temporal and spatial variables. There are two main versions of ATC-lab. The medium-fidelity simulator provides a simplified version of en route air traffic control, requiring participants to visually search a screen and both recognize and resolve conflicts so that adequate separation is maintained between all aircraft. The low-fidelity simulator presents pairs of aircraft in isolation, controlling the participant’s focus of attention, which provides a more systematic measurement of conflict recognition and resolution performance. Preliminary studies have demonstrated that ATC-lab is a flexible tool for applied cognition research.

Keywords

Flight Path Conflict Detection Option Menu Human Factor Research Script Developer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Ackerman, P. L. (1992). Predicting individual differences in complex skill acquisition: Dynamics of ability determinants.Journal of Applied Psychology,77,598–614.PubMedCrossRefGoogle Scholar
  2. Brehmer, B., &Dorner, D. (1993). Experiments with micro-simulated worlds: Escaping both the narrow straights of the laboratory and the deep blue see of the field study.Computers in Human Behavior,9,171–184.CrossRefGoogle Scholar
  3. 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
  4. Gray, W. D. (2002). Simulated task environments: The role of high-fidelity simulations, scaled worlds, synthetic environments, and micro-worlds in basic and applied cognitive research.Cognitive Science Quarterly,2, 205–227.Google Scholar
  5. Hendy, K. C., Liao, J., &Milgram, P. (1997). Combining time and intensity effects in assessing operator information-processing load.Human Factors,39,30–47.PubMedCrossRefGoogle Scholar
  6. Jones, D. G., &Endsley, M. R. (2000). Overcoming representational errors in complex environments.Human Factors,42,367–378.PubMedCrossRefGoogle Scholar
  7. Loft, S., Humphreys, M., & Neal, A. (in press). The influence of memory for prior instances on performance in a conflict detection task.Journal of Experimental Psychology: Applied.Google Scholar
  8. Loft, S., Neal, A., & Humphreys, M. (2002).Learning and transfer in an applied visual spatial task. Paper presented at the HF2002 Human Factors Conference, Melbourne.Google Scholar
  9. Metzger, U., &Parasuraman, R. (2001). The role of the air traffic controller in future air traffic management.Human Factors,43,519–528.PubMedCrossRefGoogle Scholar
  10. Neal, A., Kwantes, P., & Loft, S. (2002, November).Development of a model for prediction of conflict detection performance in a simulated air traffic control task. Paper presented at the meeting of the Defence Human Factors Special Interest Group (DHFSIG), Melbourne.Google Scholar
  11. Omodei, M. M., &Wearing, A. J. (1995). The Fire Chief microworld generating program: An illustration of computer-based microworlds as an experimental paradigm for studying complex decision-making behavior.Behavior Research Methods, Instruments, & Computers,27,303–316.CrossRefGoogle Scholar
  12. Ratcliff, R., Van Zandt, T., &McKoon, G. (1999). Connectionist and diffusion models of reaction time.Psychological Review,106,261–300.PubMedCrossRefGoogle Scholar
  13. Remington, R.W., Johnston, J. C., Ruthruff, E., Gold, M., &Romera, M. (2000). Visual search in complex displays: Factors affecting conflict recognition by air traffic controllers.Human Factors,42,349–368.PubMedCrossRefGoogle Scholar
  14. Stanislow, H., &Todorov, N. (1999). Calculation of signal detection theory measures.Behavior Research Methods, Instruments, & Computers,31,137–149CrossRefGoogle Scholar
  15. Stone, M., Dismukes, K., &Remington, R. (2001). Prospective memory in dynamic environments: Effects of load, delay, and phonological rehearsal.Memory,9,165–176.PubMedCrossRefGoogle Scholar
  16. Wickens, C. D., Mavor, A. S., &McGee, J.P. (1997).Flight to the future: Human factors in air traffic control. Washington, DC: National Academy Press.Google Scholar
  17. Yeo, G., &Neal, A. (2004). Multilevel analysis of effort, practice and performance: Effects of ability, conscientiousness and goal orientation.Journal of Applied Psychology,89,231–247.PubMedCrossRefGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2004

Authors and Affiliations

  • Shayne Loft
    • 1
    Email author
  • Andrew Hill
    • 1
  • Andrew Neal
    • 1
  • Michael Humphreys
    • 1
  • Gillian Yeo
    • 1
  1. 1.School of PsychologyUniversity of QueenslandBrisbaneSt. LuciaAustralia

Personalised recommendations