Cognition, Technology & Work

, Volume 6, Issue 2, pp 79–86 | Cite as

Human factors and folk models

  • Sidney Dekker
  • Erik HollnagelEmail author
Original Article


This paper presents a discussion of the susceptibility of human factors to the use of folk models. The case of automation-induced complacency is used as a guiding example to illustrate how folk models (1) substitute one label for another rather than decomposing a large construct into more measurable specifics; (2) are immune to falsification and so resist the most important scientific quality check; and (3) easily get overgeneralised to situations they were never meant to speak about. We then discuss the link between models and measurements, where the model constrains what can be measured by describing what is essential performance, and where the model’s parameters become the basis for specifying the measurements. We propose that one way forward for human factors is to de-emphasize the focus on inferred and uncertain states of the mind, and shift to characteristics of human performance instead.


Model Mind Cognition Human factors Explanation Falsification 


  1. Aeronautica Civil de Colombia (1996) Aircraft accident report: Controlled flight into terrain, American Airlines flight 965, Boeing 757–223, N651AA near Cali, Colombia, 20 December 1995. Aeronautica Civil, Bogota, ColombiaGoogle Scholar
  2. Attneave F (1959) Applications of information theory to psychology: A summary of basic concepts, methods, and results. Holt, Rinehart and Winston, New York, NYGoogle Scholar
  3. Billings CE (1996) Situation awareness measurement and analysis: a commentary. In: Garland DJ, Endsley MR (eds) Experimental analysis and measurement of situation awareness. Embry-Riddle Aeronautical University Press, Daytona Beach, FL, p 3Google Scholar
  4. Buck RN (1995) The pilot’s burden: Flight safety and the roots of pilot error. Iowa State University Press, Ames, IAGoogle Scholar
  5. Campbell RD, Bagshaw M (1991) Human performance and limitations in aviation. Blackwell Science, Oxford, UK, p 126Google Scholar
  6. Endsley MR (1999) Situation awareness in aviation systems. In: Garland DJ, Wise JA, Hopkin VD (eds) Handbook of aviation human factors. Lawrence Erlbaum Associates, Hillsdale, NJ, pp 257–276Google Scholar
  7. Federal Aviation Administration (1996) The interface between flightcrews and modern flight deck systems. Author, Washington DCGoogle Scholar
  8. Hollnagel E, Woods DD (1983) Cognitive systems engineering: New wine in new bottles. Int J Man Mach Stud 18:583–600Google Scholar
  9. Hollnagel E (1993) Human reliability analysis: Context and control. Academic, LondonGoogle Scholar
  10. Hollnagel E (1998a) Context, cognition, and control. In: Waern Y (ed) Co-operation in process management – Cognition and information technology. Taylor and Francis, LondonGoogle Scholar
  11. Hollnagel E (1998b) Measurements and models, models and measurements: You can’t have one without the other. In: NATO RTO Meeting Proceedings 4, Collaborative Crew Performance In Complex Operational Systems, 20–22 April 1998, Edinburgh, Scotland (TRO-MP-4 AC/323(HFM)TP/2)Google Scholar
  12. Kahneman D (1973) Attention and effort. Prentice-Hall, Englewood Cliffs, NJGoogle Scholar
  13. Kern T (1998) Flight Discipline. McGraw-Hill, New York, NY, p 240Google Scholar
  14. Lindsay PH, Norman DA (1977) Human information processing: An introduction to psychology, 2nd edn. Academic, New YorkGoogle Scholar
  15. Morick H (1971) Cartesian privilege and the strictly mental. Philos Phenom Res 31(4):546–551Google Scholar
  16. National Transportation Safety Board (1974) Delta Air Lines Douglas DC-9–31, Boston, MA, 7/31/73 (NTSB/AAR-74/03). NTSB, Washington DCGoogle Scholar
  17. National Transportation Safety Board (1994) Safety study: A review of flightcrew-involved major accidents of U.S. air carriers, 1978 through 1990 (NTSB/SS-94/01). NTSB, Washington DCGoogle Scholar
  18. Norman DA (1976) Memory and attention, 2nd edn. Wiley, New YorkGoogle Scholar
  19. O’Hare D, Roscoe S (1990) Flightdeck performance: The human factor. Iowa State University Press, Ames, IA, p 117Google Scholar
  20. Parasuraman R, Molly R, Singh I (1993) Performance consequences of automation-induced complacency. Int J Aviat Psychol 3(1):3Google Scholar
  21. Popper KR (1972) The logic of scientific discovery. Hutchinson, LondonGoogle Scholar
  22. Sarter NB, Woods DD (1997) Teamplay with a powerful and independent agent: Operational experiences and automation surprises on the Airbus A-320. Hum Factors 39(4):553–569PubMedGoogle Scholar
  23. Stich S (1985) From folk psychology to cognitive science: A case against belief. MIT Press, Cambridge, MAGoogle Scholar
  24. Stokes A, Kite K (1994) Flight stress: Stress, fatigue and performance in aviation. Avebury Aviation, Aldershot, UKGoogle Scholar
  25. Waldman RH (1999) Cockpit discipline. J Professional Aviation Training 1(6):10–15Google Scholar
  26. Weick KE (1995) Sensemaking in organizations. Sage, LondonGoogle Scholar
  27. Wiener EL (1988) Cockpit automation. In: Wiener EL, Nagel DC (eds) Human factors in aviation. Academic, San Diego, CA, p 452Google Scholar
  28. Woods DD (1993) Process-tracing methods for the study of cognition outside of the experimental laboratory. In: Klein GA, Orasanu J, Calderwood R, Zsambok CE (eds) Decision making in action: Models and methods. Ablex, Norwood, NJ, pp 228–251Google Scholar
  29. Yerkes RM, Dodson JD (1908) The relation of strength of stimulus to rapidity of habit-formation. Journal of Comparative and Neurological Psychology 18:459–482Google Scholar

Copyright information

© Springer-Verlag London Limited 2003

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringUniversity of LinköpingLinköpingSweden
  2. 2.Department of Computer and Information ScienceUniversity of LinköpingLinköpingSweden

Personalised recommendations