Advertisement

Summary: Putting It All Together

  • Frank E. RitterEmail author
  • Gordon D. Baxter
  • Elizabeth F. Churchill
Chapter

Abstract

This chapter recaps some of the many things that you have learned about users in terms of their anthropometric, behavioral, cognitive, and social aspects. You have been provided with a lot of information, so we describe a number of different possible ways you can organize it. One way to organize and apply the information is with user models. These models span the range from implicit descriptive models, such as guidelines, through to explicit information processing models, which can be executed to produce behavior and predict performance. Another way is to organize the information based on how to use it. So we finish by looking at one system development process model—the Risk-Driven Incremental Commitment Model—as an example of how you can integrate knowledge about users into the system development life cycle. Failure to consider the users and their tasks during development leads to increased system development risk.

Keywords

System Development User Model Cognitive Architecture Unmanned Aerial System Shared Representation 
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. Anderson, J. R. (2007). How can the human mind exist in the physical universe? New York, NY: Oxford University Press.Google Scholar
  2. Barnard P. J. (1987). Cognitive resources and the learning of human-computer dialogues. In J. M. Carroll (Ed.), Interfacing thought: Cognitive aspects of human–computer interaction (pp. 112–158). Cambridge, MA: MIT Press.Google Scholar
  3. Baxter, G., Besnard, D., & Riley, D. (2007). Cognitive mismatches in the cockpit: Will they ever be a thing of the past? Applied Ergonomics, 38, 417–423.CrossRefGoogle Scholar
  4. Boehm, B. (2008). Making a difference in the software century. IEEE Computer, 41(3), 32–38.CrossRefGoogle Scholar
  5. Boehm, B., & Hansen, W. (2001). The spiral model as a tool for evolutionary acquisition. Crosstalk: The Journal of Defense Software Engineering, 14(5), 4–11.Google Scholar
  6. Boehm, B., & Lane, J. (2006). 21st century processes for acquiring 21st century systems of systems. Crosstalk, 19(5), 4–9.Google Scholar
  7. Booher, H. R., & Minninger, J. (2003). Human systems integration in Army systems acquisition. In H. R. Booher (Ed.), Handbook of human systems integration (pp. 663–698). Hoboken, NJ: John Wiley.CrossRefGoogle Scholar
  8. Byrne, M. D. (2001). ACT-R/PM and menu selection: Applying a cognitive architecture to HCI. International Journal of Human–Computer Studies, 55(1), 41–84.CrossRefzbMATHGoogle Scholar
  9. Byrne, M. D., & Kirlik, A. (2005). Using computational cognitive modeling to diagnose possible sources of aviation error. International Journal of Aviation Psychology, 15(2), 135–155.CrossRefGoogle Scholar
  10. Card, S. K., Moran, T. P., & Newell, A. (1980). The keystroke-level model for user performance time with interactive systems. Communications of the ACM, 23(7), 396–410.CrossRefGoogle Scholar
  11. Card, S. K., Moran, T., & Newell, A. (1983). The psychology of human–computer interaction. Hillsdale, NJ: Erlbaum.Google Scholar
  12. Casey, S. M. (1998). Set phasers on stun: And other true tales of design, technology, and human error. Santa Barbara, CA: Aegean.Google Scholar
  13. Casey, S. M. (2006). The atomic chef: And other true tales of design, technology, and human error. Santa Barbara, CA: Aegean.Google Scholar
  14. Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis: Verbal reports as data (2nd ed.). Cambridge, MA: MIT Press.Google Scholar
  15. Gemma, E., Helm, R., Johnson, R., & Vlissides, J. (1995). Design patterns: Elements of reusable object-oriented software. Boston, MA: Addison-Wesley.Google Scholar
  16. Gluck, K. A., Ball, J. T., & Krusmark, M. A. (2007). Cognitive control in a computational model of the predator pilot. In W. D. Gray (Ed.), Integrated models of cognitive systems (pp. 13–28). New York: Oxford University Press.Google Scholar
  17. Ivory, M. Y., & Hearst, M. A. (2001). The state of the art in automating usability evaluation of user interfaces. ACM Computing Surveys, 33(4), 470–516.CrossRefGoogle Scholar
  18. Kieras, D. E. (1999). A guide to GOMS model usability evaluation using GOMSL and GLEAN3. AI Lab, University of Michigan. Retrieved 10 March 2014 from http://web.eecs.umich.edu/~kieras/docs/GOMS
  19. Kieras, D. E., Wood, S. D., Abotel, K., & Hornof, A. (1995). GLEAN: A computer-based tool for rapid GOMS model usability evaluation of user interface designs. In Proceedings of the ACM Symposium on User Interface Software and Technology (UIST’95) (pp. 91–100). New York, NY: ACM.Google Scholar
  20. Kieras, D. E., Wood, S. D., & Meyer, D. E. (1997). Predictive engineering models based on the EPIC architecture for a multimodal high-performance human-computer interaction task. Transactions on Computer–Human Interaction, 4(3), 230–275.CrossRefGoogle Scholar
  21. Laird, J. E. (2012). The soar cognitive architecture. Cambridge, MA: MIT Press.Google Scholar
  22. Landauer, T. K. (1987). Relations between cognitive psychology and computer systems design. In J. Preece & L. Keller (Eds.), Human–computer interaction (pp. 141–159). Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  23. Lebiere, C., & Anderson, J. R. (1998). Cognitive arithmetic. In J. R. Anderson & C. Lebière (Eds.), The atomic components of thought (pp. 297–342). Mahwah, NJ: Erlbaum.Google Scholar
  24. Morrison, J. E. (2003). A review of computer-based human behavior representations and their relation to military simulations (IDA Paper P-3845). Alexandria, VA: Institute for Defense Analyses.Google Scholar
  25. Newell, A. (1990). Unified theories of cognition. Cambridge, MA: Harvard University Press.Google Scholar
  26. Pew, R. W. (2007). Some history of human performance modeling. In W. Gray (Ed.), Integrated models of cognitive systems (pp. 29–44). New York, NY: Oxford University Press.CrossRefGoogle Scholar
  27. Pew, R. W., & Mavor, A. S. (Eds.). (1998). Modeling human and organizational behavior: Application to military simulations. Washington, DC: National Academies Press. Retrieved from 10 March 2014 http://books.nap.edu/catalog/6173.html
  28. Pew, R. W., & Mavor, A. S. (Eds.). (2007). Human-system integration in the system development process: A new look. Washington, DC: National Academies Press. Retrieved March, 2014 from http://books.nap.edu/catalog.php?record_id=11893
  29. Ritter, F. E. (2003). Soar. In L. Nadel (Ed.), Encyclopedia of cognitive science (Vol. 4, pp. 60–65). London: Nature Publishing Group.Google Scholar
  30. Ritter, F. E., & Bibby, P. A. (2008). Modeling how, when, and what learning happens in a diagrammatic reasoning task. Cognitive Science, 32, 862–892.CrossRefGoogle Scholar
  31. Ritter, F. E., Shadbolt, N. R., Elliman, D., Young, R. M., Gobet, F., & Baxter, G. D. (2003). Techniques for modeling human performance in synthetic environments: A supplementary review. Wright-Patterson Air Force Base, OH: Human Systems Information Analysis Center (HSIAC).Google Scholar
  32. Ritter, F. E., Van Rooy, D., St. Amant, R., & Simpson, K. (2006). Providing user models direct access to interfaces: An exploratory study of a simple interface with implications for HRI and HCI. IEEE Transactions on System, Man, and Cybernetics, Part A: Systems and Humans, 36(3), 592–601.Google Scholar
  33. Ritter, F. E., Kukreja, U., & St. Amant, R. (2007). Including a model of visual processing with a cognitive architecture to model a simple teleoperation task. Journal of Cognitive Engineering and Decision Making, 1(2), 121–147.Google Scholar
  34. Salvucci, D. D. (2006). Modeling driver behavior in a cognitive architecture. Human Factors, 48, 362–380.CrossRefGoogle Scholar
  35. Salvucci, D. D., Zuber, M., Beregovaia, E., & Markley, D. (2005). Distract-R: Rapid prototyping and evaluation of in-vehicle interfaces. In Human Factors in Computing Systems: CHI 2005 Conference Proceedings (pp. 581–589). New York, NY: ACM Press.Google Scholar
  36. Schoelles, M. J., & Gray, W. D. (2001). Argus: A suite of tools for research in complex cognition. Behavior Research Methods, Instruments, & Computers, 33(2), 130–140.CrossRefGoogle Scholar
  37. Steele, M., Dow, L., & Baxter, G. (2011). Promoting public awareness of the links between lifestyle and cancer: A controlled study of the usability of health information leaflets. International Journal of Medical Informatics, 80, e214–e229.CrossRefGoogle Scholar
  38. Thorpe, T. W. (1992). Computerized circuit analysis with SPICE: A complete guide to SPICE, with applications. New York, NY: Wiley.Google Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Frank E. Ritter
    • 1
    Email author
  • Gordon D. Baxter
    • 2
  • Elizabeth F. Churchill
    • 3
  1. 1.College of ISTThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.School of Computer ScienceUniversity of St AndrewsSt AndrewsUK
  3. 3.eBay Research LabsSan JoseUSA

Personalised recommendations