Micro and Macro Predictions: Using SGOMS to Predict Phone App Game Playing and Emergency Operations Centre Responses

  • Robert WestEmail author
  • Lawrence Ward
  • Kate Dudzik
  • Nathan Nagy
  • Fraydon Karimi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10906)


In this study, we examine the ability of SGOMS models to predict human behaviour on two different scales, in micro cognitive task performance and in high level problem solving roles to better understand strategy use and training. To do this, two experiments were designed to isolate the role of knowledge structures in task performance. The first experiment involves modelling an application-based game, played on mobile phones. Results were compared to two models: the SGOMS model that matched the knowledge structures the players had learned during training, and a model optimized for speed, resulting in the fastest game play possible using ACT-R. In the second experiment we examined SGOMS predictions in a high level problem space of an Emergency Operations Center (EOC) simulation, with many interruptions and communication demands, comparing professional EOC managers and undergraduate performance. By comparing results between tasks, HCI design can be augmented using predictive modeling to inform the design to produce efficient and effective training programs.


HCI Training SGOMS ACT-R App 


  1. Anderson, J.R., Lebiere, C.: The Atomic Components of Thought. Erlbaum, Mahwah (1998)Google Scholar
  2. Cacciabue, P.C., Hollnagel, E.: Simulation of cognition: applications. In: Hoc, J.M., Cacciabue, P., Hollnagel, E. (Eds.), Expertise and Technology: Issues in Cognition and Human– Computer Cooperation, pp. 55–74. NEA, Hillsdale (1995)Google Scholar
  3. Card, S., Moran, T., Newell, A.: The Psychology of Human– Computer Interaction. Lawrence Erlbaum Associates, Hillsdale (1983)Google Scholar
  4. Cooper, R.P.: The role of falsification in the development of cognitive architectures: Insights from a lakatosian analysis. Cogn. Sci. 31(2007), 509–533 (2007)CrossRefGoogle Scholar
  5. Emergency Operating Center: Wikipedia online (n.d.).
  6. Ericsson, K.A., Charness, N., Hoffman, R.R., Feltovich, P.J. (eds.): The Cambridge Handbook of Expertise and Expert Performance. Cambridge University Press, New York (2006)Google Scholar
  7. Federal Emergency Management Agency, Department of Homeland Security: Exercise Simulation System Document [ESSD] (2014).
  8. Gray, W.D., Boehm-davis, D.A.: Milliseconds matter: an introduction to microstrategies and to their use in describing and predicting interactive behavior. J. Exp. Psychol. 6(4), 322–335 (2000)Google Scholar
  9. Gray, W.D., John, B.E., Atwood, M.E.: Project ernestine: validating a GOMS analysis for predicting and explaining real-world task performance. Hum. Comput. Interact. 8(3), 237–309 (1993)CrossRefGoogle Scholar
  10. Gregson, R.A.M.: Nonlinear Psychophysical Dynamics. Erlbaum Associates, Hillsdale (1988)Google Scholar
  11. John, B.E., Kieras, D.E.: The GOMS family of user interface analysis techniques: comparison and contrast. ACM Trans. Comput. Hum. Interact. 3(4), 320–351 (1996)CrossRefGoogle Scholar
  12. Kieras, D.E., Meyer, D.E.: An overview of the EPIC architecture for cognition and performance with application to human-computer interaction. J. Hum. Comput. Interact. 12(4), 391–438 (1997)CrossRefGoogle Scholar
  13. Kieras, D., Santoro, P.: Computational GOMS modeling of a complex team task: lessons learned. In: Proceedings of HCI, 24–29 Apr 2004, Vienna, Austria, pp. 97–104 (2004)Google Scholar
  14. Kingstone, A., Smilek, D., Ristic, J., Friesen, C.K., John, D., Eastwood, J.D.: Attention, researchers! It is time to take a look at the real world. Curr. Dir. Psychol. Sci. 12, 176 (2003)CrossRefGoogle Scholar
  15. Kirlik, A.: The emerging toolbox of cognitive engineering models. In: Paper presented at the International Conference on Cognitive Modeling, Berlin, Germany, 13–15 Apr 2012Google Scholar
  16. Klein, G., Ross, K.G., Moon, B.M., Klein, D.E., Hoffman, R.R., Hollnagel, E.: Macrocognition. IEEE Intell. Syst. 18(3), 81–85 (2003)CrossRefGoogle Scholar
  17. Klein, G., Woods, D.D., Bradshaw, J.D., Hoffman, R.R., Feltovich, P.J.: Ten challenges for making automation a “team player” in joint human-agent activity. IEEE Intell. Syst. 19, 91–95 (2004)CrossRefGoogle Scholar
  18. Laird, J.E.: The SOAR Cognitive Architecture. MIT Press, Lakatos (2012)Google Scholar
  19. Lebiere, C., Best, B.J.: From microcognition to macrocognition: architectural support for adversarial behavior. J. Cogn. Eng. Decis. Making 3(2), 176–193 (2009)CrossRefGoogle Scholar
  20. MacDougall, W.K., West, R., Hancock, E.: Modeling multi-agent chaos: killing aliens and managing difficult people. In: 36th Annual Meeting of the Cognitive Science Society, pp. 2603–2608 (2014)Google Scholar
  21. Meyer, D.E., Kieras, D.E.: A computational theory of executive control processes and human multiple-task processes and human multiple-task performance: Part 1. Basic mechanisms. Psychol. Rev. 104, 3–65 (1997)CrossRefGoogle Scholar
  22. Newell, A.: You can’t play 20 questions with nature and win: projective comments on the papers of this symposium. In: Visual Information Processing. Academic Press (1973)CrossRefGoogle Scholar
  23. Newell, A.: Unified Theories of Cognition. Harvard University Press, Cambridge (1990)Google Scholar
  24. Pronovost, S., West, R.L.: A GOMS model of virtual sociotechnical systems: using video games to build cognitive models. In: Proceedings of the European Conference on Cognitive Ergonomics (2008a)Google Scholar
  25. Pronovost, S., West, R.L.: Bridging cognitive modeling and model-based evaluation: extending GOMS to model virtual sociotechnical systems and strategic activities. In: Proceedings of the 52nd Annual Meeting of the Human Factors and Ergonomics Society (2008b)Google Scholar
  26. Ritter, F.E., Haynes, S.R., Cohen, M.A., Howes, A., John, B.E., Best, B., Lebiere, C., Jones, R.M., Lewis, R.L., St. Amant, R., McBride, S.P., Urbas, L., Leuchter, S., Vera, A.: High-level behavior representation languages revisited. In: Proceedings of the International Conference on Cognitive Modeling, Trieste, Italy, pp. 404–407 (2006)Google Scholar
  27. Salvucci, D.D., Taatgen, N.A.: Threaded cognition: an integrated theory of concurrent multitasking. Psychol. Rev. 115, 101 (2008)CrossRefGoogle Scholar
  28. Stewart, T.C., West, R.L.: Deconstructing ACT-R. In: International conference on cognitive modelling, Trieste, Italy (2006)Google Scholar
  29. Somers, S., West, R.L.: Macro cognition: using SGOMS to pilot a flight simulator. In: Proceedings of the Annual ACT-R Workshop (2012)Google Scholar
  30. Somers, S. West, R.L.: Steering control in a flight simulator using ACT-R. In: Proceedings of the International Conference on Cognitive Modeling (2013)Google Scholar
  31. Thomson, R., Lebiere, C., Anderson, J.R., Staszewski, J.: A general instance-based learning framework for studying intuitive decision-making in a cognitive architecture. J. Appl. Res. Mem. Cogn. 4, 180–190 (2015)CrossRefGoogle Scholar
  32. Turvey, M.T., Carello, C.: On intelligence from first principles: Guidelines for inquiry into the hypothesis of physical intelligence (PI). Ecol. Psychol. 24(1), 3–32 (2012)CrossRefGoogle Scholar
  33. Van Gelder, T., Port, R.F.: It’s about time: an overview of the dynamical approach to cognition. In: Port, R.F., Van Gelder, T. (eds.) Mind as motion: Explorations in the dynamics of cognition. MIT Press, Cambridge (1995)Google Scholar
  34. Varela, F., Lachaux, J.P., Rodriguez, E., Martinerie, J.: The brainweb: phase synchronization and large-scale integration. Nat. Rev. Neurosci. 2(4), 229–239 (2001)CrossRefGoogle Scholar
  35. Vera, A.H., Tollinger, I., Eng, K., Lewis, R., Howes, A.: Architectural building blocks as the locus of adaptive behavior selection. Proc. Cogn. Sci. Soc. 27 (2005)Google Scholar
  36. West, R.L., Nagy, G.: Using GOMS for modeling routine tasks within complex sociotechnical systems: connecting macrocognitive models to microcognition. J. Cogn. Eng. Decis. Mak. 1, 186–211 (2007)CrossRefGoogle Scholar
  37. West, R.L., Pronovost, S.: Modeling SGOMS in ACT- R: linking macro-and microcognition. J. Cogn. Eng. Decis. Mak. 3(2), 194–207 (2009)CrossRefGoogle Scholar
  38. West, R.L., Somers, S.: Scaling up from micro cognition to macro cognition: using SGOMS to build macro cognitive models of sociotechnical work in ACT-R. In: The proceedings of Cognitive Science, pp. 1788–1793. Boston, Mass: Cognitive Science (2011)Google Scholar
  39. West, R.L., Hancock, E., Somers, S., MacDougal, K., Jeanson, F.: The macro-architecture hypothesis: applications to modeling teamwork, conflict resolution, and literary analysis. In: Proceedings of the International Conference for Cognitive Modeling (2013)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Robert West
    • 1
    Email author
  • Lawrence Ward
    • 2
  • Kate Dudzik
    • 1
  • Nathan Nagy
    • 1
  • Fraydon Karimi
    • 1
  1. 1.Institute of Cognitive ScienceCarleton UniversityOttawaCanada
  2. 2.Department of PsychologyUniversity of British ColombiaVancouverCanada

Personalised recommendations