Applications for Cognitive User Modeling

  • Marcus Heinath
  • Jeronimo Dzaack
  • Andre Wiesner
  • Leon Urbas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4511)


Usability of complex dynamic human computer interfaces can be evaluated by cognitive modeling to investigate cognitive processes and their underlying structures. Even though the prediction of human behavior can help to detect errors in the interaction design and cognitive demands of the future user the method is not widely applied. The time-consuming transformation of a problem “in the world” into a “computational model” and the lack of fine-grained simulation data analysis are mainly responsible for this. Having realized these drawbacks we developed HTAmap and SimTrA to simplify the development and analysis of cognitive models. HTAmap, a high-level framework for cognitive modeling, aims to reduce the modeling effort. SimTrA supports the analysis of cognitive model data on an overall and microstructure level and enables the comparison of simulated data with empirical data. This paper describes both concepts and shows their practicability on an example in the domain of process control.


usability evaluation human computer interaction cognitive modeling high-level description analysis 


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  1. 1.
    Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., Qin, Y.: An integrated theory of the mind. Psychological Review 111, 1036–1060 (2004)CrossRefGoogle Scholar
  2. 2.
    Byrne, M.D.: Cognitive Architectures. In: Jacko, J., Sears, A. (eds.) Handbook of Human-Computer Interaction, pp. 97–117. Lawrence Erlbaum Associates, Hillsdale, NJ (2003)Google Scholar
  3. 3.
    Card, S.K., Moran, T.P., Newell, A.: The Psychology of Human-Computer Interaction. Lawrence Erlbaum Associates, Hillsdale, NJ (1983)Google Scholar
  4. 4.
    Crossman, J., Wray, R.E., Jones, R.M., Lebiere, C.A.: High Level Symbolic Representation for Behaviour Modeling. In: Proceedings of the Conference on Behavior Representation. Arlington (2004)Google Scholar
  5. 5.
    Dzaack, J., Urbas, L.: Kognitive Modelle zur Evaluation der Gebrauchstauglichkeit von Mensch-Maschine Systemen. In: Grandt, M., Bauch, A. (eds.): Cognitive Systems Engineering in der Fahrzeug- und Prozessführung. pp. 305–318, DGLR, Bonn, (2006)Google Scholar
  6. 6.
    Ellis, S.R., Smith, J.D.: Patterns of Statistical Dependency in Visual Scanning. In: Groner, R., McConkie, G.W., Menz, C. (eds.) Eye Movement and Human Information Processing, pp. 221–238. Elsevier, Amsterdam (1985)Google Scholar
  7. 7.
    Gray, W.D., Sims, C.R., Schoelles, M.J.: Musings on Models of Integrated Cognition: What Are They? What Do They Tell Us that Simpler Approaches Cannot? How Do We Evaluate Them? In: Proceedings of the 7th ICCM, pp. 12–13, LEA, Hillsdale, NJ, (2006)Google Scholar
  8. 8.
    Groner, R., Walder, F., Groner, M.: Looking at faces: local and global aspects of scanpaths. In: Gale, A.G., Johnson, F. (eds.) Theoretical and Applied Aspects of Eye Movement Research, pp. 523–533. Elsevier, North-Holland (1984)CrossRefGoogle Scholar
  9. 9.
    Heffernan, N.T., Koedinger, K.R., Aleven, V.A.: Tools Towards Reducing the Costs of Designing, Building, and Testing Cognitive Models. In: Proceedings of the Conference on Behavior Representation in Modeling and Simulation (2003)Google Scholar
  10. 10.
    Hollnagel, E.: Cognitive Reliability and Error Analysis Method (CREAM). Elsevier, Oxford (1998)Google Scholar
  11. 11.
    Howes, A., Young, R.M.: The role of cognitive architecture in modeling the user: Soar’s learning mechanism. Human-Computer Interaction 12(4), 311–343 (1997)CrossRefGoogle Scholar
  12. 12.
    Just, M.A., Carpenter, P.A.: Using eye fixations to study reading comprehension: Individual differences in working memory. Psychological Review 99, 122–149 (1984)CrossRefGoogle Scholar
  13. 13.
    Ormerod, T.C., Shepherd, A.: Using Task Analysis for Information Requirements Specification: The Sub-Goal Template (SGT) Method. In: Diaper, D., Stanton, N.A. (eds.) The handbook of task analysis for human-computer interaction, pp. 347–365. Lawrence Erlbaum Associates, Mahwah, NJ (2004)Google Scholar
  14. 14.
    Rayner, K.: Do eye movements reflect higher order processes in reading? In: Groner, R., d’Ydewalle, G., Parham, R. (eds.) From Eye to Mind: Information Acquisition in Perception, Search, and Reading, pp. 179–190. Elsevier, New York (1999)Google Scholar
  15. 15.
    Ritter, F.R., Haynes, S.R., Cohen, M., Howes, A., John, B., Best, B., et al.: High-level Behavior Representation Languages Revisited. In: Proceedings of the ICCM 2006. Edizioni Goliardiche, pp. 404– 407 (2006)Google Scholar
  16. 16.
    Rötting, M.: Parametersystematik der Augen- und Blickbewegungen für arbeitswissenschaftliche Untersuchungen. Shaker, Aachen (2001)Google Scholar
  17. 17.
    Salvucci, D.D., Lee, F.J.: Simple Cognitive Modeling in a Complex Cognitive Architecture. In: Proceedings of the CHI 2003, pp. 265–272. ACM Press, New York (2003)Google Scholar
  18. 18.
    Shepherd, A.: Hierarchical task analysis. Taylor & friends, New York (2001)Google Scholar
  19. 19.
    Strube, G.: Cognitive Modeling: Research Logic in Cognitive Science. In: Smelser, N.J., Baltes, P.B. (eds.) International encyclopedia of the social and behavioral sciences, pp. 2124–2128. Elsevier, Oxford (2001)Google Scholar
  20. 20.
    Urbas, L., Leuchter, S.: Model Based Analysis and Design of Human-Machine Dialogues through Displays. KI – Künstliche Intelligenz, vol. 4, pp. 45–51 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Marcus Heinath
    • 1
  • Jeronimo Dzaack
    • 1
  • Andre Wiesner
    • 1
  • Leon Urbas
    • 1
  1. 1.Center of Human-Machine-Systems - Technische Universität Berlin, Institute of Automation - Technische Universität Dresden 

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