Cognitive modeling has not yet played much of a role in the study of sociotechnical systems. Arguably, this is because most cognitive modeling systems were originally created to model microcognitive results, not the types of macrocognitive behaviors that drive sociotechnical systems (Klein et al., 2003). However, this does not mean that cognitive modeling systems cannot be adapted to deal with macrocognitive activities in ways that are relevant to cognitive engineering. Previous research using GOMS in sociotechnical systems indicated that GOMS is problematic to use when interruptions and task switching are common; therefore, we added new theoretical structures to GOMS to deal with these issues. We tested the system by constructing a model of routine network maintenance and installation at a large telecommunications company. We then compared the model predictions with observations of the work. The results showed that the model results were useful in guiding the research and organizing the findings.
Get full access to this article
View all access options for this article.
References
1.
AndersonJ. R. (2002). Spanning seven orders of magnitude: A challenge for cognitive modeling. Cognitive Science, 26, 85–112.
2.
AndersonJ. R.BowerG. H. (1973). Human associative memory. Washington, DC: Winston and Sons.
3.
AndersonJ. R.LebiereC. (1998). The atomic components of thought. Mahwah, NJ: Erlbaum.
4.
AndersonJ. R.QinY.SohnM-H.StengerV. A.CarterC. S. (2003) An information-processing model of the BOLD response in symbol manipulation tasks. Psychonomic Bulletin and Review, 10, 241–261.
5.
ByrneM. D. (2003). Cognitive architecture. In JackoJ.SearsA. (Eds.), The human-computer interaction handbook: Fundamentals, evolving technologies and emerging applications (pp. 97–117). Mahwah, NJ: Erlbaum.
6.
ByrneM. D.AndersonJ. R. (1998). Perception and action. In AndersonJ. R.LebiereC. (Eds.), The atomic components of thought (pp. 167–200). Mahwah, NJ: Erlbaum.
7.
CacciabueP. C.HollnagelE. (1995). Simulation of cognition: Applications, expertise, and technology. In HocJ. M.CacciabueP. C.HollnagelE. (Eds.), Cognition and human-computer cooperation (pp. 55–73). Mahwah, NJ: Erlbaum.
8.
CardS. K.MoranT. P.NewellA. (1983). The psychology of human-computer interaction. Mahwah, NJ: Erlbaum.
9.
CardS. K.MoranT. P.NewellA. (1983). The keystroke level model for user performance with interactive systems. Communications of the ACM, 23, 396–410.
10.
GrayW. D.JohnB. E.AtwoodM. E. (1993). Project Ernestine: Validating a GOMS analysis for predicting and explaining real-world task performance. Human-Computer Interaction, 8, 237–309.
11.
JohnB. (1988). Contributions to engineering models of human computer interaction. Ph.D. thesis, Carnegie Mellon University, Pittsburgh, PA.
12.
JohnB. (1990). Extensions of GOMS analyzes to expert performance requiring perception of dynamic visual and auditory information. Proceedings of CHI 199 (pp. 107–115). New York: Association for Computing Machinery.
13.
JohnB. E. (1995, October). Why GOMS? Interactions, 2(4), 80–89.
14.
JohnB. E.KierasD. E. (1996). The GOMS family of user interface analysis techniques: Comparison and contrast. ACM Transactions on Computer-Human Interaction, 3, 320–351.
15.
KleinG. (1998). Sources of power: How people make decisions. Cambridge: MIT Press.
16.
KleinG. (2002). Year one final report: High-level cognitive processes in field research (Prepared through participation in the Advanced Decision Architectures Collaborative Technology Alliance sponsored by the U.S. Army Research Laboratory under Cooperative Agreement DAAD19–01–2–0009).
KleinG.WoodsD. D.BradshawJ. D.HoffmanR. R.FeltovichP. J. (2004, November/December). Ten challenges for making automation a “team player” in joint human-agent activity. IEEE: Intelligent Systems, 91–95.
19.
KierasD. (2004). GOMS models for task analysis. In DiaperD.StantonN. A. (Eds.), The handbook of task analysis for human computer interaction (pp. 83–116). Mahwah, NJ: Erlbaum.
20.
KierasD.MeyerD. E. (1997). An overview of the EPIC architecture for cognition and performance with application to human-computer interaction. Human-Computer Interaction, 12, 391–438.
21.
KierasD.SantoroP. (2004) Computational GOMS modeling of a complex team task: Lessons learned. In Proceedings of CHI 2004 (pp. 97–104), Vienna, Austria.
22.
KierasD. E. (1996). Guide to GOMS model usability evaluations using NGOMSL. In HelanderM.LandauerT. (Eds.), The handbook of human-computer interaction (2nd ed., pp. 733–766). Amsterdam: North-Holland.
23.
NewellA. (1990). Unified theories of cognition. Cambridge: Harvard University Press.
24.
PejtersenA. M.RasmussenJ. (1997). Ecological information systems and support of learning: Coupling work domain information and user characteristics. In HelanderM.LandauerT.PrabhuP. (Eds.), Handbook of human-computer interaction (2nd ed., pp. 315–346), Amsterdam: North-Holland.
25.
SalvucciD. D. (2006). Modeling driver behavior in a cognitive architecture. Human Factors, 48, 362–380.
26.
TristE. L.BamforthK. W. (1951). Some social and psychological consequences of the long-wall method of coal-getting. Human Relations, 4, 3–38.
27.
VicenteK. J. (1999). Cognitive work analysis: Toward safe, productive, and healthy computer-based work. Mahwah, NJ: Erlbaum.
28.
WestR. L.YuenK. L. (2006). A framework for incorporating social context into GOMS models. Poster presented at Cognitive Science 1999: The Annual Conference of the Cognitive Science Society, Vancouver, Carleton University, Cognitive Science Technical Reports.
