The increasing complexity of computational cognitive architectures may increase both their modeling capabilities and their difficulty to learn and use as cognitive engineering tools. This paper reports our work dedicated to enhance the usability and the cognitive engineering applicability of a complex computational cognitive architecture called QN-ACTR, which integrates two complementary architectures Queueing Network and Adaptive Control of Thought-Rational. The aim is to provide an easy-to-use interface and intuitive modeling that support both inexperienced and experienced users in using this complex and powerful architecture. The process of model development is greatly simplified with improved visualization and validation methods. The results were examined using heuristic evaluation. The benefits and practice implications are discussed.
Get full access to this article
View all access options for this article.
References
1.
AllenderL.KelleyT.ArcherS.AdkinsR. (1997). IMPRINT: The transition and further development of a soldier-system analysis tool. MANPRINT Quarterly, 5(1), 1-7.
2.
AmantR. S.FreedA. R.RitterF. E. (2005). Specifying ACT-R models of user interaction with a GOMS language. Cognitive Systems Research, 6, 71–88.
3.
AndersonJ. R.BothellD.ByrneM. D.DouglassS.LebiereC.QinY. (2004). An integrated theory of the mind. Psychological Review, 111(4), 1036–1060.
4.
ByrneM. D.PewR. W. (2009). A history and primer of human performance modeling. Reviews of Human Factors and Ergonomics, 5, 225-263.
5.
CaoS.LiuY. (2011a). Integrating Queueing Network and ACT-R cognitive architectures. Human Factors and Ergonomics Society Annual Meeting Proceedings, 55, 836-840.
6.
CaoS.LiuY. (2011b). Mental workload modeling in an integrated cognitive architecture. Human Factors and Ergonomics Society Annual Meeting Proceedings, 55, 2083-2087.
7.
GilG.-H. (2010). An accessible cognitive modeling tool for evaluation of human-automation interaction in the systems design process. Doctoral Dissertation. North Carolina State University.
8.
GrayW. D. (2008). Cognitive modeling for cognitive engineering. In SunR. (Ed.), The Cambridge Handbook of Computational Psychology (pp. 565-588). New York: Cambridge University Press.
9.
HollingsedT.NovickD. G. (2007). Usability inspection methods after 15 years of research and practice. SIGDOC ’07: Proceedings of the 25th Annual ACM International Conference on Design of Communication, 249-255.
10.
JohnB. E.PrevasK.SalvucciD. D.KoedingerK. (2004). Predictive human performance modeling made easy. Paper presented at the Proceedings of the SIGCHI conference on Human factors in computing systems, Vienna, Austria.
11.
LiuY.FeyenR.TsimhoniO. (2006). Queueing Network-Model Human Processor (QN-MHP): A computational architecture for multitask performance in human-machine systems. ACM Transactions on Computer-Human Interaction, 13(1), 37–70.
12.
NewellA. (1990). Unified theories of cognition. Cambridge, MA: Harvard University Press.
13.
NielsenJ. (1994). Heuristic evaluation. In NielsenJ.MackR. L. (Eds.), Usability Inspection Methods. New York, NY: John Wiley & Sons.
14.
PattonE. W.GrayW. D. (2010). SANLab-CM: A tool for incorporating stochastic operations into activity network modeling. Behavior Research Methods, 42(3), 877-883.
15.
PewR. W. (2008). Foreword. In FoyleD. C.HooeyB. L. (Eds.), Human Performance Modeling in Aviation. Boca Raton, FL: CRC Press/Taylor & Francis Group.
16.
SalvucciD. D. (2006). Modeling driver behavior in a cognitive architecture. Human Factors, 48(2), 362–380.
17.
SalvucciD. D. (2009). Rapid prototyping and evaluation of in-vehicle interfaces. ACM Transactions on Computer-Human Interaction, 16(2), 9: 1-33.
18.
SalvucciD. D.LeeF. J. (2003). Simple cognitive modeling in a complex cognitive architecture. Paper presented at the Proceedings of the SIGCHI conference on Human factors in computing systems, Ft. Lauderdale, Florida, USA.
19.
SchunnC. D.GrayW. D. (2002). Introduction to the special issue on computational cognitive modeling. Cognitive Systems Research, 3, 1–3.
20.
WuC.LiuY. (2008). Queuing network modeling of the psychological refractory period (PRP). Psychological Review, 115(4), 913–954.
21.
WuC.LiuY. (2009). Development and evaluation of an ergonomic software package for predicting multiple-task human performance and mental workload in human–machine interface design and evaluation. Computers and Industrial Engineering, 56, 323–333.
