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A model of a task in a cognitive architecture, generally referred to as a cognitive model, consists of both the architecture and the knowledge to perform the task. This knowledge is acquired through human factors methods including task analyses of the activity being modeled. Cognitive architectures are also connected with a complex simulation of the environment in which the task is to be performed - sometimes, the architecture interacts directly with the actual software humans use to perform the task. Cognitive architectures not only produce a prediction about performance, but also output actual performance data - able to produce time-stamped sequences of actions that can be compared with real human performance on a task.
Examples of cognitive architectures include the EPIC system (Hornof & Kieras, 1997, 1999), CPM-GOMS (Kieras, Wood, & Meyer, 1997), the Queuing Network-Model Human Processor (Wu & Liu, 2007, 2008),<ref name=":4">{{Cite journal|title = Queuing Network Modeling of Driver Workload and Performance|journal = IEEE Transactions on Intelligent Transportation Systems|date = 2007-09-01|issn = 1524-9050|pages = 528–537|volume = 8|issue = 3|doi = 10.1109/TITS.2007.903443|first = Changxu|last = Wu|first2 = Yili|last2 = Liu}}</ref><ref>{{Cite journal|title = Queuing Network Modeling of a Real-Time Psychophysiological Index of Mental Workload #x2014;P300 in Event-Related Potential (ERP)|journal = IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans|date = 2008-09-01|issn = 1083-4427|pages = 1068–1084|volume = 38|issue = 5|doi = 10.1109/TSMCA.2008.2001070|first = Changxu|last = Wu|first2 = Yili|last2 = Liu|first3 = C.M.|last3 = Quinn-Walsh}}</ref>, ACT-R (Anderson, 2007; Anderson & Lebiere, 1998), and QN-ACTR (Cao & Liu, 2013)<ref>{{Cite journal|title = Queueing network-adaptive control of thought rational (QN-ACTR): An integrated cognitive architecture for modelling complex cognitive and multi-task performance|journal = International Journal of Human Factors Modelling and Simulation|date = 2013|pages = 63-86|volume = 4|issue = 1|first = Shi|last = Cao|first2 = Yili|last2 = Liu}}</ref>.
The Queuing Network-Model Human Processor model was used to predict how drivers perceive the operating speed and posted speed limit, make choice of speed, and execute the decided operating speed. The model was sensitive (average d’ was 2.1) and accurate (average testing accuracy was over 86%) to predict the majority of unintentional speeding<ref name=":4" />
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