Cognitive Architectures are broad theories of human cognition based on a wide selection of human empirical data and are generally implemented as computer simulations. They are the embodiment of a scientific hypothesis about those aspects of human cognition relatively constant over time and independent of task (Gray, Young, & Kirschenbaum, 1997; Ritter & young, 2001). Cognitive architectures are an attempt to theoretically unify disconnected empirical phenomena in the form of computer simulation models. While theory is inadequate for the application of human factors, since the 1990s cognitive architectures also include mechanisms for sensation, perception, and action. Two early examples of this include the Executive Process Interactive Control model (EPIC; Kieras, Wood, & Meyer, 1995; Meyer & Kieras, 1997) and the ACT-R (Byrne & Anderson, 1998).

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),[35][36] and the ACT-R (Anderson, 2007; Anderson & Lebiere, 1998).

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.

ACT-R has been used to model a wide variety of phenomena. It consists of several modules, each one modeling a different aspect of the human system. Modules are associated with specific brain regions, and the ACT-R has thus successfully predicted neural activity in parts of those regions. Each model essentially represents a theory of how that piece of the overall system works - derived from research literature in the area. For example, the declarative memory system in ACT-R is based on series of equations considering frequency and recency and that incorporate Baysean notions of need probability given context, also incorporating equations for learning as well as performance, Some modules are of higher fidelity than others, however - the manual module incorporates Fitt's law and other simple operating principles, but is not as detailed as the optimal control theory model (as of yet). The notion, however, is that each of these modules require strong empirical validation. This is both a benefit and a limitation to the ACT-R, as there is still much work to be done in the integration of cognitive, perceptual, and motor components, but this process is promising (Byrne, 2007; Foyle and Hooey, 2008; Pew & Mavor, 1998).

Group Behavior
Team/Crew Performance Modeling
GOMS has been used to model both complex team tasks (Kieras & Santoro, 2004) and group decision making (Sorkin, Hays, & West, 2001).

Modeling Approaches
Computer Simulation Models/Approaches

Example: IMPRINT

Mathematical Models/Approaches

Example: Cognitive model

Comparing HPM Models

To compare different HPM models, one of ways is to calculate their AIC (Akaike information criterion) and consider the Cross-validation criterion.