[7] Defining and communicating the domain of a given model is an essential feature of the practice - and of the entirety of human factors - as a systems discipline.
[7] The models are applicable in many number of industries and domains including military,[9][10] aviation,[11] nuclear power,[12] automotive,[13] space operations,[14] manufacturing,[15] user experience/user interface (UX/UI) design,[2] etc.
Hick (1952) and Hyman (1953) note that the difficulty of a choice reaction-time task is largely determined by the information entropy of the situation.
[7] Pointing at stationary targets such as buttons, windows, images, menu items, and controls on computer displays is commonplace and has a well-established modeling tool for analysis - Fitts's law (Fitts, 1954) - which states that the time to make an aimed movement (MT) is a linear function of the index of difficulty of the movement: MT = a + bID.
In application, the situation of interest is one in which a human operator has to make a binary judgement about whether a signal is present or absent in a noise background.
Vigilance, referring to the ability of operators to detect infrequent signals over time, is important for human factors across a variety of domains.
It was developed by the researchers (Wickens et al., 2001) as a model of scanning behavior describing the probability that a given area of interest will attract attention (AOI).
The model makes numerous accurate predictions, and those for which it cannot account are addressed by cognitive architectures (Byrne & Anderson, 2001; Meyer & Kieras, 1997).
The detailed computational implementations are better alternatives for application in HPM methods, to include the Horrey and Wickens (2003) model, which is general enough to be applied in many domains.
[7] Numerical typing is an important perceptual-motor task whose performance may vary with different pacing, finger strategies and urgency of situations.
Queuing network-model human processor (QN-MHP), a computational architecture, allows performance of perceptual-motor tasks to be modelled mathematically.
The model can be applied to provide optimal speech rates for voice synthesis and keyboard designs in different numerical typing situations.
[7] Detailed versions of GOMS exist, including: --CPM-GOMS: "Cognitive, perceptual, motor"" and 'critical path method" (John & Kieras, 1996a, 1996b) - attempts to break down performance into primitive CPM units lasting tens to hundreds of milliseconds (durations for many operations in CPM-GOMS models come from published literature, especially Card et al., 1983).
Many generic mental operations in the KLM are replaced with detailed descriptions of the cognitive activity involving the organization of people's procedural knowledge into methods.
These activities starkly contrast to routine cognitive skills, for which the procedures are known in advance, as many situations require operators to make judgments under uncertaintly - to produce a rating of quality, or perhaps choose among many possible alternatives.
[7] Sometimes optimal performance is uncertain, one powerful and popular example is the lens model (Brunswick, 1952; Cooksey, 1996; Hammond, 1955), which deals with policy capturing, cognitive control, and cue utilization, and has been used in aviation (Bisantz & Pritchett, 2003), command and control (Bisantz et al., 2000); to investigate human judgement in employment interviews (Doherty, Ebert, & Callender, 1986), financial analysis (Ebert & Kruse, 1978), physicians' diagnoses (LaDuca, Engel, & Chovan, 1988), teacher ratings (Carkenord & Stephens, 1944), and numerous others.
The narrow scope of these models is not limited to human factors, however - Newton's laws of motion have little predictive power regarding electromagnetism, for example.
When all these specifications are programmed into a computer, the network is exercised repeatedly in Monte Carlo fashion to build up distributions of the aggregate performance measures that are of concern to the analyst.
Detail regarding this, related, and alternative approaches may be found in Laughery, Lebiere, and Archer (2006) and in the work of Schwieckert and colleagues, such as Schweickert, Fisher, and Proctor (2003).
In the early 1970s, the U.S. Air Force sponsored the development of SAINT (Systems Analysis of Integrated Networks of Tasks), a high-level programming language specifically designed to support the programming of Monte Carlo simulations of human-machine task networks (Wortman, Pritsker, Seum, Seifert, & Chubb, 1974).
The network approach to modeling using these programs is popular due to its technical accessibility to individual with general knowledge of computer simulation techniques and human performance analysis.
The models can be developed to serve specific purposes - from simulation of an individual using a human-computer interface to analyzing potential traffic flow in a hospital emergency center.
Their weakness is the great difficulty required to derive performance times and success probabilities from previous data or from theory or first principles.
While theory is inadequate for the application of human factors, since the 1990s cognitive architectures also include mechanisms for sensation, perception, and action.
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).
GOMS has been used to model both complex team tasks (Kieras & Santoro, 2004) and group decision making (Sorkin, Hays, & West, 2001).
Concepts such as "attention", "processing capacity", "workload", and "situation awareness" (SA), both general and specific to human factors, are often difficult to quantify in applied domains.
Specificity requires that explanations be internally coherent; while verbal theories are often so flexible that they fail to remain consistent, allowing contradictory predictions to be derived from their use.
Purely empirical methods analyzed with hypothesis testing techniques, as standard in most psychological experiments, focus on providing answers to vague questions such as "Are A and B different?"
[7] As is the case in most model-based sciences, free parameters rampant within models of human performance also require empirical data a priori.