Human performance modeling (HPM) is a method of quantifying human behavior, cognition, and processes. It is a tool used by human factors researchers and practitioners for both the analysis of human function and for the development of systems designed for optimal user experience and interaction . It is a complementary approach to other usability testing methods for evaluating the impact of interface features on operator performance.
History.
The Human Factors and Ergonomics Society (HFES) formed the Human Performance Modeling Technical Group in 2004. Although a recent discipline, human factors practitioners have been constructing and applying models of human performance since World War II. Notable early examples of human performance models include Paul Fitts' model of aimed motor movement (1954), the choice reaction time models of Hick (1952) and Hyman (1953), and the Swets et al. (1964) work on signal detection. It is suggested that the earliest developments in HPM arose out of the need to quantify human-system feedback for those military systems in development during WWII (see Manual Control Theory below), with continued interest in the development of these models augmented by the cognitive revolution (see Cognition &amp; Memory below).
Human Performance Models.
Human performance models predict human behavior in a task, domain, or system. However, these models must be based upon and compared against empirical human-in-the-loop data to ensure that the human performance predictions are correct. As human behavior is inherently complex, simplified representations of interactions are essential to the success of a given model. As no model is able to capture the complete breadth and detail of human performance within a system, domain, or even task, details are abstracted away to these keep models manageable. Although the omission of details is an issue in basic psychological research, it is less of a concern in applied contexts such as those of most concern to the human factors profession. This is related to the internal-external validity trade-off. Regardless, development of a human performance model is an exercise in complexity science. Communication and exploration of the most essential variables governing a given process are often just as important as the accurate prediction of an outcome given those variables.
The goal of most human performance models is to capture enough detail in a particular domain to be useful for the purposes of investigation, design, or evaluation; thus the domain for any particular model is often quite restricted. 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. Human performance models contain both the explicit and implicit assumptions or hypotheses upon which the model depends, and are typically mathematical - being composed of equations or computer simulations - although there are also important models that are qualitative in nature.
Individual models vary in their origins, but share in their application and use for issues in the human factors perspective. These can be models of the products of human performance (e.g., a model that produces the same decision outcomes as human operators), the processes involved in human performance (e.g., a model that simulates the processes used to reach decisions), or both. Generally, they are regarded as belonging to one of three areas: perception &amp; attention allocation, command &amp; control, or cognition &amp; memory; although models of other areas such as emotion, motivation, and social/group processes continue to grow burgeoning within the discipline. Integrated models are also of increasing importance. Anthropometric and biomechanical models are also crucial human factors tools in research and practice, and are used alongside other human performance models, but have an almost entirely separate intellectual history, being individually more concerned with static physical qualities than processes or interactions.
The models are applicable in many number of industries and domains including military, aviation, nuclear power, automotive, space operations, manufacturing, user experience/user interface (UX/UI) design, etc. and have been used to model human-system interactions both simple and complex.
Model Categories.
Command &amp; Control.
Human performance models of Command &amp; Control describe the products of operator output behavior, and are often also models of dexterity within the interactions for certain tasks.
Hick-Hyman Law.
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. They suggested that information entropy ("H") is a function of the number of alternatives ("n") in a choice task, H = log2("n" + 1); and that reaction time (RT) of a human operator is a linear function of the entropy: RT = a + bH. This is known as the Hick-Hyman law for choice response time.
Pointing.
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. The index of difficulty (ID) for any given movement is a function of the ratio of distance to the target (D) and width of the target (W): ID = log2"(2D/W) -" a relationship derivable from information theory. Fitts' law is actually responsible for the ubiquity of the computer mouse, due to the research of Card, English, and Burr (1978). Extensions of Fitt's law also apply to pointing at spatially moving targets, via the "steering law", originally discovered by C.G. Drury in 1971 and later on rediscovered in the context of human-computer interaction by Accott &amp; Zhai (1997, 1999).
Manual Control Theory.
Complex motor tasks, such as those carried out by musicians and athletes, are not well modeled due to their complexity. Human target-tracking behavior, however, is one complex motor task that is an example of successful HPM.
The history of manual control theory is extensive, dating back to the 1800s in regard to the control of water clocks. However, during the 1940s with the innovation of servomechanisms in WWII, extensive research was put into the continuous control and stabilization of contemporary systems such as radar antennas, gun turrets, and ships/aircraft via feedback control signals.
Analysis methods were developed that predicted the required control systems needed to enable stable, efficient control of these systems (James, Nichols, &amp; Phillips, 1947). Originally interested in temporal response - the relationship between sensed output and motor output as a function of time - James et al. (1947) discovered that the properties of such systems are best characterized by understanding temporal response after it had been transformed into a frequency response; a ratio of output to input amplitude and lag in response over the range of frequencies to which they are sensitive. For systems that respond linearly to these inputs, the frequency response function could be expressed in a mathematical expression called a "transfer function". This was applied first to machine systems, then human-machine systems for maximizing human performance. Tustin (1947), concerned with the design of gun turrets for human control, was first to demonstrate that nonlinear human response could be approximated by a type of transfer function. McRuer and Krenzel (1957) synthesized all the work since Tustin (1947), measuring and documenting the characteristics of the human transfer function, and ushered in the era of manual control models of human performance. As electromechanical and hydraulic flight control systems were implemented into aircraft, automation and electronic artificial stability systems began to allow human pilots to control highly sensitive systems These same transfer functions are still used today in control engineering.
From this, the "optimal control model" (Pew &amp; Baron, 1978) developed in order to model a human operator's ability to internalize system dynamics and minimize objective functions, such as root mean square (RMS) error from the target. The optimal control model also recognizes noise in the operator's ability to observe the error signal, and acknowledges noise in the human motor output system.
Technological progress and subsequent automation have reduced the necessity and desire of manual control of systems, however. Human control of complex systems is now often of a supervisory nature over a given system, and both human factors and HPM have shifted from investigations of perceptual-motor tasks, to the cognitive aspects of human performance.
Attention &amp; Perception.
Signal Detection Theory (SDT).
Although not a formal part of HPM, signal detection theory has an influence on the method, especially within the Integrated Models. SDT is almost certainly the best-known and most extensively used modeling framework in human factors, and is a key feature of education regarding human sensation and perception. 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. This judgement may be applied in any number of vital contexts. Besides the response of the operator, there are two possible "true" states of the world - either the signal was present or it was not. If the operator correctly identifies the signal as present, this is termed a "hit" (H). If the operator responds that a signal was present when there was no signal, this is termed a "false alarm" (FA). If the operator correctly responds when no signal is present, this is termed a "correct rejection" (CR). If a signal is present and the operator fails to identify it, this is termed a "miss" (M).
In applied psychology and human factors, SDT is applied to research problems including recognition, memory, aptitude testing, and vigilance. Vigilance, referring to the ability of operators to detect infrequent signals over time, is important for human factors across a variety of domains.
Visual Search.
A developed area in attention is the control of visual attention - models that attempt to answer, "where will an individual look next?" A subset of this concerns the question of visual search: How rapidly can a specified object in the visual field be located? This is a common subject of concern for human factors in a variety of domains, with a substantial history in cognitive psychology. This research continues with modern conceptions of salience and salience maps. Human performance modeling techniques in this area include the work of Melloy, Das, Gramopadhye, and Duchowski (2006) regarding Markov models designed to provide upper and lower bound estimates on the time taken by a human operator to scan a homogeneous display. Another example from Witus and Ellis (2003) includes a computational model regarding the detection of ground vehicles in complex images. Facing the nonuniform probability that a menu option is selected by a computer user when certain subsets of the items are highlighted, Fisher, Coury, Tengs, and Duffy (1989) derived an equation for the optimal number of highlighted items for a given number of total items of a given probability distribution. Because visual search is an essential aspect of many tasks, visual search models are now developed in the context of integrating modeling systems. For example, Fleetwood and Byrne (2006) developed an ACT-R model of visual search through a display of labeled icons - predicting the effects of icon quality and set size not only on search time but on eye movements.
Visual Sampling.
Many domains contain multiple displays, and require more than a simple discrete yes/no response time measurement. A critical question for these situations may be "How much time will operators spend looking at X relative to Y?" or "What is the likelihood that the operator will completely miss seeing a critical event?" Visual sampling is the primary means of obtaining information from the world. An early model in this domain is Sender's (1964, 1983) based upon operators monitoring of multiple dials, each with different rates of change. Operators try, as best as they can, to reconstruct the original set of dials based on discrete sampling. This relies on the mathematical Nyquist theorem stating that a signal at W Hz can be reconstructed by sampling every 1/W seconds. This was combined with a measure of the information generation rate for each signal, to predict the optimal sampling rate and dwell time for each dial. Human limitations prevent human performance from matching optimal performance, but the predictive power of the model influenced future work in this area, such as Sheridan's (1970) extension of the model with considerations of access cost and information sample value.
A modern conceptualization by Wickens et al. (2008) is the salience, effort, expectancy, and value (SEEV) model. It was developed by the researchers (Wickens et al., 2001) as a model