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== History ==
The [[Human Factors and Ergonomics Society]] (HFES) formed the Human Performance Modeling Technical Group in 2004. Although a recent discipline, [[Human factors and ergonomics|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 Fitt's model of aimed motor movement (1954),<ref>{{cite journal | last1 = Fitts
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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 & attention allocation, command & control, or cognition & 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<s>.</s> 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.<ref name=":1" />
The models are applicable in many number of industries and domains including military,<ref>Lawton, C. R., Campbell, J. E., & Miller, D. P. (2005). ''Human performance modeling for system of systems analytics: soldier fatigue'' (No. SAND2005-6569). Sandia National Laboratories.</ref><ref>Mitchell, D. K., & Samms, C. (2012). An Analytical Approach for Predicting Soldier Workload and Performance Using Human Performance Modeling. ''Human-Robot Interactions in Future Military Operations''.</ref> aviation,<ref>Foyle, D. C., & Hooey, B. L. (Eds.). (2007). ''Human performance modeling in aviation''. CRC Press.</ref> nuclear power,<ref>O’Hara, J. (2009). ''Applying Human Performance Models to Designing and Evaluating Nuclear Power Plants: Review Guidance and Technical Basis''. BNL-90676-2009). Upton, NY: Brookhaven National Laboratory.</ref> automotive,<ref>{{cite journal | last1 = Lim
== Model Categories ==
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===== [[Visual search|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 (neuroscience)|salience]] and [http://www.scholarpedia.org/article/Saliency_map 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.<ref>{{cite journal | last1 = Melloy
==== 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.<ref name=":3">Cassavaugh, N. D., Bos, A., McDonald, C., Gunaratne, P., & Backs, R. W. (2013). Assessment of the SEEV Model to Predict Attention Allocation at Intersections During Simulated Driving. In ''7th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design'' (No. 52).</ref> An early model in this ___domain is Sender's (1964, 1983) based upon operators monitoring of multiple dials, each with different rates of change.<ref>Senders, J. W. (1964). The human operator as a monitor and controller of multidegree of freedom systems. ''Human Factors in Electronics, IEEE Transactions on'', (1), 2-5.</ref><ref>Senders, J. W. (1983). ''Visual sampling processes'' (Doctoral dissertation, Universiteit van Tilburg).</ref> Operators try, as best as they can, to reconstruct the original set of dials based on discrete sampling. This relies on the mathematical [[Nyquist–Shannon sampling theorem|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.<ref name=":1" /><ref>{{cite journal | last1 = Sheridan
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 of scanning behavior describing the probability that a given area of interest will attract attention (AOI). The SEEV model is described by '''''p(A) = sS - efEF + (exEX)(vV)''''', in which ''p(A)'' is the probability a particular area will be samples, ''S'' is the ''salience'' for that area; ''EF'' represents the ''effort'' required in reallocating attention to a new AOI, related to the distance from the currently attended ___location to the AOI; ''EX'' (''expectancy'') is the expected event rate (bandwidth), and ''V'' is the value of the information in that AOI, represented as the product of Relevance and Priority (R*P).<ref name=":3" /> The lowercase values are scaling constants. This equation allows for the derivation of optimal and normative models for how an operator should behave, and to characterize how they behave. Wickens et al., (2008) also generated a version of the model that does not require absolute estimation of the free parameters for the environment - just the comparative salience of other regions compared to region of interest.<ref name=":1" />
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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 current study enhanced QN-MHP with a top-down control mechanism, a close-loop movement control and a finger-related motor control mechanism to account for task interference, endpoint reduction, and force deficit, respectively. The model also incorporated neuromotor noise theory to quantify endpoint variability in typing. The model predictions of typing speed and accuracy were validated with Lin and Wu’s (2011) experimental results. The resultant root-meansquared errors were 3.68% with a correlation of 95.55% for response time, and 35.10% with a correlation of 96.52% for typing accuracy. The model can be applied to provide optimal speech rates for voice synthesis and keyboard designs in different numerical typing situations.<ref>{{Cite journal|title = Mathematically modelling the effects of pacing, finger strategies and urgency on numerical typing performance with queuing network model human processor|url = http://dx.doi.org/10.1080/00140139.2012.697583|journal = Ergonomics|date = 2012-10-01|issn = 0014-0139|pmid = 22809389|pages = 1180–1204|volume = 55|issue = 10|doi = 10.1080/00140139.2012.697583|first = Cheng-Jhe|last = Lin|first2 = Changxu|last2 = Wu}}</ref>
The psychological refractory period (PRP) is a basic but important form of dual-task information processing. Existing serial or parallel processing models of PRP have successfully accounted for a variety of PRP phenomena; however, each also encounters at least 1 experimental counterexample to its predictions or modeling mechanisms. This article describes a queuing network-based mathematical model of PRP that is able to model various experimental findings in PRP with closed-form equations including all of the major counterexamples encountered by the existing models with fewer or equal numbers of free parameters. This modeling work also offers an alternative theoretical account for PRP and demonstrates the importance of the theoretical concepts of “queuing” and “hybrid cognitive networks” in understanding cognitive architecture and multitask performance.<ref>{{Cite journal|title = Queuing network modeling of the psychological refractory period (PRP).|url = http://doi.apa.org/getdoi.cfm?doi=10.1037/a0013123|journal = Psychological Review|pages = 913–954|volume = 115|issue = 4|doi = 10.1037/a0013123|first = Changxu|last = Wu|first2 = Yili|last2 = Liu|pmid=18954209}}</ref>
=== Cognition & Memory ===
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Another critical cognitive activity of interest to human factors is that of judgement and decision making. 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. Although many disciplines including mathematics and economics make significant contributions to this area of study, the majority of these models do not model human behavior but rather model optimal behavior, such as ''[[Expected utility hypothesis|subjective expected utility theory]]'' (Savage, 1954; von Neumann & Morgenstern, 1944). While models of optimal behavior are important and useful, they do not consider a baseline of comparison for human performance - though much research on human decision making in this ___domain compares human performance to mathematically optimal formulations. Examples of this include Kahneman and Tversky's (1979) ''[[prospect theory]]'' and Tversky's (1972) ''elimination by aspects model''. Less formal approaches include Tversky and Kahneman's seminal work on heuristics and biases, Gigerenzer's work on 'fast and frugal' shortcuts (Gigerenzer, Todd, & ABC Research Group, 2000), and the descriptive models of Paune, Bettman, and Johnson (1993) on adaptive strategies.<ref name=":1" />
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.<ref name=":1" /> Although the model does have limitations [described in Byrne & Pew (2009)], it is very powerful and remains underutilized in the human factors profession.<ref name=":1" />
===== [[Situation awareness|Situation Awareness]] (SA) =====
Models of SA range from descriptive (Endsley, 1995) to computational (Shively et al., 1997).<ref name=":2" /><ref>{{cite journal | last1 = Endsley
model of situational awareness instantiated in MIDAS.
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