Human performance modeling: Difference between revisions

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Revision as of 18:32, 9 August 2019

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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 | first1 = P. M. | year = 1954 | title = The information capacity of the human motor system in controlling the amplitude of movement | url = | journal = Journal of Experimental Psychology | volume = 47 | issue = 6| pagepages = 381381–91 | doi=10.1037/h0055392 | pmid=13174710}}</ref> the choice reaction time models of Hick (1952)<ref>{{cite journal | last1 = Hick | first1 = W. E. | year = 1952 | title = On the rate of gain of information | url = | journal = Quarterly Journal of Experimental Psychology | volume = 4 | issue = 1| pages = 11–26 | doi=10.1080/17470215208416600}}</ref> and Hyman (1953),<ref>{{cite journal | last1 = Hyman | first1 = R | year = 1953 | title = Stimulus information as a determinant of reaction time | url = | journal = Journal of Experimental Psychology | volume = 45 | issue = 3| pagepages = 188188–96 | doi=10.1037/h0056940 | pmid=13052851}}</ref> and the Swets et al. (1964) work on signal detection.<ref>Swets, J. A., Tanner, W. P., & Birdsall, T. G. (1964). Decision processes in perception. ''Signal detection and recognition in human observers'', 3-57.</ref> 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 & Memory''''' below).<ref name=":1">{{Cite journal

| last = Byrne

| first = Michael D.

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| date = 2009-06-01

| title = A History and Primer of Human Performance Modeling

| url = http://rev.sagepub.com/content/5/1/225

| journal = Reviews of Human Factors and Ergonomics

| language = en

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==== 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|Fitt'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]].<ref name=":1" /> Fitt's law is actually responsible for the ubiquity of the computer [[Mouse (computing)|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<ref>{{Cite journal|last=DRURY|first=C. G.|date=1971-03-01|title=Movements with Lateral Constraint|url=https://dx.doi.org/10.1080/00140137108931246|journal=Ergonomics|volume=14|issue=2|pages=293–305|doi=10.1080/00140137108931246|issn=0014-0139|pmid=5093722}}</ref><ref>{{Cite journal|last=Drury|first=C. G.|last2=Daniels|first2=E. B.|date=1975-07-01|title=Performance Limitations in Laterally Constrained Movements|url=https://dx.doi.org/10.1080/00140137508931472|journal=Ergonomics|volume=18|issue=4|pages=389–395|doi=10.1080/00140137508931472|issn=0014-0139}}</ref><ref>{{Cite web|url=http://ieeexplore.ieee.org/abstract/document/4309061/?reload=true|title=Self-Paced Path Control as an Optimization Task - IEEE Xplore Document|website=ieeexplore.ieee.org|language=en-US|access-date=2017-03-02}}</ref> and later on rediscovered in the context of human-computer interaction by Accott & Zhai (1997, 1999).<ref>{{Cite journal|last=Accot|first=Johnny|last2=Zhai|first2=Shumin|date=1997-01-01|title=Beyond Fitts' Law: Models for Trajectory-based HCI Tasks|url=http://doi.acm.org/10.1145/258549.258760|journal=Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems|series=CHI '97|___location=New York, NY, USA|publisher=ACM|pages=295–302|doi=10.1145/258549.258760|isbn=0897918029}}</ref><ref>{{Cite journal|last=Accot|first=Johnny|last2=Zhai|first2=Shumin|date=1999-01-01|title=Performance Evaluation of Input Devices in Trajectory-based Tasks: An Application of the Steering Law|url=http://doi.acm.org/10.1145/302979.303133|journal=Proceedings of the SIGCHI Conference on Human Factors in Computing Systems|series=CHI '99|___location=New York, NY, USA|publisher=ACM|pages=466–472|doi=10.1145/302979.303133|isbn=0201485591}}</ref>

==== [[Control theory|Manual Control Theory]] ====

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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 | first1 = B. J. | last2 = Das | first2 = S. | last3 = Gramopadhye | first3 = A. K. | last4 = Duchowski | first4 = A. T. | year = 2006 | title = A model of extended, semisystematic visual search | url = | journal = Human Factors: The Journal of the Human Factors and Ergonomics Society | volume = 48 | issue = 3| pages = 540–554 | doi=10.1518/001872006778606840| pmid = 17063968 }}</ref> Another example from Witus and Ellis (2003) includes a computational model regarding the detection of ground vehicles in complex images.<ref>{{cite journal | last1 = Witus | first1 = G. | last2 = Ellis | first2 = R. D. | year = 2003 | title = Computational modeling of foveal target detection | url = | journal = Human Factors: The Journal of the Human Factors and Ergonomics Society | volume = 45 | issue = 1| pages = 47–60 | doi=10.1518/hfes.45.1.47.27231| pmid = 12916581 }}</ref> 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.<ref>{{cite journal | last1 = Fisher | first1 = D. L. | last2 = Coury | first2 = B. G. | last3 = Tengs | first3 = T. O. | last4 = Duffy | first4 = S. A. | year = 1989 | title = Minimizing the time to search visual displays: The role of highlighting | url = | journal = Human Factors: The Journal of the Human Factors and Ergonomics Society | volume = 31 | issue = 2| pages = 167–182 | doi = 10.1177/001872088903100206 | pmid = 2744770 }}</ref> 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.<ref name=":1" /><ref>{{cite journal | last1 = Fleetwood | first1 = M. D. | last2 = Byrne | first2 = M. D. | year = 2006 | title = Modeling the visual search of displays: a revised ACT-R model of icon search based on eye-tracking data | url = | journal = Human-Computer Interaction | volume = 21 | issue = 2| pages = 153–197 | doi=10.1207/s15327051hci2102_1}}</ref>

==== 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 | first1 = T | year = 1970 | title = On how often the supervisor should sample | url = | journal = IEEE Transactions on Systems Science and Cybernetics | volume = 2 | issue = 6| pages = 140–145 | doi = 10.1109/TSSC.1970.300289 }}</ref>

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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Although multiple resource theory the best known workload model in human factors, it is often represented qualitatively. 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. Integrated approaches, such as task network modeling, are also becoming more prevalent in the literature.<ref name=":1" />

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 = https://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|year = 2008}}</ref>

=== Cognition & Memory ===

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===== [[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 | first1 = M. R. | year = 1995 | title = Toward a theory of situation awareness in dynamic systems | url = | journal = Human Factors | volume = 37 | issue = 1| pages = 85–104 }}</ref><ref>Shively, R. J., Brickner, M., & Silbiger, J. (1997). A computational

model of situational awareness instantiated in MIDAS.

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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|url = http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4298914|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)|url = http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4604816|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> 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<ref name=":4" />