Human performance modeling: Difference between revisions

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{{Short description|Human research factorization and quantification system}}

'''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 .<ref name=":0">Sebok, A., Wickens, C., & Sargent, R. (2013, September). Using Meta-Analyses Results and Data Gathering to Support Human Performance Model Development. In ''Proceedings of the Human Factors and Ergonomics Society Annual Meeting'' (Vol. 57, No. 1, pp. 783-787). SAGE Publications.</ref> It is a complementary approach to other usability testing methods for evaluating the impact of interface features on operator performance.<ref name="Carolan, T. 2000, pp. 650-653">Carolan, T., Scott-Nash, S., Corker, K., & Kellmeyer, D. (2000, July). An application of human performance modeling to the evaluation of advanced user interface features. In ''Proceedings of the Human Factors and Ergonomics Society Annual Meeting'' (Vol. 44, No. 37, pp. 650-653). SAGE Publications.</ref>

== History ==

The [[Human Factors and Ergonomics Society]] (HFES) formed the [https://sites.google.com/view/hfes-hpmtg/ 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 FittFitts'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 | journal = Journal of Experimental Psychology | volume = 47 | issue = 6| pages = 381–91 | doi=10.1037/h0055392 | pmid=13174710| s2cid = 501599 }}</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 | journal = Quarterly Journal of Experimental Psychology | volume = 4 | issue = 1| pages = 11–26 | doi=10.1080/17470215208416600| s2cid = 39060506 | doi-access = free }}</ref> and Hyman (1953),<ref>{{cite journal | last1 = Hyman | first1 = R | year = 1953 | title = Stimulus information as a determinant of reaction time | journal = Journal of Experimental Psychology | volume = 45 | issue = 3| pages = 188–96 | doi=10.1037/h0056940 | pmid=13052851| s2cid = 17559281 }}</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

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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 | first1 = J. H. | last2 = Liu | first2 = Y. | last3 = Tsimhoni | first3 = O. | year = 2010 | title = Investigation of driver performance with night-vision and pedestrian-detection systems—Part 2: Queuing network human performance modeling | journal = IEEE Transactions on Intelligent Transportation Systems| volume = 11 | issue = 4| pages = 765–772 | doi=10.1109/tits.2010.2049844| s2cid = 17275244 }}</ref> space operations,<ref name=":2">McCarley, J. S., Wickens, C. D., Goh, J., & Horrey, W. J. (2002, September). A computational model of attention/situation awareness. In ''Proceedings of the Human Factors and Ergonomics Society Annual Meeting'' (Vol. 46, No. 17, pp. 1669-1673). SAGE Publications.</ref> manufacturing,<ref>{{cite journal | last1 = Baines | first1 = T. S. | last2 = Kay | first2 = J. M. | year = 2002 | title = Human performance modelling as an aid in the process of manufacturing system design: a pilot study | journal = International Journal of Production Research | volume = 40 | issue = 10| pages = 2321–2334 | doi=10.1080/00207540210128198| s2cid = 54855742 }}</ref> user experience/user interface (UX/UI) design,<ref name="Carolan, T. 2000, pp. 650-653"/> etc. and have been used to model human-system interactions both simple and complex.

== Model Categories ==

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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" /> FittFitts'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|journal=Ergonomics|volume=14|issue=2|pages=293–305|doi=10.1080/00140137108931246|issn=0014-0139|pmid=5093722}}</ref><ref>{{Cite journal|lastlast1=Drury|firstfirst1=C. G.|last2=Daniels|first2=E. B.|date=1975-07-01|title=Performance Limitations in Laterally Constrained Movements|journal=Ergonomics|volume=18|issue=4|pages=389–395|doi=10.1080/00140137508931472|issn=0014-0139}}</ref><ref>{{Cite journal |doi = 10.1109/TSMC.1987.4309061|title = Self-Paced Path Control as an Optimization Task|journal = IEEE Transactions on Systems, Man, and Cybernetics|volume = 17|issue = 3|pages = 455–464|year = 1987|last1 = Drury|first1 = Colin G.|last2 = Montazer|first2 = M. Ali|last3 = Karwan|first3 = Mark H.|s2cid = 10648877}}</ref> and later on rediscovered in the context of human-computer interaction by Accott & Zhai (1997, 1999).<ref>{{Cite journalbook|lastlast1=Accot|firstfirst1=Johnny|last2=Zhai|first2=Shumin|date=1997-01-01|title=Beyond Fitts' Law: Models for Trajectory-based HCI Tasks|journal=Proceedings of the ACM SIGCHI Conference on Human Factorsfactors in Computingcomputing systems |chapter=Beyond Fitts' law Systems|date=1997-01-01|series=CHI '97|___location=New York, NY, USA|publisher=ACM|pages=295–302|doi=10.1145/258549.258760|isbn=0897918029|s2cid=53224495}}</ref><ref>{{Cite journalbook|lastlast1=Accot|firstfirst1=Johnny|last2=Zhai|first2=Shumin|date=1999-01-01|title=Performance EvaluationProceedings of Inputthe DevicesSIGCHI conference on Human factors in Trajectory-basedcomputing Tasks:systems Anthe ApplicationCHI ofis the Steeringlimit Law|journal=Proceedings- ofCHI the'99 SIGCHI|chapter=Performance Conferenceevaluation onof Humaninput Factorsdevices in Computingtrajectory-based tasks Systems|seriesdate=CHI '991999-01-01|___location=New York, NY, USA|publisher=ACM|pages=466–472|doi=10.1145/302979.303133|isbn=0201485591|s2cid=207247723}}</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 =http://andrewd.ces.clemson.edu/research/vislab/docs/MDGD-HF-2006.pdf | 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 | s2cid = 686156 }}</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 | 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 | s2cid = 10022486 }}</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 | 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 | s2cid = 12313971 }}</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 | journal = Human-Computer Interaction | volume = 21 | issue = 2| pages = 153–197 | doi=10.1207/s15327051hci2102_1| s2cid = 6042892 }}</ref>

==== Visual Sampling ====

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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|journal = Ergonomics|date = 2012-10-01|issn = 0014-0139|pmid = 22809389|pages = 1180–1204|volume = 55|issue = 10|doi = 10.1080/00140139.2012.697583|firstfirst1 = Cheng-Jhe|lastlast1 = Lin|first2 = Changxu|last2 = Wu|s2cid = 8895779}}</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).|journal = Psychological Review|pages = 913–954|volume = 115|issue = 4|doi = 10.1037/a0013123|firstfirst1 = Changxu|lastlast1 = Wu|first2 = Yili|last2 = Liu|pmid=18954209|year = 2008|citeseerx = 10.1.1.606.7844}}</ref>

=== Cognition & Memory ===

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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|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|firstfirst1 = Changxu|lastlast1 = Wu|first2 = Yili|last2 = Liu|s2cid = 16004384}}</ref><ref>{{Cite journal|title = Queuing Network Modeling of a Real-Time Psychophysiological Index of Mental Workload #x2014;P300 in Event-Related Potential (ERP)|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|firstfirst1 = Changxu|lastlast1 = Wu|first2 = Yili|last2 = Liu|first3 = C.M.|last3 = Quinn-Walsh|s2cid = 6629069}}</ref>, ACT-R (Anderson, 2007; Anderson & Lebiere, 1998), and QN-ACTR (Cao & Liu, 2013).<ref>{{Cite journal|title = Queueing network-adaptive control of thought rational (QN-ACTR): An integrated cognitive architecture for modelling complex cognitive and multi-task performance|journal = International Journal of Human Factors Modelling and Simulation|date = 2013|pages = 63–86|volume = 4|issue = 1|firstfirst1 = Shi|lastlast1 = Cao|first2 = Yili|last2 = Liu|doi=10.1504/ijhfms.2013.055790}}</ref>

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" />

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 BayseanBayesian 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 ===

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{{Reflist}}

[[Category:ModelingScientific and simulationmodelling]]

[[Category:Software optimization]]