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 Fitts' 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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==== 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'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" /> Fitts' 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|last1=Drury|first1=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|last1=Accot|first1=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 Systemssystems |chapter=Beyond Fitts' law |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|last1=Accot|first1=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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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]]