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>

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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, 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 book|last1=Accot|first1=Johnny|last2=Zhai|first2=Shumin|title=Proceedings of the ACM SIGCHI Conference on Human factors in computing systems |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 book|last1=Accot|first1=Johnny|last2=Zhai|first2=Shumin|title=Proceedings of the SIGCHI conference on Human factors in computing systems the CHI is the limit - CHI '99 |chapter=Performance evaluation of input devices in trajectory-based tasks |date=1999-01-01|series=CHI '99|___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 ===