Human performance modeling: Difference between revisions

Revision as of 16:21, 6 August 2017

Revision as of 12:51, 3 December 2017

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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=httphttps://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=httphttps://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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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 = httphttps://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>