Content deleted Content added
m →Human Performance Models: task, replaced: | last1 = Lim | first1 = J. H. | last2 = Liu | first2 = Y. | last3 = Tsimhoni | first3 = O. | year = 2010 | title = Investigation of driver performance with night-vision and |
m clean up, replaced: IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans → IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, typo(s) fixed: Wu’s → Wu's |
||
Line 80:
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
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>
Line 134:
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
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" />
| |||