Human performance modeling: Difference between revisions

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Horrey and Wickens (2003) consider the questions: To what extent will a secondary task interfere with driving performance, and does it depend on the nature of the driving and on the interface presented in the second task? Using a model based on ''[[Workload|multiple resource theory]]'' (Wickens, 2002, 2008; Navon & Gopher, 1979), which proposes that there are several loci for multiple-task interference (the stages of processing, the codes of processing, and [[Modality (human–computer interaction)|modalities]]), the researchers suggest that cross-task interference increases proportional to the extent that the two tasks use the same resources within a given dimension: Visual presentation of a read-back task should interfere more with driving than should auditory presentation, because driving itself makes stronger demands on the visual modality than on the auditory.<ref name=":1" />

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 applicatonapplication 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 = http://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>