Human performance modeling: Difference between revisions

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Workload: Corrected typo. "What exactly is mean by attention" should be "What exactly is meant by attention" to be grammatically correct.
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==== [[Workload]] ====
Although an exact definition or method for measurement of the construct of workload is debated by the human factors community, a critical part of the notion is that human operators have some capacity limitations and that such limitations can be exceeded only at the risk of degrading performance. For physical workload, it may be understood that there is a maximum amount that a person should be asked to lift repeatedly, for example. However, the notion of workload becomes more contentious when the capacity to be exceeded is in regard to attention - what are the limits of human attention, and what exactly is meanmeant by attention? Human performance modeling produces valuable insights into this area.<ref name=":1" />
 
Byrne and Pew (2009) consider an example of a basic workload question: "To what extent do task A and B interfere?" These researchers indicate this as the basis for the ''[[psychological refractory period]]'' (PRP) paradigm. Participants perform two choice reaction-time tasks, and the two tasks will interfere to a degree - especially when the participant must react to the stimuli for the two tasks when they are close together in time - but the degree of interference is typically smaller than the total time taken for either task. The ''response selection bottleneck model'' (Pashler, 1994) models this situation well - in that each task has three components: perception, response selection (cognition), and motor output. The attentional limitation - and thus locus of workload - is that response selection can only be done for one task at a time. The model makes numerous accurate predictions, and those for which it cannot account are addressed by cognitive architectures (Byrne & Anderson, 2001; Meyer & Kieras, 1997). In simple dual-task situations, attention and workload are quantified, and meaningful predictions made possible.<ref name=":1" />