Factors influencing analysis of complex cognitive tasks: A framework and example from industrial process control

We propose that considering four categories of task factors can facilitate knowledge elicitation efforts in the analysis of complex cognitive tasks: m aterials, strategies, knowledge characteristics, and goals. A study was con ducted to examine the effects of altering aspects of two of these task cate gories on problem-solving behavior across skill levels: materials and goals . Two versions of an applied engineering problem were presented to expert, intermediate, and novice participants. Participants were to minimize the co st of running a steam generation facility by adjusting steam generation lev els and flows. One version was cast in the form of a dynamic, computer-base d simulation that provided immediate feedback on flows, costs, and constrai nt violations, thus incorporating key variable dynamics of the problem cont ext. The other version was cast as a static computer-based model, with no d ynamic components, cost feedback, or constraint checking. Experts performed better than the other groups across material conditions, and, when require d, the presentation of the goal assisted the experts more than the other gr oups. The static group generated richer protocols than the dynamic group, b ut the dynamic group solved the problem in significantly less time. Little effect of feedback was found for intermediates, and none for novices. We co nclude that demonstrating differences in performance in this task requires different materials than explicating underlying knowledge that leads to per formance. We also conclude that substantial knowledge is required to exploi t the information yielded by the dynamic form of the task or the explicit s olution goal. This simple model can help to identify the contextual factors that influence elicitation and specification of knowledge, which is essent ial in the engineering of joint cognitive systems.