F Marchak | PubMed Health

PubMed2000

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: materials, strategies, knowledge characteristics, and goals. A study was conducted to examine the effects of altering aspects of two of these task categories 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 cost of running a steam generation facility by adjusting steam generation levels and flows. One version was cast in the form of a dynamic, computer-based simulation that provided immediate feedback on flows, costs, and constraint violations, thus incorporating key variable dynamics of the problem context. The other version was cast as a static computer-based model, with no dynamic components, cost feedback, or constraint checking. Experts performed better than the other groups across material conditions, and, when required, the presentation of the goal assisted the experts more than the other groups. The static group generated richer protocols than the dynamic group, but the dynamic group solved the problem in significantly less time. Little effect of feedback was found for intermediates, and none for novices. We conclude that demonstrating differences in performance in this task requires different materials than explicating underlying knowledge that leads to performance. We also conclude that substantial knowledge is required to exploit the information yielded by the dynamic form of the task or the explicit solution goal. This simple model can help to identify the contextual factors that influence elicitation and specification of knowledge, which is essential in the engineering of joint cognitive systems.

Cognitive Science↗

PubMed1988

Practice effects in backward masking.

In two experiments we demonstrate that much larger practice effects occur in a backward masking paradigm where patterned masks are used than in similar visual processing paradigms, such as lateral masking and whole report. In additional experiments we examine four possible explanations for the large practice effects: increased familiarity with the paradigm in general, learning about the targets, learning about the masks, and enhanced sensory processing. Because of failure to observe similar practice effects in related paradigms not involving backward masking and because of the sustained nature of the improvement, we reject the first explanation as a source of practice effect. Experiment 3 allowed us to reject target learning as a source of improvement as well; target sets were switched at the end of training, but no decrement in performance was observed. In Experiment 4, mask sets were switched at the end of training, revealing a significant decrement in performance. Learning about the specific masks, then, does contribute to the observed improvement. However, it is responsible for only about one third of the overall improvement in performance. The final experiment provides evidence that the residual improvement is due to enhanced sensory processing. In that experiment, training on backward masking led to a lowered threshold in a two-flash paradigm but not to a significant change in whole-report performance.

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