Impact of Cognitive Abilities and Prior Knowledge on Complex Problem Solving Performance - Empirical Results and a...

The original aim of complex problem solving (CPS) research was to bring the cognitive demands of complex real-life problems into the lab in order to investigate problem solving behavior and performance under controlled conditions. Up until now, the validity of psychometric intelligence constructs has been scrutinized with regard to its importance for CPS performance. At the same time, different CPS measurement approaches competing for the title of the best way to assess CPS have been developed. In the first part of the paper, we investigate the predictability of CPS performance on the basis of the Berlin Intelligence Structure Model and Cattell's investment theory as well as an elaborated knowledge taxonomy. In the first study, 137 students managed a simulated shirt factory (Tailorshop; i.e., a complex real life-oriented system) twice, while in the second study, 152 students completed a forestry scenario (FSYS; i.e., a complex artificial world system). The results indicate that reasoning - specifically numerical reasoning (Studies 1 and 2) and figural reasoning (Study 2) - are the only relevant predictors among the intelligence constructs. We discuss the results with reference to the Brunswik symmetry principle. Path models suggest that reasoning and prior knowledge influence problem solving performance in the Tailorshop scenario mainly indirectly. In addition, different types of system-specific knowledge independently contribute to predicting CPS performance. The results of Study 2 indicate that working memory capacity, assessed as an additional predictor, has no incremental validity beyond reasoning. We conclude that (1) cognitive abilities and prior knowledge are substantial predictors of CPS performance, and (2) in contrast to former and recent interpretations, there is insufficient evidence to consider CPS a unique ability construct. In the second part of the paper, we discuss our results in light of recent CPS research, which predominantly utilizes the minimally complex systems (MCS) measurement approach. We suggest ecologically valid microworlds as an indispensable tool for future CPS research and applications.