Impact of Cognitive Abilities and Prior Knowledge on Complex Problem Solving Performance – Empirical Results and a Plea for Ecologically Valid Microworlds

Abstract

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.

The Relation of Personality and Intelligence—What Can the Brunswik Symmetry Principle Tell Us?

“…For example, interactive and dynamic tasks allow to study more fine-grained cognitive processes on the basis of log file analysis (e.g., exploration of problems [119,120,121]). These tasks are often labelled as measurements of complex problem solving (e.g., [122,123,124]) or dynamic decision making (e.g., [125,126]) but more and more evidence is provided that these tasks should be considered as innovative intelligence tests (e.g., [29,127,128]). Therefore, further insights about the association between intelligence and personality might be gained based on these fine-grained measurement approaches (e.g., higher levels of Conscientiousness may be associated with different exploration strategies in complex problems).…”

Working Memory, Fluid Reasoning, and Complex Problem Solving: Different Results Explained by the Brunswik Symmetry

“…However, in the study of Bühner et al ( 2008 ), only working memory but not fluid reasoning significantly explained CPS variance if both abilities were considered. The opposite was found in the study of Süß and Kretzschmar ( 2018 ), in which only fluid reasoning but not working memory significantly explained variance in CPS. Zech et al ( 2017 ) argued that such inconsistent findings could be explained by different aggregation (or generalization) levels, as well as different task contents (i.e., verbal, numerical, figural), of the operationalizations used.…”

“…In detail, we were interested in whether working memory incrementally explains variance in CPS above and beyond fluid reasoning. According to the findings of Süß and Kretzschmar ( 2018 ), the CPS measure used in the present study put similar demands on the content as the CPS measure in Zech et al ’s ( 2017 ) study, that is, strong requirements concerning figural content, to a slightly lesser extent requirements concerning numerical content, and only weak requirements concerning verbal content. Therefore, it can be assumed that the findings of the present study would be consistent with those of the conditions considered in Zech et al ’s ( 2017 ) study.…”

“…The question of how complex problem solving (CPS) skills are to be integrated into the nomological network of intellectual abilities was and still is one of the most examined questions in CPS research (e.g., Dörner and Funke 2017 ; Dörner et al 1983 ; Funke and Frensch 2007 ; Kretzschmar et al 2016 ; Süß 1996 ; Süß and Kretzschmar 2018 ). While most studies have investigated the association between (subconstructs of) intelligence and CPS (for an overview, see Stadler et al 2015 ), there are only a few studies that have additionally considered working memory as a relevant cognitive ability to solve complex problems (for an overview, see Zech et al 2017 ).…”

Engineering creativity: Prior experience modulates electrophysiological responses to novel metaphors

“…Prior knowledge has been argued to be the most important predictor of successful problem-solving in a real-life context (e.g., Ceci & Liker, 1986;Süß & Kretzschmar, 2018) and has been shown to greatly facilitate language comprehension Ericsson & Kintsch, 1995;Ricks & Wiley, 2009;Spilich, Vesonder, Chiesi, & Voss, 1979;Voss, Vesonder, & Spilich, 1980;Wiley, George, & Rayner, 2018), irrespective of individuals' reading abilities (e.g., Recht & Leslie, 1988). For example, Katz and Pexman (1997) showed that knowledge about a speaker's sociocultural background (e.g., occupation) had an impact on interpreting a sentence as being metaphorical or ironic in meaning (see Katz, 2005 for a detailed discussion on the role of context in figurative language comprehension).…”