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With the advent of computers in the experimental labs, dynamic systems have become a new tool for research on problem solving and decision making. A short review of this research is given and the main features of these systems (connectivity and dynamics) are illustrated. To allow systematic approaches to the influential variables in this area, two formal frameworks (linear structural equations and finite state automata) are presented. Besides the formal background, the article sets out how the task demands of system (...) |
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Real causal systems are complicated. Despite this, causal learning research has traditionally emphasized how causal relations can be induced on the basis of idealized events, i.e. those that have been mapped to binary variables and abstracted from time. For example, participants may be asked to assess the efficacy of a headache-relief pill on the basis of multiple patients who take the pill (or not) and find their headache relieved (or not). In contrast, the current study examines learning via interactions with (...) |
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Many situations require people to acquire knowledge about, and learn how to control, complex dynamic systems of inter-connected variables. Numerous studies have found that most problem solvers are unable to acquire complete knowledge of the underlying structure of a system through an unguided exploration of the system variables; additional instruction or guidance is required. This paper examines whether providing structural information following an unguided exploration also improves control performance, and the extent to which any improvements are moderated by problem solvers’ (...) |
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This article assesses how people reason with categories whose features are related in causal cycles. Whereas models based on causal graphical models have enjoyed success modeling category-based judgments as well as a number of other cognitive phenomena, CGMs are only able to represent causal structures that are acyclic. A number of new formalisms that allow cycles are introduced and evaluated. Dynamic Bayesian networks represent cycles by unfolding them over time. Chain graphs augment CGMs by allowing the presence of undirected links (...) |
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Talents that selectively facilitate the acquisition of high levels of skill are said to be present in some children but not others. The evidence for this includes biological correlates of specific abilities, certain rare abilities in autistic savants, and the seemingly spontaneous emergence of exceptional abilities in young children, but there is also contrary evidence indicating an absence of early precursors of high skill levels. An analysis of positive and negative evidence and arguments suggests that differences in early experiences, preferences, (...) |
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The present study discusses findings that replicate and extend the original work of Burns and Vollmeyer (2002), which showed that performance in problem solving tasks was more accurate when people were engaged in a non-specific goal than in a specific goal. The main innovation here was to examine the goal specificity effect under both observation-based and conventional action-based learning conditions. The findings show that goal specificity affects the accuracy of problem solving in the same way, both when the learning stage (...) |
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The current study had three goals: to investigate strategies, tactics, and errors as predictors of success and failure under uncertainty following the dynamic decision making and complex problem solving framework; to use observation and to examine its reliability and potential as a data collection method when using microworlds; and to investigate the applicability and validity of a microworld developed in the West, to an Asian sample. One hundred three participants in the Philippines took the role of fire chief in the (...) |
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Using the Chinese Ring Puzzle (Kotovsky & Simon, Citation1990; P. J. Reber & Kotovsky, Citation1997), we studied the effect on rule discovery of having to plan actions or not in order to reach a goal state. This was done by asking participants to predict legal moves as in implicit learning tasks (Experiment 1) and by asking participants to make legal moves as in problem-solving tasks (Experiment 2). Our hypothesis was that having a specific goal state to reach has a dual (...) |
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The term conceptual simulation refers to a type of everyday reasoning strategy commonly called “what if” reasoning. It has been suggested in a number of contexts that this type of reasoning plays an important role in scientific discovery; however, little direct evidence exists to support this claim. This article proposes that conceptual simulation is likely to be used in situations of informational uncertainty, and may be used to help scientists resolve that uncertainty. We conducted two studies to investigate the relationship (...) |
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This study examined whether experience with different computer-based complex problem situations would improve complex problem solving competence in an unknown problem situation. Therefore, university students took part in a control group study. They were trained in five different complex problem situations for up to 7 hr, and their performance was tested in a sixth complex problem situation. The data analyses revealed that the training influenced the CPS process of knowledge acquisition. However, the CPS process of knowledge application was not impacted (...) |
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Multiple goals balancing is an important but not yet fully validated dimension of complex problem solving (CPS). The present study used process data to explore how solvers clarify goals, set priorities, and balance conflicting goals. We extracted behavioral indicators of goal pursuit from the log data of 3,201 students on the third subtask of the “Ticket” task in the PISA 2012 CPS test. Cluster analysis was used to identify 10 groups that varied in goal pursuit behavior. Logistics and least-squares regression (...) |
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