Quantifying insightful problem solving: a modified compound remote associates paradigm using lexical priming to parametrically modulate different sources of task difficulty.

Insight problem solving has been conceptualized as a dynamic search through a constrained search space, where a non-obvious solution needs to be found. Multiple sources of task difficulty have been defined that can keep the problem solver from finding the right solution such as an overly large search space or knowledge constraints requiring a change of the problem representation. Up to now, there are very few accounts that focus on different aspects of difficulty within an insight problem-solving context and how they affect task performance as well as the probability of finding a solution that is accompanied by an Aha! experience. In addition, we are not aware of any approaches investigating how knowledge constraints parametrically modulate task performance and the Aha! experience in compound remote associates (CRA) when controlling for other sources of task difficulty. Therefore, we first developed, tested, and externally validated a modified CRA paradigm in combination with lexical priming that is more likely to elicit representational change than the classical CRA tasks. Second, we parametrically estimated the effect of the knowledge constraint together with other sources of difficulty (size of the problem and search space, word length and frequency) using general linear mixed models. The knowledge constraint (and the size of the search space) was operationalized as lexical distance (measured as cosine distances) between different word pairs within this task. Our results indicate that the experimentally induced knowledge constraint still affects task performance and is negatively related to the Aha! experience when controlling for various other types of task difficulties. Finally, we will present the complete stimulus set in German language together with their statistical (i.e., item difficulty and mean solution time) and lexical properties.