Numerous studies of insight problem solving are focused on both the control and storage systems of working memory. We obtained contradictory data about how working memory systems are involved in insight problem solving process. We argue that measuring the dynamics of the control system and storage systems through the course of problem solving can provide a more refined view on the processes involved, as a whole, and explain the existing controversies. We theorize that specific insight mechanisms require varying working memory capacities at different stages of the problem solving process. Our study employed a dual task paradigm to track the dynamics of working memory systems load during problem solving by measuring the reaction time in a secondary probe-task during different stages of problem solving. We varied the modality (verbal, visual) and the complexity of the probe-task during insight and non-insight problem solving. The results indicated that the dynamics of working memory load in insight problems differs from those in non-insight problems. Our first experiment shows that the complexity of the probe-task affects overall probe-task reaction times in both insight and non-insight problem solving. Our second experiment demonstrates that the solution of a non-insight problem is primarily associated with the working memory control system, while insight problems rely on relevant storage systems. Our results confirm that insight process requires access to various systems of working memory throughout the solution. We found that working memory load in non-insight problems increases from stage to stage due to allocation of the attentional control resources to interim calculations. The nature of the dynamics of working memory load in insight problems remains debatable. We claim that insight problem solving demands working memory storage during the entire problem solving process and that control system plays an important role just prior to the solution.

Insight problems—as a type of ill-defined problems—are often solved without an articulate plan, and finding their solution is accompanied by the Aha! experience (positive feeling from suddenly finding a solution). However, the solution of such problems can also be guided, for example, by expectations in terms of criteria for achieving the goal. We hypothesize that adjusting the expectation accuracy based on the reward prediction error (discrepancy between the reward and its prediction) affects the strength of affective components of the Aha! experience (pleasure and surprise), allowing to learn how to solve similar problems. We manipulated expectation accuracy by varying the similarity in problem solution principle and structure in a short learning set. Each set was followed by a critical problem where both the structure and solution principle were changed (except for control set). Subjective feelings, solution time, and expectation were measured after each problem. The results revealed that problems with similarities become more expected at the end of the set and their solution time is decreased. However, the critical problem featured a rapid increase in pleasure and surprise and decrease in expectedness only in the condition where both the solution principle and structure were expected, suggesting that problem structure is a key feature determining expectedness in insight problem solving. The Aha! experience is not an epiphenomenon; it plays a role in learning of problem solving through adjusting expectations.

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