Insight into the ten-penny problem: guiding search by constraints and maximization

For a long time, insight problem solving has been either understood as nothing special or as a particular class of problem solving. The first view implicates the necessity to find efficient heuristics that restrict the search space, the second, the necessity to overcome self-imposed constraints. Recently, promising hybrid cognitive models attempt to merge both approaches. In this vein, we were interested in the interplay of constraints and heuristic search, when problem solvers were asked to solve a difficult multi-step problem, the ten-penny problem. In three experimental groups and one control group (N = 4 × 30) we aimed at revealing, what constraints drive problem difficulty in this problem, and how relaxing constraints, and providing an efficient search criterion facilitates the solution. We also investigated how the search behavior of successful problem solvers and non-solvers differ. We found that relaxing constraints was necessary but not sufficient to solve the problem. Without efficient heuristics that facilitate the restriction of the search space, and testing the progress of the problem solving process, the relaxation of constraints was not effective. Relaxing constraints and applying the search criterion are both necessary to effectively increase solution rates. We also found that successful solvers showed promising moves earlier and had a higher maximization and variation rate across solution attempts. We propose that this finding sheds light on how different strategies contribute to solving difficult problems. Finally, we speculate about the implications of our findings for insight problem solving

Sleep Does Not Promote Solving Classical Insight Problems and Magic Tricks

During creative problem solving, initial solution attempts often fail because of self-imposed constraints that prevent us from thinking out of the box. In order to solve a problem successfully, the problem representation has to be restructured by combining elements of available knowledge in novel and creative ways. It has been suggested that sleep supports the reorganization of memory representations, ultimately aiding problem solving. In this study, we systematically tested the effect of sleep and time on problem solving, using classical insight tasks and magic tricks. Solving these tasks explicitly requires a restructuring of the problem representation and may be accompanied by a subjective feeling of insight. In two sessions, 77 participants had to solve classical insight problems and magic tricks. The two sessions either occurred consecutively or were spaced 3 h apart, with the time in between spent either sleeping or awake. We found that sleep affected neither general solution rates nor the number of solutions accompanied by sudden subjective insight. Our study thus adds to accumulating evidence that sleep does not provide an environment that facilitates the qualitative restructuring of memory representations and enables problem solving

Fast track to the neocortex: A memory engram in the posterior parietal cortex

Models of systems memory consolidation postulate a fast-learning hippocampal store and a slowly developing, stable neocortical store. Accordingly, early neocortical contributions to memory are deemed to reflect a hippocampus-driven online reinstatement of encoding activity. In contrast, we found that learning rapidly engenders an enduring memory engram in the human posterior parietal cortex. We assessed microstructural plasticity via diffusion-weighted magnetic resonance imaging as well as functional brain activity in an object-location learning task. We detected neocortical plasticity as early as 1 hour after learning and found that it was learning specific, enabled correct recall, and overlapped with memory-related functional activity. These microstructural changes persisted over 12 hours. Our results suggest that new traces can be rapidly encoded into the parietal cortex, challenging views of a slow-learning neocortex