Cognitive mechanisms of insight problem-solving and its computational model

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This year, we empirically explored individual differences in insight problem-solving and theoretically contrasted our approach with others.Firstly, we analyzed sources of individual differences in insight problem-solving, using a geometric puzzle. According to our theory there are three sources of individual differences: the initial strengths of constraints, the evaluation of problem-solving attempts, and the learning rate that updates the strength value of each constraint. In order to validate these predictions, we compared the subjects who could solve a geometric insight problem within 10 minutes with those who could not. A microscopic analysis of the solution processes and rating task revealed that the initial strengths of the constraints and the adequacy of evaluation are different between the two, but their learning rates are not. These results suggest that insightful problem-solvers try non-standard attempts more often even in the initial phase of their problem-solving and evaluate their attempts more precisely. The results were discussed in contrast with other approaches to insight.Secondly, we reviewed frameworks such as standard problem-solving, memory retrieval, problem-space, opportunistic, and constraint approaches to insight. The important findings and implications obtained from these approaches are to develop a theory that does not involve insight-specific mechanisms, to explain time-sensitiveness to useful information, and to incorporate to a theory a mechanism for the feedbacks from problem-solving attempts. Theoretical analysis revealed that our theory gives coherent accounts to these findings, while other approaches do not. We discussed further issues for theories of insight such as extensions to other creative thinking, conscious/unconscious nature of insight, and possibilities of neurological exploration.