A Chaotic Neural Network Model of Insightful Problem Solving and the Generation Process of Constraints

The solution of an insightful problem needs a drastic change from the "impasse" to the "insight" stage. It is assumed that in this type of a problem, solvers encounter the impasse stage because of special "constraints" like common sense. Abe, Wajima, and Nakagawa (2003) proposed a model of insight problem solving using a chaotic neural network. The model successfully simulates an insight problem. Based on this system, we developed a new model to explain the generation process of new constraints. We hypothesized that once people have solved a problem using insight, the experience of the insight generates new constraints. In order to verify the above hypothesis, we conducted a psychological experiment and executed a computational simulation of the model. In the experiment, participants were instructed to solve the two pictorial puzzles, one was an insight problem and the other was a non-insight problem. The experimental results showed that the solution of the insight problem generated a new constraint, while inhibiting the solution of non-insight problem. We constructed a new model that represents a reinforcement state after solving the insightful problem and several simulations were executed. The result of model's simulation showed a close similarity with the experimental result. The model successfully simulated the process of generation of new constraints.