Roles of Self Goal Setting in Insight Problem Solving

Roles of Self Goal Setting in Insight Problem Solving Sachiko Kiyokawa (kiyo@isc.chubu.ac.jp) Katsuyuki Hayashi (hw08075-2030@sti.chubu.ac.jp) Department of Psychology, College of Humanities, Chubu University 1200 Matsumoto-cho, Kasugai-shi, Aichi, 487-8501, Japan Toshihiko Matsuka (matsuka.toshihiko@gmail.com) Department of Cognitive & Information Science, Chiba University 1-33, Yayoi-cho, Inage-ku, Chiba-shi, Chiba, 263-8522, Japan Abstract Previous studies have shown that emphasizing the goal state could facilitate insight problem solving (e.g. Chronicle, MacGregor, & Ormerod, 2004). In these studies, the goal states were given by the experimenters and the participants were instructed to reach them. In the present study, we investigated whether the same facilitative effect could be obtained when the participants were forced to find the goal state by themselves. We used the 6-coin problem and compared the performance between the self goal setting condition and the control condition. The results showed that the participants in the self goal setting condition could solve the problem less often than those in the control condition when they were not allowed to reach the other goal state. It, however, slightly facilitated the insight problem solving if the participants were allowed to change the goal. The results indicated that self goal setting is effective in finding emergent goals. Keywords: Insight problem solving; Goal setting, Plan Introduction Problem solving is defined as an activity in which one tries to fill the gap between the initial and the goal state. It is well known that we adopt some heuristics in order to solve the problems. The hill climbing heuristic is one of the most common ones. It is the way of selecting operators so that the distance between the present state and the goal states can be minimized. To apply the heuristic, we need to know or at least to infer what goal state is. Although no one denies that goal plays a critical role in problem solving, it seems remain a matter of debate what roles the goal plays. In the present study, we investigate what roles the goal plays in insight problem solving. Importance of Goals in Insight Problem Solving Although what processes underlies insight problem solving is still open (e.g. special process view vs. business as usual view), there is agreement that the goal plays an important role in insight problem solving. Kaplan and Simon (1990) applied the information processing framework to understand the process of insight problem solving. They argued that one uses some heuristics to narrow the problem space. MacGregor, Ormerod, and Chronicle (2001) have proposed the progress monitoring theory. They argued that hill climbing heuristic underlies the selection of moves to solve the nine-dot problem. Ormerod, MacGregor, and Chronicle (2002) applied the theory to the 8-coin problem. Hiraki and Suzuki (1998) proposed the dynamic constraint relaxation theory to explain the processes of insight problem solving. They hypothesized three types of constraints working during insight problem solving: object- level, relational, and goal. The object-level constraint is our natural tendency to encode objects at a basic level, although there are numerous other ways of interpretations. The relational constraint is a tendency to choose specific relations among innumerable alternatives. The word “relation” is defined as the manner in which objects are related to each other. The goal constraint is the ideal image, which provides feedback to the other two constraints by evaluating a match between the present and the desired states. They suggested that these constraints create an impasse at the earlier stage during insight problem solving and the incremental relaxation of the constraints driven by failures probabilistically causes qualitative transitions. Wajima, Abe, and Nakagawa (2008) proposed the chaotic neural network model of the insight problem solving. Their model was implemented the goal orienting mechanism by which the model selects operators to minimize the gap between the present and the goal state. By comparing the models with and without the goal-orienting mechanism, they showed that the goal-orienting mechanism is necessary. Effects of Goals on Insight Problem Solving The models mentioned above hypothesized that the goal plays a role as a criterion in evaluating the current states. When the goal state is explicitly shown, one can evaluate the present state easier and more accurately than when not. It can be expected that emphasizing the goal state facilitates the insight problem solving. Suzuki, Miyazaki, and Hiraki (1999) examined whether emphasizing the goal state could be effective in solving the insight problem using the T-puzzle. The task was to arrange the four pieces such that they formed a T- shape. The goal state is essentially included and is explicitly shown in the original task instruction. In order to emphasize the goal state, they provided the T-shape template with the participants and asked them to match the pieces to it. The results showed that the solution rate in the template condition was higher than that in the control condition. It implied that reinforcing the goal constraint can be effective in insight problem solving.