Neural bases for basic processes in heuristic problem solving: Take solving Sudoku puzzles as an example.
Newell and Simon postulated that the basic steps in human problem-solving involve iteratively applying operators to transform the state of the problem to eventually achieve a goal. To check the neural basis of this framework, the present study focused on the basic processes in human heuristic problem-solving that the participants identified the current problem state and then recalled and applied the corresponding heuristic rules to change the problem state. A new paradigm, solving simplified Sudoku puzzles, was developed for an event-related functional magnetic resonance imaging (fMRI) study in problem solving. Regions of interest (ROIs), including the left prefrontal cortex, the bilateral posterior parietal cortex, the anterior cingulated cortex, the bilateral caudate nuclei, the bilateral fusiform, as well as the bilateral frontal eye fields, were found to be involved in the task. To obtain convergent evidence, in addition to traditional statistical analysis, we used the multivariate voxel classification method to check the accuracy of the predictions for the condition of the task from the blood oxygen level dependent (BOLD) response of the ROIs, using a new classifier developed in this study for fMRI data. To reveal the roles that the ROIs play in problem solving, we developed an ACT-R computational model of the information-processing processes in human problem solving, and tried to predict the BOLD response of the ROIs from the task. Advances in human problem-solving research after Newell and Simon are then briefly discussed.
The International WIC Institute, Beijing University of Technology, Beijing, China.|Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China.|Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA.
Yulin
Y
Qin
The International WIC Institute, Beijing University of Technology, Beijing, China.|Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China.|College of Computer and Software, Taiyuan University of Technology, Taiyuan, Shanxi, China.
Jie
J
Xiang
The International WIC Institute, Beijing University of Technology, Beijing, China.|Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China.|Department of Computer Engineering, Guangxi University of Technology, Liuzhou, Guangxi, China.
Rifeng
R
Wang
The International WIC Institute, Beijing University of Technology, Beijing, China.|Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China.
Haiyan
H
Zhou
Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China.|Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.
Kuncheng
K
Li
The International WIC Institute, Beijing University of Technology, Beijing, China.|Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China.|Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, Japan.
Ning
N
Zhong
classification in brain imaging; cognitive architecture; fMRI; problem solving