Abstract
In the present study, brain activation patterns of heuristic problem solving were investigated in the context of the puzzle Sudoku experiment by using a two-stage clustering approach. The cognitive experiment was composed of easy tasks and difficult tasks. In the two-stage clustering approach, K-means served as the data selection role in the first stage and the affinity propagation (AP) served as partition role in the second stage. Functional magnetic resonance imaging (fMRI) was used to collect the slow event related paradigm data. Simulated fMRI datasets were used to evaluate the validity of the clustering method and compare the performance of fuzzy c-means (FCM) as an alternate method in the first stage. Test results illustrated that the performance of K-means in this role was better than that of FCM. Further, the proposed method was applied to the heuristic problem solving fMRI data and the results showed that the brain activation patterns observed in the experiment exhibited compact and coherent activity mode in dealing with different cognitive tasks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Anderson, J.R., Albert, M.V., Fincham, J.M.: Tracing Problem Solving in Real Time: fMRI Analysis of the Subject-paced Tower of Hanoi. Journal of Cognitive Neuroscience 17(8), 1261–1274 (2005)
Danker, J.F., Anderson, J.R.: The roles of prefrontal and posterior parietal cortex in algebra problem solving: A case of using cognitive modeling to inform neuroimaging data. NeuroImage 35, 1365–1377 (2007)
Qiu, J., Li, H., Jou, J., Liu, J., Luo, Y., Feng, T.: Neural correlates of the “Aha” experiences: Evidence from an fMRI study of insight problem solving. CORTEX 46, 397–403 (2010)
Wang, R., Xiang, J., Zhou, H., Qin, Y., Zhong, N.: Simulating Human Heuristic Problem Solving: A Study by Combining ACT-R and fMRI Brain Image. In: Zhong, N., Li, K., Lu, S., Chen, L. (eds.) BI 2009. LNCS(LNAI), vol. 5819, pp. 53–62. Springer, Heidelberg (2009)
Xiang, J., Chen, J., Zhou, H., Qin, Y., Li, K., Zhong, N.: Using SVM to Predict High-Level Cognition from fMRI Data: A Case Study of 4*4 Sudoku Solving. In: Zhong, N., Li, K., Lu, S., Chen, L. (eds.) BI 2009. LNCS(LNAI), vol. 5819, pp. 171–181. Springer, Heidelberg (2009)
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis, pp. 189–225. John Wiley & Sons, New York (1973)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, NY (1981)
Frey, B.J., Dueck, D.: Clustering by Passing Messages between Data Points. Science 315(5814), 972–976 (2007)
Yang, J., Zhong, N., Liang, P.P., Wang, J., Yao, Y.Y., Lu, S.F.: Brain Activation Detection by Neighborhood One-class SVM. In: Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology – Workshops, pp. 47–51 (2007)
Chuang, K., Chiu, M., Lin, C.C., Chen, J.: Model-free Functional MRI Analysis Using Kohonen Clustering Neural Network and Fuzzy C-means. IEEE Transactions on Medical Imaging 18, 1117–1128 (1999)
Dimitriadou, E., Barth, M., Windischberger, C., Hornik, K., Moser, E.: A Quantitative Comparison of Functional MRI Cluster Analysis. Artificial Intelligence in Medicine 31, 57–71 (2004)
Fadili, M.J., Ruan, S., Bloyet, D., Mazoyer, B.: A Multistep Unsupervised Fuzzy Clustering Analysis of fMRI Time Series. Human Brain Mapping 10, 160–178 (2000)
Formisano, E., Martino, F.D., Valente, G.: Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning. Magnetic Resonance Imaging 26, 921–934 (2008)
Ye, J., Lazar, N.A., Li, Y.: Geostatistical Analysis in Clustering fMRI Time Series. Statistics in Medicine 28(19), 2490–2508 (2009)
Sohn, M.-h., Goode, A., Stenger, V.A., Carter, C.S., Anderson, J.R.: Competition and representation during memory retrieval: Roles of the prefrontal cortex and the posterior parietal cortex. PNAS 100, 7412–7417 (2003)
Friston, K.J., Frith, C.D., Liddle, P.F., Frackowiak, R.S.J.: Functional connectivity.: The Principal Component Analysis of Large (PET) Data sets. J. Cereb. Blood Flow Metab. 13, 5–14 (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, D. (2011). Exploring Brain Activation Patterns during Heuristic Problem Solving Using Clustering Approach. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7002. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23881-9_43
Download citation
DOI: https://doi.org/10.1007/978-3-642-23881-9_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23880-2
Online ISBN: 978-3-642-23881-9
eBook Packages: Computer ScienceComputer Science (R0)