Exploring brain activation patterns during heuristic problem solving using clustering approach

  • Authors:

  • Dazhong Liu

  • Affiliations:

  • International WIC Institute, Beijing University of Technology, Beijing, China and School of Mathematics and Computer Science, Hebei University, Baoding, China

  • Venue:
  • AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
  • Year:
  • 2011

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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 Kmeans 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.