Simulating Human Heuristic Problem Solving: A Study by Combining ACT-R and fMRI Brain Image

  • Rifeng Wang
  • Jie Xiang
  • Haiyan Zhou
  • Yulin Qin
  • Ning Zhong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5819)


In this paper, we present an investigation on heuristics retrieval in human problem solving by combining the computational cognitive model ACT-R (Adaptive Control of Thought-Rational) and advanced fMRI (functional Magnetic Resonance Imaging) brain imaging technique. As a new paradigm, 4*4 Sudoku is developed to facilitate this study, in which seven heuristics that can be classified into 3 groups are designed to solve two types of tasks: simple and complex ones. The cognitive processes of the two types of 4*4 Sudoku tasks are explored based on the outputs of ACT-R model. This study shows that several key elements take important roles in the retrieval of heuristics, including the ways of problem presentation, complexity of heuristics and status of goal. The fitness of model prediction to real participants’ data on behavior and BOLD (Blood Oxygenation Level-Dependent) response in five predefined brain regions illustrates that our hypotheses and results are acceptable. This work is a significant step towards tackling the puzzle of the heuristics retrieval in human brain.


Procedural Knowledge Bold Response Declarative Knowledge Visual Module Brain Imaging Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Anderson, J.R., Bothell, D., Byrne, M.D., et al.: An Integrated Theory of Mind. Psychological Review 111(4), 1036–1060 (2004)CrossRefGoogle Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Anderson, J.R., Qin, Y.L., Jung, K.J., et al.: Information-Processing Modules and Their Relative Modality Specificity. Cognitive Psychology 54(3), 185–217 (2007)CrossRefGoogle Scholar
  4. 4.
    Anderson, J.R.: How Can the Human Mind Occur in the Physical Universe? Oxford University Press, USA (2007)CrossRefGoogle Scholar
  5. 5.
    Anderson, J.R.: Using Brain Imaging to Guide the Development of a Cognitive Architecture. In: Gray, W.D. (ed.) Integrated Models of Cognitive Systems, pp. 49–62. Oxford University Pres, Oxford (2007)Google Scholar
  6. 6.
    Anderson, J.R., Ficham, J.M., Qin, Y.L., et al.: A Central Circuit of the Mind. Trends in Cognitive Sciences 12(4), 136–143 (2008)CrossRefGoogle Scholar
  7. 7.
    Laha, D., Sarin, S.C.: A Heuristic to Minimize Total Flow Time in Permutation Flow Shop. Omega 37(3), 734–739 (2009)CrossRefGoogle Scholar
  8. 8.
    Newell, A., Simon, H.A.: Human Problem Solving. Prentice-Hal, Englewood Cliffs (1972)Google Scholar
  9. 9.
    McCarthy, J.: From Here to Human-Level AI. Artificial Intelligence 171(18), 1174–1182 (2007)CrossRefGoogle Scholar
  10. 10.
    Qin, Y.L., Sohn, M.H., Anderson, J.R., et al.: Predicting the Practice Effects on the Blood Oxygenation Level-Dependent (BOLD) Function of fMRI in a Symbolic Manipulation Task. PNAS (Proceedings of the National Academy of Sciences of the United States of America) 100(8), 4951–4956 (2003)CrossRefGoogle Scholar
  11. 11.
    Qin, Y.L., Bothell, D., Anderson, J.R.: ACT-R meets fMRI. In: Zhong, N., Liu, J., Yao, Y., Wu, J., Lu, S., Li, K. (eds.) Web Intelligence Meets Brain Informatics. LNCS (LNAI), vol. 4845, pp. 205–222. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Renaud, J., Boctor, F.F., Ouenniche, J.: A Heuristic for the Pickup and Delivery Traveling Salesman Problem. Computers and Operations Research 27(9), 905–916 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Simon, H.A.: Search and Reasoning in Problem Solving. Artificial Intelligence 21, 7–29 (1983)CrossRefGoogle Scholar
  14. 14.
    Simon, H.A.: The Information-Processing Theory of Mind. American Psychologist 50(7), 507–508 (1995)CrossRefGoogle Scholar
  15. 15.
    Stocco, A., Anderson, J.R.: Endogenous Control and Task Representation: An fMRI Study in Algebraic Problem Solving. Journal of Cognitive Neuroscience 20(7), 1300–1314 (2008)CrossRefGoogle Scholar
  16. 16.
    Zhong, N., Liu, J., Yao, Y., Wu, J., Lu, S., Qin, Y., Li, K., Wah, B.W.: Web Intelligence Meets Brain Informatics. In: Zhong, N., Liu, J., Yao, Y., Wu, J., Lu, S., Li, K. (eds.) Web Intelligence Meets Brain Informatics. LNCS (LNAI), vol. 4845, pp. 1–31. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  17. 17.
    Zhong, N., Liu, J.M., Yao, Y.Y.: Envisioning Intelligent Information Technologies through the Prism of Web Intelligence. Communications of the ACM 50(3), 89–94 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Rifeng Wang
    • 1
    • 2
  • Jie Xiang
    • 3
    • 1
  • Haiyan Zhou
    • 1
  • Yulin Qin
    • 1
    • 4
  • Ning Zhong
    • 1
    • 5
  1. 1.The International WIC InstituteBeijing University of TechnologyChina
  2. 2.Dept of Computer ScienceGuangxi University of TechnologyChina
  3. 3.College of Computer and SoftwareTaiyuan University of TechnologyChina
  4. 4.Dept of PsychologyCarnegie Mellon UniversityUSA
  5. 5.Dept of Life Science and InformaticsMaebashi Institute of TechnologyJapan

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