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Metacognitive and computational aspects of chance discovery

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Abstract

Chance discovery is concerned with events or situations that affect human decision making; such events or situations are viewed as opportunities or risks. Perspectives are mental representations that describe partial knowledge of a task domain (cognitive perspective) as well as knowledge about other participants (social perspectives). Based on verbal protocols and a computational model of these protocols, it is argued that perspective taking is a suitable strategy to achieve chance discovery. Therefore the cognitive mechanisms underlying this strategy have been investigated and the results implicate metacognition as necessary requirement to achieve chance discovery.

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References

  1. Brown, A., “Metacognition, Executive Control, Self-Regulation and Other More Mysterious Mechanisms,” inMetacognition, Motivation and Human Performance (Weinert, F. and Kluwe, R., eds.), Lawrence Erlbaum Associates, Hillsdale, pp. 65–116, 1987

    Google Scholar 

  2. Flavell, J., “Metacognitive Aspects of Problem Solving,” inThe Nature of Intelligence (Resnick, L., ed.), Lawrence Elbaum Associates, Hillsdale, 1976.

    Google Scholar 

  3. Gavelek, J. and Raphael, T., “Metacognition, Instruction, and the Role of Questioning Activities,” inMetacognition, Cognition and Human Performance (Forst-Pressley, D., MacKinnon, G. and Waller, T., eds.), Academic Press, 1985.

  4. Hammond, K.,Case-Based Planning, Academic Press, 1989.

  5. Horiguchi, T. and Hirashima, T., “The Role of Counterexamples in Discovery Learning Environment: Awareness of the Chance for Learning,” in On-site Notes ofThe First International Workshop on Chance Discovery, 2001.

  6. Klayman, J. and Ha, Y., “Confirmation, Disconfirmation, and Information in Hypothesis Testing,”Psychological Review, 94, pp. 211–228, 1987.

    Article  Google Scholar 

  7. Nakakoji, K., Ohira, M. and Yamamoto, Y., “Computational Support for Collective Creativity,”Knowledge-based Systems Journal, Elsevier Science,13, 7–8, pp. 451–458, 2000.

    Article  Google Scholar 

  8. Oehlmann, R., “Metacognitive Attention: Reasoning about Strategy Selection” inProc. of the 17th Annual Conference of the Cognitive Science Society, 1995 (Moore, J. and Lehman, J., eds.), Lawrence Erlbaum Associates, Hillsdale, pp. 66–71, 1995.

    Google Scholar 

  9. Oehlmann, R., “A Computational Model of Reasoning as Socially-Constructed Process,” inProc. of the Seventh Pacific Rim International Conference on Artificial Intelligence, 2000 (accepted for publication).

  10. Ohira, M., Yamamoto, K. and Nakakoji, K., “EVIDII; An Environment That Supports Understanding “Differences” among People,”International Conference on Cognitive Science ’99(ICCS99), pp. 466–471, 1999.

  11. Ohira, M., Yamamoto, K. and Nakakoji, K., “EVIDII: A System That Supports Mutual Understanding through Visualizing Differences of Impressions,”Journal of Information Processing Society of Japan, Special Issue on Knowledge and Information Sharing (in Japanese), 41, 10, pp. 2814–2826, 2000.

    Google Scholar 

  12. Ohlson, St., “Restructuring Revisited. I. Summary and Critique of the Gestalt Theory of Restructuring and Insight,”Scandinavian Journal of Psychology, 25, pp. 65–78, 1984.

    Article  Google Scholar 

  13. Ohsawa, Y., “Chance Discoveries for Making Decisions in Complex Real World,”New Generation Computing, 20, 2, 2002

  14. Sternberg, R., “A Three-facet Model of Creativity,” The Nature of Creativity (Sternberg, R., ed.), Cambridge University Press, pp. 125–147, 1988.

  15. Sugimoto, M., Hori, K. and Ohsuga, S., “A System for Supporting Researcher’s Creativity by Visualizing Different Viewpoints,”Workshop on Creativity and Cognition, Loughborough, 1996.

  16. Wong, B., “Self-questioning Instructional Research. A Review,”Review of Educational Research, 55, pp. 227–268, 1985.

    Google Scholar 

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Correspondence to Ruediger Oehlmann.

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Ruediger Oehlmann, Ph.D.: He is a senior lecturer in the Cognitive Science Laboratory, School of Computing and Information Systems, Kingston University, London. He received his degrees in Mathematics, Computer Science and Psychology. His doctoral thesis describes a model of discovery learning, which he has extensively tested using psychological experiments as well as computer programs. His current research interests include perspective taking, creativity, chance discovery and collaborative work in design domains. He is a member of the British Computer Society and the Cognitive Science Society.

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Oehlmann, R. Metacognitive and computational aspects of chance discovery. NGCO 21, 3–12 (2003). https://doi.org/10.1007/BF03042321

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  • DOI: https://doi.org/10.1007/BF03042321

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