Reasoning Involved in Control Technology

  • Jacques Sougné
Conference paper
Part of the NATO ASI Series book series (volume 116)


Problem solving in control technology is analysed in the light of the most recent reasoning theories. Divergences exist among theorists about the mechanisms involved in reasoning. Some suggest the existence of general purpose inference rules: subjects are assumed to possess a standard formal logic. Others suppose the existence of domain or context dependent inference rules: knowledge is organized in units called scripts, schemas or schemata which contain rules more or less isomorphic to logic. Finally, a third party assumes the existence of mental models: they claim that people need neither rules nor logic to produce valid inferences. My experiences using LOGO in control technology showed relative stereotypy in the way children were using the programming language. I will try to explain these biases in the light of different reasoning theories. This analysis will come to some conclusions directed to the enhancement of control technology activity LOGO style, and to the modelling of human cognition.


Control technology Learning theory LOGO Problem solving Pupil Learning Reasoning Pedagogical robotics 


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  1. Abelson, H. & Sussman, GJ. (1985). Structure and Interpretation of Computer Programs. MIT Press, Cambridge, Ma.Google Scholar
  2. Beth, E.W. & Piaget, J. (1961). Epistémologie mathématique et psychologie: essais sur les relations entre la logique formelle et la pensée réelle. PUF XIV, Paris.Google Scholar
  3. Beth, E.W., Grize, J.B., Martin, R., Matalon, B., Naess, A. & Piaget, J. (1962). Implication, formalisation et logique naturelle. PUF XVI, Paris.Google Scholar
  4. Braine, M.D.S. (1978). On the relation between the natural logic of reasoning and standard logic. Psychological Review, 85, 1–21.CrossRefGoogle Scholar
  5. Cheng, P.W. & Holyoak, K.J. (1985). Pragmatic reasoning schemas. Cognitive Psychology, 17, 391–416.CrossRefGoogle Scholar
  6. Cheng, P.W. & Holyoak, K.J. (1989). On the natural selection of reasoning theories. cognition, 33, 285–313.CrossRefGoogle Scholar
  7. Denis, B. & Sougné, J. (1990). Projet Robotique Itinérante: Rapport final. Tech. Report. Service de Technologie de l’Éducation, Direction générale de l’enseignement et de la formation.Google Scholar
  8. Drescher, G.L. (1991). Made-up Minds: A Constructivist Approach to Artificial Intelligence. MIT Press, Cambridge, Ma.zbMATHGoogle Scholar
  9. Evans, J.S.B.T. (1989). Bias in Human Reasoning: Causes and Consequences. Lawrence Erlbaum Ass., London, UK.Google Scholar
  10. Fodor, J.A. (1980). Fixation of belief and concept acquisition. In M. Piattelli-Palmarini (Ed.) Language and Learning: The Debate Between Jean Piaget and Noam Chomsky. Routledge & Kegan Paul, London, UK.Google Scholar
  11. Grigg, R.A. & Cox, J.R. (1982). The elusive thematic-materials effect in Wason’s selection task. British Journal of Psychology, 73, 407–420.CrossRefGoogle Scholar
  12. Holland, J.H., Holyoak, K.J., Nisbett, R.E. & Thagard, P.R. (1986). Induction: Processes of Inference, Learning and Discovery. MIT Press, Cambridge, Ma.Google Scholar
  13. Holyoak, K.J. & Thagard, P. (1989). Analogical mapping by constraint satisfaction. Cognitive Science. 13, 295–355.CrossRefGoogle Scholar
  14. Inhelder, B. & Piaget, J. (1955). De la logique de l’enfant à la logique de l’adolescent. PUF, Paris.Google Scholar
  15. Johnson-Laird, P.N. (1983). Mental Models: Towards a cognitive science of language, inference, and conciousness. Cambridge University Press, Cambridge, UK.Google Scholar
  16. Johnson-Laird, P.N. (1989). Mental models. In Posner, M.I. (ed.) Foundations of Cognitive Science. MIT Press, Cambridge, Ma.Google Scholar
  17. Johnson-Laird, P.N., & Byrne, M.J. (1991). Deduction. Lawrence Erlbaum Ass., London UK.Google Scholar
  18. Manktelow, K. I. & Evans, J.S.B.T. (1979). Facilitation of reasoning by realism: Effect or non-effect? British Journal of Psychology, 70, 477–488.CrossRefGoogle Scholar
  19. Miller, G.A. (1956). The magical number seven, plus or minus two. Psychological Review, Vol. 63, 81–97.CrossRefGoogle Scholar
  20. Newell, A. & Simon, H.A. (1972). Human Problem Solving. Prentice-Hall, Englewood-Cliffs, NJ.Google Scholar
  21. Newell, A. (1991). Unified Theories of Cognition. Harvard University Press, Cambridge, Ma.Google Scholar
  22. Nisbett, R. E., Fong, G.T., Lehman, D.R., & Cheng, P.W. (1987). Teaching reasoning. Science, Vol 238, 625–631.CrossRefGoogle Scholar
  23. Oakhill, J. (1982). Constructive processes in skilled and less skilled comprehenders’ memory for sentences. British Journal of Psychology, 73, 13–20.CrossRefGoogle Scholar
  24. Oakhill, J. & Johnson-Laird, P.N. (1985). The effect of belief on the spontaneous production of syllogistic conclusions. Quarterly Journal of Experimental psychology, 37, 553–570.CrossRefGoogle Scholar
  25. Piaget, J. (1975). L’équilibration des structures cognitives: problème central du développement. PUF XXXIII, Paris.Google Scholar
  26. Pollard, P. & Evans, J.S.B.T. (1987). On the Relationship between Content and Context Effects in Reasoning. American Journal of Psychology, 100, 41–60.CrossRefGoogle Scholar
  27. Rips, L. J. (1983). Cognitive Processes in Prepositional Reasoning. Psychological Review, 90, 38–71.CrossRefGoogle Scholar
  28. Rumelhart, D.E. (1980). Schemata: The building blocks of cognition. In Spiro, R.J., Bruce, B.C. & Brewer, W.F. (Eds.) Theoretical Issues in Reading Comprehension, Lawrence Erlbaum Ass., Hillsdale, NJ.Google Scholar
  29. Rumelhart, D.E., Smolensky, P., McClelland, J.L. & Hinton, G.E. (1986) Schemata and Sequential Thought Processes in PDP Models. In McClelland, J.L., Rumelhart, D.E. & The PDP Research Group. Parallel Distributed Processing. Vol. 2. MIT Press, Cambridge, Ma.Google Scholar
  30. Rumelhart, D.E. & Norman, D.A. (1988). Representation in Memory. In R.C. Atkinson, R.J. Herrnstein, G. Lindzey & R.D. Duncan Luce (Eds.) Stevens’ Handbook of Experimental Psychology; Vol. 2, Learning and Cognition, 511–587. J. Wiley & Sons, New York.Google Scholar
  31. Schank, R. C. (1982). Dynamic Memory: A theory of reminding and learning in computers and people. Cambridge University Press, Cambridge, UK.Google Scholar
  32. Sougné, J. (1989). Le micro-monde LEGO®. Université de Liège, Service de Technologie de l’Education.Google Scholar
  33. Sougné, J. (1990). LOGO-Scan: A Tool Kit To Analyse LOGO Programs. In N. Estes, J. Heene & D. Leclercq (Eds.) The Seventh International Conference on Technology and Education. Vol. 2, 313–315. CEP, Edinburgh, UK.Google Scholar
  34. Sougné, J. (1991). Analyse de projets de robotique pédagogique par LOGO-Scan. Actes du troisième congrès international sur la robotique pédagogique. CISE, Mexico.Google Scholar
  35. Sougné, J. & Blondin, C. (1992). Modelling Expertise in Hydrodynamics. Belgian national incentive program for fundamental research in artificial intelligence, Tech. Report. Université de liège, SPPS.Google Scholar
  36. Sougné, J. (in press). Modelling Physics Problem Solving with Classifier Systems. In M. Caillot (ed.) Learning electricity and electronics with advanced educational technology. NATO ASI Series F, Vol. 115, Springer-Verlag, Berlin.Google Scholar
  37. Thagard, P. & Holyoak, K.J. (1985). Discovering the Wave Theory of Sound: Inductive Inference in the Context of Problem Solving. Proceedings of the Ninth International Joint Conference on Artificial Intelligence. Morgan Kaufmann, Palo Alto, Ca.Google Scholar
  38. Thorndike, E. (1913). The Psychology of Learning. Mason-Henry, New York.Google Scholar
  39. Wason, P.C. (1966). Reasoning. In B.M. Foss (Ed.), New Horizons in Psychology 1. Penguin, Harmandsworth.Google Scholar
  40. Wason, P.C. & Shapiro, D. (1971). Natural and Contrived Experience in a Reasoning Problem. Quarterly Journal of Experimental Psychology, 23, 63–71.CrossRefGoogle Scholar
  41. Wykes, T. & Johnson-Laird, P.N. (1977). How Do Children Learn the Meaning of Verbs? Nature, 268, 326–327.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Jacques Sougné
    • 1
  1. 1.Université de Liège, Service de Technologie de l’Education (STE)LiègeBelgium

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