The Nature and Development of Scientific Reasoning: A Synthetic View


This paper presents a synthesis of what is currently known about the nature and development of scientific reasoning and why it plays a central role in acquiring scientific literacy. Science is viewed as a hypothetico-deductive (HD) enterprise engaging in the generation and test of alternative explanations. Explanation generation and test requires the use of several key reasoning patterns and sub-patterns. Reasoning at the highest level is complicated by the fact that scientific explanations generally involve the postulation of non-perceptible entities, thus arguments used in their test require sub-arguments to link the postulate under test with its deduced consequence. Science is HD in nature because this is how the brain spontaneously processes information whether it basic visual recognition, every-day descriptive and causal hypothesis testing, or advanced theory testing. The key point in terms of complex HD arguments is that if sufficient chunking of concepts and/or reasoning sub-patterns have not occurred, then one’s attempt to construct and maintain such arguments in working memory and use them to draw conclusions and construct concepts will “fall apart.” Thus, the conclusions and concepts will be “lost.” Consequently, teachers must know what students bring with them in terms of their stages of intellectual development (i.e., preoperational, concrete, formal, or post-formal) and subject-specific declarative knowledge. Effective instruction mirrors the practice of science where students confront puzzling observations and then personally participate in the explanation generation and testing process – a process in which some of their ideas are contradicted by the evidence and by the arguments of others.


brain functioning intellectual development instruction reasoning scientific literacy 


  1. Anderson, J.R. (1980). Cognitive psychology and its implications. San Francisco: Freeman. Google Scholar
  2. Baddeley, A. (1995). Working memory. In M.S. Gazzaniga (Ed.), The cognitive neurosciences. Cambridge, MA: MIT Press. Google Scholar
  3. Biela, A. (1993). Psychology of analogical inference. Stuttgart, Germany: Hirzel Verlag. Google Scholar
  4. Cavallo, A.M.L. (1996). Meaningful learning, reasoning ability, and students’ understanding and problem solving of topics in genetics. Journal of Research in Science Teaching, 33, 625–656. Google Scholar
  5. Cohen, M.R. & Nagel, E. (1934). An introduction to logic and scientific method. London: Routledge and Kegan Paul. Google Scholar
  6. Coll, R. & Treagust, D.F. (2002). Learners’ use of analogy and alternative conceptions for chemical bonding: A cross age study. Australian Science Teachers’ Journal, 48, 24–32. Google Scholar
  7. Chamberlain, T.C. (1965). The method of multiple working hypotheses. Science, 148, 754–759. (First published in 1897.) Google Scholar
  8. De Kruif, P. (1953). Microbe hunters. New York: Harcourt Brace & World. (First published in 1926.) Google Scholar
  9. Dominowski, R.L. & Dallob, P. (1995). Insight and problem solving. In R.J. Sternberg & J.E. Davidson (Eds.), The nature of insight. Cambridge, MA: MIT Press. Google Scholar
  10. Dreistadt, R. (1968). An analysis of the use of analogies and metaphors in science. Journal of Psychology, 68, 97–116. Google Scholar
  11. Educational Policies Commission (1961). The central purpose of American education. Washington, DC: National Education Association. Google Scholar
  12. Educational Policies Commission (1966). Education and the spirit of science. Washington, DC: National Education Association. Google Scholar
  13. Elementary Science Study (1974). Attribute games and problems: Teachers’ guide. New York: McGraw-Hill. Google Scholar
  14. Finke, R.A., Ward, T.B. & Smith, S.M. (1992). Creative cognition: Theory, research and applications. Cambridge, MA: The MIT Press. Google Scholar
  15. Gabriel, S.E., O’Fallon, W.M., Kurland, L.T., Beard, C.M., Woods, J.E. & Melton, I.I. (1994). Risk of connective-tissue disease and other disorders after breast implantation. New England Journal of Medicine, 330, 1697–1702. Google Scholar
  16. Galilei, G. (1610). The sidereal messenger. In H. Shapley, S. Rapport & H. Wright (Eds.) (1954). A treasury of science. New York: Harper & Brothers. Google Scholar
  17. Gentner, D. (1989). The mechanisms of analogical learning. In S. Vosniadou & A. Ortony (Eds.), Similarity and analogical reasoning. Cambridge, England: Cambridge University Press. Google Scholar
  18. Germann, P.J. (1994). Testing a model of science process skills acquisition: An interaction with parents’ education, preferred language, gender, science attitude, cognitive development, academic ability, and biology knowledge. Journal of Research in Science Teaching, 31, 749–783. Google Scholar
  19. Grossberg, S. (1982). Studies of mind and brain. Dordrecht, Holland: D. Reidel. Google Scholar
  20. Hand, B.M., Prain, V. & Yore, L.D. (2001). Sequential writing tasks’ influence on science learning. In P. Tynjala, L. Mason & K. Lonka (Eds.), Writing as a learning tool: Integrating theory and practice. Dordrecht, The Netherlands: Kluwer Academic Publishers. Google Scholar
  21. Hanson, N.R. (1958). Patterns of discovery: An inquiry into the conceptual foundations of science. Cambridge, England: Cambridge University Press. Google Scholar
  22. Harrison, A.G., Grayson, D.J. & Treagust, D.F. (1999). Investigating a Grade 11 student’s evolving conceptions of heat and temperature. Journal of Research in Science Teaching, 36, 55–87. Google Scholar
  23. Hauser, M.D. (2000). What do animals think about numbers? American Scientist, 88, 144–151. Google Scholar
  24. Hempel, C. (1966). Philosophy of natural science. Upper Saddle River, NJ: Prentice-Hall. Google Scholar
  25. Heiss, E.D., Obourn, E.S. & Hoffman, C.W. (1950). Modern science teaching. New York: MacMillan. Google Scholar
  26. Hestenes, D. (1992). Modeling games in a Newtonian world. American Journal of Physics, 55, 440–454. Google Scholar
  27. Hoffman, R.R. (1980). Metaphor in science. In P.R. Honeck & R.R. Hoffman (Eds.), The psycholinguistics of figurative language. Hillsdale: Erlbaum. Google Scholar
  28. Hofstadter, D.R. (1981). Metamagical themas: How might analogy, the core of human thinking, be understood by computers? Scientific American, 249, 18–29. Google Scholar
  29. Holland, J.H., Holyoak, K.J., Nisbett, R.E. & Thagard, P.R. (1986). Induction: Processes of inference, learning, and discovery. Cambridge, MA: The MIT Press. (See especially Chapter 11.) Google Scholar
  30. Inhelder, B. & Piaget, J. (1958). The growth of logical thinking from childhood to adolescence. New York: Basic Books. Google Scholar
  31. Johnson, M. (1987). The body in the mind. Chicago: University of Chicago Press. Google Scholar
  32. Johnson, M.A. & Lawson, A.E. (1998). What are the relative effects of reasoning ability and prior knowledge on biology achievement in expository and inquiry classes? Journal of Research in Science Teaching, 35, 89–103. Google Scholar
  33. Johnson-Laird, P.N. (1983). Mental models: Towards a cognitive science of language, inference, and consciousness. Cambridge, MA: Harvard University Press. Google Scholar
  34. Johnson-Laird, P.N. (2003). Mental models and reasoning. In J.P. Leighton & R.J. Sternberg (Eds.), The nature of reasoning. New York: Cambridge University Press. Google Scholar
  35. Karplus, R. & Thier, H.D. (1967). A new look at elementary school science. Chicago: Rand McNally. Google Scholar
  36. Koestler, A. (1964). The act of creation. London: Hutchinson. Google Scholar
  37. Kosslyn, S.M. & Koenig, O. (1995). Wet mind: The new cognitive neuroscience. New York: The Free Press. Google Scholar
  38. Lawson, A.E. (1992). The development of reasoning among college biology students. Journal of College Science Teaching, 21, 338–344. Google Scholar
  39. Lawson, A.E. (1993). Deductive reasoning, brain maturation, and science concept acquisition: Are they linked? Journal of Research in Science Teaching, 30, 1029–1052. Google Scholar
  40. Lawson, A.E. (1999). What should students learn about the nature of science and how should we teach it? Journal of College Science Teaching, 28, 401–411. Google Scholar
  41. Lawson, A.E. (2000). How do humans acquire knowledge? And what does that imply about the nature of knowledge? Science and Education, 9, 577–598. Google Scholar
  42. Lawson, A.E. (2002). What does Galileo’s discovery of Jupiter’s moons tell us about the process of scientific discovery? Science and Education, 11, 1–14. Google Scholar
  43. Lawson, A.E. (2003a). The nature and development of hypothetico-predictive argumentation with implications for science teaching. International Journal of Science Education, 25, 1387–1408. Google Scholar
  44. Lawson, A.E. (2003b). The neurological basis of learning, development and discovery: Implications for teaching science and mathematics. Dordrecht, The Netherlands: Kluwer Academic Publishers. Google Scholar
  45. Lawson, A.E., Abraham, M.R. & Renner, J.W. (1989). A theory of instruction: Using the learning cycle to teach science concepts and thinking skills. Cincinnati: National Association for Research in Science Teaching. Google Scholar
  46. Lawson, A.E., Clark, B., Cramer-Meldrum, E., Falconer, K.A., Kwon, Y.J. & Sequist, J.M. (2000a). The development of reasoning skills in college biology: Do two levels of general hypothesis-testing skills exist? Journal of Research in Science Teaching, 37, 81–101. Google Scholar
  47. Lawson, A.E., Drake, N., Johnson, J., Kwon, Y.J. & Scarpone, C. (2000b). How good are students at testing alternative explanations of unseen entities? The American Biology Teacher, 62, 249–255. Google Scholar
  48. Lawson, A.E., Lewis, C.M. Jr. & Birk, J.P. (1999). Why do students “cook” lab data? A case study of the tenacity of misconceptions. The Journal of College Science Teaching, 29, 191–198. Google Scholar
  49. Lawson, A.E. & Wollman, W.T. (1976). Encouraging the transition from concrete to formal cognitive functioning – an experiment. Journal of Research in Science Teaching, 13, 413–430; also (2003). Journal of Research in Science Teaching, 40, S33–S50. Google Scholar
  50. Lewis, R.W. (1988). Biology: A hypothetico-deductive science. The American Biology Teacher, 50, 362–367. Google Scholar
  51. Medawar, P.B. (1969). Induction and intuition in scientific thought. Philadelphia: American Philosophical Society. Google Scholar
  52. Miller, G.A. (1956). The magical number seven, plus or minus two. Psychological Review, 63, 81–97. Google Scholar
  53. Moore, J.A. (1993). Science as a way of knowing. Cambridge, MA: Harvard University Press. Google Scholar
  54. Moshman, D. (1998). Cognitive development beyond childhood. In D. Kuhn & R.S. Siegler (Eds.), Handbook of child psychology: Vol. 2. Cognition, perception, and language, 5th edn. New York: Wiley. Google Scholar
  55. Nauta, W.J.H. (1971). The problem of the frontal lobe: A reinterpretation. Journal of Psychiatric Research, 8, 167–187. Google Scholar
  56. Noh, T. & Scharmann, L.C. (1997). Instructional influence of a molecular-level pictorial presentation of matter on students’ conceptions and problem-solving ability. Journal of Research in Science Teaching, 34, 199–217. Google Scholar
  57. Pascual-Leone, J. (1970). A mathematical model for the transition rule in Piaget’s developmental stages. Acta Psychologica, 32, 301–345. Google Scholar
  58. Pascual-Leone, J. & Ijaz, H. (1989). Mental capacity testing as a form of intellectual development. In R.J. Samuda, S.L. Kong, J. Cummins, J. Lewis & J. Pascual-Leone (Eds.), Assessment and placement of minority students (pp. 143–171). Toronto: C.J. Hogrefe. Google Scholar
  59. Paulesu, E., Frith, D.D. & Frackowiak, R.S.J. (1993). The neural correlates of the verbal component of working memory. Nature, 362, 342–345. CrossRefPubMedGoogle Scholar
  60. Piaget, J. (1962). Judgment and reasoning in the child. London: Routledge & Kegan Paul. (First published in 1928.) Google Scholar
  61. Piaget, J. (1970). Genetic epistemology. New York: Norton. Google Scholar
  62. Piaget, J. (1971). Problems of equilibration. In C.F. Nodine, J.M. Gallagher & R.D. Humphreys (Eds.), Piaget and Inhelder on equilibration. Philadelphia, PA: The Jean Piaget Society. Google Scholar
  63. Piaget, J. (1976). Piaget’s theory. In B. Inhelder & H.H. Chipman (Eds.), Piaget and his school. New York: Springer. Google Scholar
  64. Piaget, J. (1985). The equilibration of cognitive structures: The central problem of intellectual development. Chicago and London: The University of Chicago Press. Google Scholar
  65. Piaget, J. & Inhelder, B. (1969). The psychology of the child. New York: Basic Books. Google Scholar
  66. Platt, J.R. (1964). Strong inference. Science, 146, 347–353. Google Scholar
  67. Popper, K.R. (1959). The logic of scientific discovery. New York: Basic Books. Google Scholar
  68. Popper, K.R. (1965). Conjectures and refutations: The growth of scientific knowledge. New York: Basic Books. Google Scholar
  69. Shayer, M. & Adey, P.S. (1993). Accelerating the development of formal thinking in middle and high school students IV: Three years after a two-year intervention. Journal of Research in Science Teaching, 30, 351–366. Google Scholar
  70. Shymansky, J.A. (1984). BSCS programs: Just how effective were they? The American Biology Teacher, 46, 54–57. Google Scholar
  71. Shymansky, J.A., Kyle, W.C. & Alport, J.M. (1983). The effects of new science curricula on student performance. Journal of Research in Science Teaching, 20, 387–404; also (2003). Journal of Research in Science Teaching, 40, S68–S87. Google Scholar
  72. Simon, H.A. (1974). How big is a chunk? Science, 183, 482–488. Google Scholar
  73. Squire, L.R. & Zola-Morgan, S. (1991). The medial temporal lobe memory system. Science, 253, 1380–1386. Google Scholar
  74. Thatcher, R.W. (1991). Maturation of the human frontal lobes: Physiological basis of staging. Developmental Neuropsychology, 7, 397–419. CrossRefGoogle Scholar
  75. Thatcher, R.W., Walker, R.A. & Giudice, S. (1987). Human cerebral hemispheres develop at different rates and ages. Science, 236, 1110–1113. Google Scholar
  76. Tidman, P. & Kahane, H. (2003). Logic and philosophy, 9th edn. Belmont, CA: Wadsworth/Thomson. Google Scholar
  77. Toulmin, S.E. (1958). The uses of argument. Cambridge, England: Cambridge University Press. Google Scholar
  78. Toulman, S.E., Rieke, R. & Janik, A. (1984). An introduction to reasoning, 2nd edn. New York: Macmillan. Google Scholar
  79. Van Deventer, W.C. (1958). A simplified approach to the problem of scientific methodology. School Science and Mathematics, 58, 99. Google Scholar
  80. Wallis, J.D., Anderson, K.C. & Miller, E.K. (2001). Single neurons in prefrontal cortex encode abstract rules. Nature, 411, 953–956. Google Scholar
  81. Warnick, B. & Inch, E.S. (1989). Critical thinking and communication: The use of reason in argument. New York: Macmillan. Google Scholar
  82. Washton, N.S. (1967). Teaching science creatively in the secondary schools. Philadelphia: Saunders. Google Scholar
  83. Westbrook, S.L. & Rogers, L.N. (1994). Examining the development of scientific reasoning in ninth-grade physical science students. Journal of Research in Science Teaching, 31, 65–76. Google Scholar
  84. Wollman, W. (1977). Controlling variables: Assessing levels of understanding. Science Education, 61, 371–383. Google Scholar
  85. Wong, E.D. (1993). Self-generated analogies as a tool for constructing and evaluating explanations of scientific phenomena. Journal of Research in Science Teaching, 30, 367–380. Google Scholar
  86. Woodward, J. & Goodstein, D. (1996). Conduct, misconduct and the structure of science. American Scientist, 84, 479–490. Google Scholar
  87. Zohar, A., Weinberger, Y. & Tamir, P. (1994). The effect of biology critical thinking project on the development of critical thinking. Journal of Research in Science Teaching, 32, 183–196. Google Scholar

Copyright information

© National Science Council, Taiwan 2004

Authors and Affiliations

  1. 1.School of Life SciencesArizona State UniversityTempeU.S.A.

Personalised recommendations