• Patrice Potvin
  • Steve Masson
  • Stéphanie Lafortune
  • Guillaume Cyr
Open Access


Recent research efforts have argued for the persistence of some of students’ frequent scientific misconceptions, even after correct answers are produced. Some of these studies, based on the analysis of reaction times, have recorded latencies for counter-intuitive or incongruent stimuli compared to intuitive or congruent ones. The proposed interpretations were that prior knowledge survives learning and still coexists with new closer-to-scientific knowledge, producing conflicts that delay correct answers. But these conclusions are based on the assumption that stimuli from different conditions only differ in the presence/absence of interfering misconceptions, which is sometimes, in our opinion, a rather fragile claim. Thus, we have designed a task in which it is possible to test different levels of interference and not only its effects in contrast to another condition. Then, we have used it to see if different intensities of interference produce different levels of conflict. The task tested the persistence of the misconception that “heavy objects sink more than lighter ones”. One hundred twenty-eight 14- to 15-year-olds were asked to tell which of the 2 balls presented (3 different materials and 3 different sizes) would “sink more” than the other. Analysis verified the presence of latencies and negative priming. For the most part, results show that the intensity of interference does produce corresponding latencies, which suggests greater conflict and therefore supports the hypothesis of persistence and coexistence of conceptions, even after correct answers are produced, and beyond other plausible effects due to the used stimuli. Prescriptions for theory and teaching are proposed.

Key words

buoyancy (sink/float) conceptual change misconception negative priming persistence reaction time science education 


  1. Aoki, T., Francis, P. R. & Kinoshita, H. (2003). Differences in the abilities of individual fingers during the performance of fast, repetitive tapping movements. Experimental Brain Research, 152(2), 270–280.CrossRefGoogle Scholar
  2. Babai, R. & Amsterdamer, A. (2008). The persistance of solid and liquid naive conceptions: A reaction time study. Journal of Science Education and Technology, 17, 553–559.CrossRefGoogle Scholar
  3. Babai, R., Eidelman, R. & Stavy, R. (2012). Preactivation of inhibitory control mechanisms hinders intuitive reasoning. International Journal of Science and Mathematics Education, 10, 763–775.CrossRefGoogle Scholar
  4. Babai, R., Sekal, R. & Stavy, R. (2010). Persistance of the intuitive conception of living things in adolescence. Journal of Science Education and Technology, 19, 20–26.CrossRefGoogle Scholar
  5. Borst, G., Poirel, N., Pineau, A. & Cassoti, M. (2012). Inhibitory control in number-conservation and class-inclusion tasks: A neo-Piagetian inter-task priming study. Cognitive Development, 27(3), 283–298.CrossRefGoogle Scholar
  6. Chi, M. (1992). Conceptual change in and across ontological categories: Examples for learning and discovery in science. In R. N. Giere (Ed.), Cognitive models of science (pp. 129–160). Minneapolis, MN: University of Minneapolis Press.Google Scholar
  7. DiSessa, A. A. (2006). A history of conceptual change research. In K. Sawyer (Ed.), Cambridge handbook of the learning sciences (pp. 167–281). Cambridge, UK: Cambridge University Press.Google Scholar
  8. Duit, R. & Treagust, D. (2003). Conceptual change: A powerful framework for improving science teaching and learning. International Journal of Science Education, 25(6), 671–688.CrossRefGoogle Scholar
  9. Duit, R., Treagust, D., (2012). Conceptual change: Still a powerful framework for improving science teaching and learning. In K. Shwee, D. Tan and M. Kim (Eds.), Issues and challenges in science education research (pp. 43–55). Berlin, Germany: Springer.Google Scholar
  10. Dunbar, K., Fugelsang, J. & Stein, C. (2007). Do naive theories ever go away? Using brain and behavior to understand changes in concept. In M. C. Lovett & P. Shah (Eds.), Thinking with data: 33rd Carnegie symposium on cognition (pp. 193–206). Mahwah, NJ: Erlbaum.Google Scholar
  11. Egner, T. & Hirsch, J. (2005). Where memory meets attention: Neural substrates of negative priming. Journal of Cognitive Neuroscience, 17(11), 1774–1784.CrossRefGoogle Scholar
  12. Evans, S. B. T. (2003). In two minds: Dual-process accounts of reasoning. Trends in Cognitive Sciences, 7(10), 454–459.CrossRefGoogle Scholar
  13. Hewson, P. W. (1981). A conceptual change approach to learning science. European Journal of Science Education, 3(4), 383–396.CrossRefGoogle Scholar
  14. Hewson, M. (2006). The acquisition of scientific knowledge: Analysis and representation of student conceptions concerning density. Science Education, 70(2), 159–170.CrossRefGoogle Scholar
  15. Houdé, O. (2000). Inhibition and cognitive development: Object, number, categorization and reasoning. Cognitive Development, 15, 63–73.CrossRefGoogle Scholar
  16. Houdé, O. & Guichart, E. (2001). Negative priming effect after inhibition of number/length interference in a Piaget-like task. Developmental Science, 4, 71–74.CrossRefGoogle Scholar
  17. Houdé, O., Pineau, A., Leroux, G., Poirel, N., Perchey, G., Lanoë, C., et al (2011). Functional magnetic resonance imaging study of Piaget’s conservation-of-number task in preschool and school-age children: A neo-Piagetian approach. Journal of Experimental Child Psychology, 110, 332–334.CrossRefGoogle Scholar
  18. Houdé, O., Zago, L., Mellet, E., Moutier, S., Pineau, A., Mazoyer, B., et al (2000). Shifting from the perceptual brain to the logical brain: The neural impact of cognitive inhibition training. Journal of Cognitive Neuroscience, 12(5), 721–728.CrossRefGoogle Scholar
  19. Hsin, C.-T. & Wu, H.-K. (2011). Using scaffolding strategies to promote young children’s scientific understandings of floating and sinking. Journal of Science Education and Technology, 20(5), 656–666.CrossRefGoogle Scholar
  20. Jensen, M. & Finley, F. (1995). Teaching evolution using historical arguments in a conceptual change strategy. Science Education, 79(2), 147–166.CrossRefGoogle Scholar
  21. Kelemen, D. & Rosset, E. (2009). The human function compunction: Teleological explanation in adults. Cognition, 111(1), 138–143.CrossRefGoogle Scholar
  22. Kelemen, D., Rottman, J. & Seston, R. (2012). Professional physical scientists display tenacious teleological tendencies: Purpose-based reasoning as a cognitive default. Journal of Experimental Psychology 142(4), 1074–1083.Google Scholar
  23. Lafortune, S., Masson, S. & Potvin, P. (2012a). Does inhibition have a key role to play in overcoming intuitive interferences in science? Paper presented at the Neuroscience and education: 2012 meeting of the EARLI SIG 22.Google Scholar
  24. Lafortune, S., Masson, S. & Potvin, P. (2012b). Étude du développement cérébral de la capacité à surmonter des interférences intuitives en sciences. Paper presented at the XVIIe Congrès de l'Association Mondiale des Sciences de l'Éducation (AMSE-AMCE-WAER)- Recherche en éducation et en formation: Enjeux et défis d'aujourd'hui.Google Scholar
  25. Limon, M. (2001). On the cognitive conflict as an instructional strategy for conceptual change: A critical appraisal. Learning and Instruction, 11, 357–380.CrossRefGoogle Scholar
  26. Lubin, A., Lanoë, C., Pineau, A. & Rossi, S. (2012). Apprendre à inhiber: Une pédagogie innovante au service des apprentissages scolaires fondamentaux (mathématiques et orthographe) chez des élèves de 6 à 11 ans. Neuroeducation, 1(1), 55–84.Google Scholar
  27. Masson, S., Potvin, P., Riopel, M. & Brault-Foisy, L.-M. (2014). Differences in Brain Activation Between Novices and Experts in Science During a Task Involving a Common Misconception in Electricity. Mind, Brain, and Education, 8(1), 44–55.Google Scholar
  28. Nersessian, N. J. (1998). Model-based reasoning in conceptual change. In L. Magnini, N. J. Nersessian & P. Thagard (Eds.), Model-based reasoning in scientific discovery. New York: Kluwer Academic.Google Scholar
  29. Ohlsson, S. (2009). Resubsumption: A possible mechanism for conceptual change and belief revision. Educational Psychologist, 44(1), 20–40.CrossRefGoogle Scholar
  30. Piaget, J. & Cook, M. (1952). The origins of intelligence in children. New York: W.W. Norton and Co.CrossRefGoogle Scholar
  31. Potvin, P. (2011). Manuel d'enseignement des sciences et de la technologie: Pour intéresser les élèves du secondaire. Québec: Multimondes.Google Scholar
  32. Potvin, P. (2013). Proposition for improving the classical models of conceptual change based on neuroeducational evidence: Conceptual prevalence. Neuroeducation, 1(2), 16–43.Google Scholar
  33. Potvin, P., Turmel, É. & Masson, S. (2014). Linking neuroscientific research on decision making to the educational context of novice students assigned to a multiple-choice scientific task involving common misconceptions about electrical circuits. Frontiers in Human Neuroscience, 8(14).Google Scholar
  34. Posner, G., Strike, K., Hewson, P. & Gertzog, W. (1982). Accommodation of a scientific conception: Toward a theory of conceptual change. Science Education, 66(2), 211–227.CrossRefGoogle Scholar
  35. Rossi, S., Lubin, A., Lanoë, C. & Pineau, A. (2012). Une pédagogie du contrôle cognitif pour l’amélioration de l’attention à la consigne chez l’enfant de 4–5 ans. Neuroeducation, 1(1), 29–54.Google Scholar
  36. Rowell, J. A. & Dawson, C. J. (1977). Teaching about floating and sinking: An attempt to link cognitive psychology with classroom practice. Science Education, 61(2), 243–251.CrossRefGoogle Scholar
  37. Schroeder, et al (2007). A meta-analysis of national research: Effects of teaching strategies on student achievement in science in the United States. Journal of Research in Science Teaching, 44(10), 1436–1460.CrossRefGoogle Scholar
  38. Shtulman, A. & Valcarel, J. (2012). Scientific knowledge suppresses but does not supplant earlier intuitions. Cognition, 124, 209–215.CrossRefGoogle Scholar
  39. Smith, C., Carey, S. & Wiser, M. (1985). On differentiation: A case study of the development of the concepts of size, weight, and density. Cognition, 21(3), 177–237.CrossRefGoogle Scholar
  40. Smith, C., Carey, S. & Wiser, M. (1992). Using conceptual models to facilitate conceptual change: The case of weight–density differentiation. Cognition and Instruction, 9(3), 221–283.CrossRefGoogle Scholar
  41. Smith, C., Carey, S. & Wiser, M. (1997). Teaching for understanding: A study of students’ preinstruction theories of matter and a comparison of the effectiveness of two approaches to teaching about matter and density. Cognition and Instruction, 15(3), 317–393.CrossRefGoogle Scholar
  42. Solomon, J. (1984). Prompts, cues and discrimination: The utilization of two separate knowledge systems. European Journal of Science Education, 6(1), 63–82.Google Scholar
  43. Spada, H. (1994). Conceptual change or multiple representations? Learning and Instruction, 4, 113–116.CrossRefGoogle Scholar
  44. Stavy, R. & Tirosh, D. (2000). How students (mis-)understand science and mathematics. New York: Teachers College Press.Google Scholar
  45. Thouin, M. (2001). Notions de culture scientifique et technologique. Concepts de base, percées historiques et conceptions fréquentes. Sainte-Foy, QC: Multimondes.Google Scholar
  46. Tipper, S. P. (1985). The negative priming effect: Inhibitory priming by ignored objects. The Quarterly Journal of Experimental Psychology, 37(4), 571–590.CrossRefGoogle Scholar
  47. Tipper, S. P. (2001). Does negative priming reflect inhibitory mechanisms? A review and integration of conflicting views. The Quarterly Journal of Experimental Psychology, 54A(2), 321–343.CrossRefGoogle Scholar
  48. Villani, A. (1992). Conceptual change in science and science education. Science Education, 76(2), 223–237.CrossRefGoogle Scholar
  49. Yeend, R., Loverude, M. E. & Gonzales, B. (2001). Student understanding of density: A cross-age investigation. Paper presented at the Physics Education Research Conference 2001.Google Scholar

Copyright information

© The Author(s) 2014

Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

Authors and Affiliations

  • Patrice Potvin
    • 1
  • Steve Masson
    • 1
  • Stéphanie Lafortune
    • 1
  • Guillaume Cyr
    • 1
  1. 1.Équipe de recherche en éducation scientifique et technologique, Département de didactiqueUniversité du Québec à Montréal, UQAMMontréalCanada

Personalised recommendations