Skip to main content


Log in

Traveling down the road: from cognitive neuroscience to mathematics education … and back

  • Commentary Paper
  • Published:
ZDM Aims and scope Submit manuscript


In this commentary paper to the special issue on “Cognitive Neuroscience and Mathematics Education”, we reflect on the connection between cognitive neuroscience and mathematics education from an educational research point of view. The current issue highlights that cognitive neuroscience offers a series of tools, methodologies and theories to investigate cognitive processes that take place during mathematical thinking and learning. This might complement and extend our knowledge that has been obtained on the basis of behavioral data only, the common approach in educational research. At the same time, we note that the existing neuroscientific studies have investigated mathematical performance in relative isolation from the educational context. The characteristics of this context have, however, a large influence on mathematical performance and its correlated brain activity, an issue that should be addressed in future research. We contend that traveling back and forth from cognitive neuroscience to mathematics education might yield a better understanding of how mathematical learning takes place and how it can be influenced.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others


  • Ansari, D., & Coch, D. (2006). Bridges over troubled waters: Education and cognitive neuroscience. Trends in Cognitive Sciences, 10, 146–151.

    Article  Google Scholar 

  • Bornemann, B., Foth, M., Horn, J., Ries, J., Warmuth, E., Wartenburger, I., et al. (2010). Mathematical cognition—Individual differences in resource allocation. ZDMThe International Journal on Mathematics Education. doi:10.1007/s11858-010-0253-x.

  • Butterworth, B., & Laurillard, D. (2010). Low numeracy and dyscalculia: Identification and intervention. ZDMThe International Journal on Mathematics Education. doi:10.1007/s11858-010-0267-4.

  • Cacioppo, J. T., Berntson, G. G., & Nusbaum, H. C. (2008). Neuroimaging as a new tool in the toolbox of psychological science. Current Directions in Psychological Science, 17, 62–67.

    Article  Google Scholar 

  • De Corte, E., Greer, B., & Verschaffel, L. (1996). Learning and teaching mathematics. In D. C. Berliner & R. C. Calfee (Eds.), Handbook of educational psychology (pp. 491–549). New York: MacMillan.

    Google Scholar 

  • De Smedt, B., Ansari, D., Grabner, R. H., Hannula, M. M., Schneider, M., & Verschaffel, L. (2010). Cognitive neuroscience meets mathematics education. Educational Research Review, 5, 97–105.

    Article  Google Scholar 

  • Dowker, A. (2005). Individual differences in arithmetic. Implications for psychology, neuroscience and education. Hove: Psychology Press.

    Book  Google Scholar 

  • Gabrieli, J. E. D. (2009). Dyslexia: A new synergy between education and cognitive neuroscience. Science, 325, 280–283.

    Article  Google Scholar 

  • Grabner, R. H., & Ansari, D. (2010). Promises and pitfalls of a “cognitive neuroscience of mathematics learning”. ZDMThe International Journal on Mathematics Education. doi:10.1007/s11858-010-0283-4.

  • Greeno, J. G., Collins, A. M., & Resnick, L. B. (1996). Cognition and learning. In D. C. Berliner & R. C. Calfee (Eds.), Handbook of educational psychology (pp. 15–46). New York: MacMillan.

    Google Scholar 

  • Howard-Jones, P. (2008). Education and neuroscience [special issue]. Educational Research, 50(2):119–201.

    Google Scholar 

  • Landgraf, S., van der Meer, E., & Krueger, F. (2010). Cognitive resource allocation for neuronal activity underlying mathematical cognition: A multi-method study. ZDMThe International Journal on Mathematics Education. doi:10.1007/s11858-010-0264-7.

  • Lee, K., Yeong, S. H. M., Ng, S. F., Venkatraman, V., Graham, S., & Chee, M. W. L. (2010). Computing solutions to algebraic problems using a symbolic vs. a schematic strategy. ZDM—The International Journal on Mathematics Education. doi:10.1007/s11858-010-0265-6.

  • Luna, B., Garver, K. E., Urban, T. A., Lazar, N. A., & Sweeney, J. A. (2004). Maturation of cognitive processes from late childhood to adulthood. Child Development, 75, 1357–1372.

    Article  Google Scholar 

  • Menon, V. (2010). Developmental cognitive neuroscience of arithmetic: Implications for learning and education. ZDMThe International Journal on Mathematics Education. doi:10.1007/s11858-010-0242-0.

  • Nickerson, S. D., & Whitacre, I. (2010). A local instruction theory for the development of number sense. Mathematical Thinking and Learning, 3, 227–252.

    Article  Google Scholar 

  • Obersteiner, A., Dresler, T., Reiss, K., Vogel, C. M., Pekrun, R., & Fallgatter, A. J. (2010). Bringing brain imaging to the school to assess arithmetic problem solving. Chances and limitations in combining educational and neuroscientific research. ZDMThe International Journal on Mathematics Education. doi:10.1007/s11858-010-0256-7.

  • Preusse, F., van der Meer, E., Ullwer, D., Brucks, M., Krueger, F., & Wartenburger, I. (2010). Long-term characteristics of analogical processing in high-school students with high fluid intelligence. An fMRI study. ZDMThe International Journal on Mathematics Education. doi:10.1007/s11858-010-0259-4.

  • Sloane, F. C. (2008). Randomized trials in mathematics education: Recalibrating the proposed high watermark. Educational Researcher, 9, 624–630.

    Article  Google Scholar 

  • Stavy, R., & Babai, R. (2010). Overcoming intuitive interference in mathematics: Insights from behavioural, brain imaging and intervention studies. ZDMThe International Journal on Mathematics Education. doi:10.1007/s11858-010-0251-z.

  • Stern, E. (2005). Pedagogy meets neuroscience. Science, 310, 745.

    Article  Google Scholar 

  • Stern, E., & Schneider, M. (2010). Editorial: A digital roadmap analogy of the relation between neuroscience and educational research. ZDMThe International Journal on Mathematics Education. doi:10.1007/s11858-010-0278-1.

  • Tang, Y. Y., Zhang, W. T., Chen, K. W., Feng, S. G., Ji, Y., Shen, J. X., et al. (2006). Arithmetic processing in the brain shaped by cultures. Proceedings of the National Academy of Sciences of the United States of America, 103, 10775–10780.

    Article  Google Scholar 

  • Thomas, M. J., Wilson, A. J., Corballis, M. C., Lim, V. K., & Yoon, C. (2010). Evidence from cognitive neuroscience for the role of graphical and algebraic representations in understanding function. ZDMThe International Journal on Mathematics Education. doi:10.1007/s11858-010-0272-7.

  • van Merriënboer, J. J. G., & Kirschner, P. (2007). Ten steps to complex learning. A systematic approach to four-component instructional design. New York: Lawrence Erlbaum Associates.

  • Zago, L., Petit, L., Mellet, E., Joliot, M., Mazoyer, B., & Tzourio-Mazoyer, N. (2010). Neural correlates of counting large numerosity. ZDMThe International Journal on Mathematics Education. doi:10.1007/s11858-010-0254-9.

  • Zamarian, L., Ischebeck, A., & Delazer, M. (2009). Neuroscience of learning arithmetic—Evidence from brain imaging studies. Neuroscience and Biobehavioral Reviews, 33, 909–925.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Bert De Smedt.

Rights and permissions

Reprints and permissions

About this article

Cite this article

De Smedt, B., Verschaffel, L. Traveling down the road: from cognitive neuroscience to mathematics education … and back. ZDM Mathematics Education 42, 649–654 (2010).

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: