• O. Roger AndersonEmail author


Modern neuroscientific research has substantially enhanced our understanding of the human brain. However, many challenges remain in developing a strong, brain-based theory of human learning, especially in complex environments such as educational settings. Some of the current issues and challenges in our progress toward developing comprehensive neuroscientific-based theories of human learning, particularly in the academic disciplines, are reviewed, beginning with a brief summary of the history of publications in science learning. This is followed by an analysis of some of the large-scale issues and conceptual problems that we currently face in developing a strong, middle-ground “neuroeducational theory” relevant to learning, especially in rather abstract disciplines such as mathematics and science. Finally, some perspectives on possible future strategies and challenges in reaching the goal of a neuroeducational theory are presented.

Key words

brain imaging and analysis brain science and science education cognitive learning theory neurocognitive models neuroeducational theory neuropsychology and learning science curriculum improvement science learning theory 


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  1. Achieve (2013). Next generation science standards. Accessed 27 Jan 2013.
  2. Anderson, O. R. (1983). A neuromathematical model of human information processing. Journal of Research in Science Teaching, 20, 603–620.CrossRefGoogle Scholar
  3. Anderson, O. R. (1991). Neurocognitive models of information processing and knowledge acquisition. Progress in Sensory Physiology, 12, 115–192.CrossRefGoogle Scholar
  4. Anderson, O. R. (1997). A neurocognitive perspective on current learning theory and science instructional strategies. Science Education, 81, 67–90.CrossRefGoogle Scholar
  5. Anderson, O. R. (2009). Neurocognitive theory and constructivism in science education: A review of neurobiological, cognitive and cultural perspectives. Brunei International Journal of Mathematics and Science Education, 1, 1–32.Google Scholar
  6. Anderson, O. R. (2011). Mind, brain and the organization of knowledge for effective recall and application. LEARNing Landscapes (special issue on Brain, Mind and Learning), 5, 45–61.Google Scholar
  7. Anderson, O. R. & Contino, J. (2013). The role of visualization in conceptual learning and conceptual change (Chap. 1 in Section 1: Brain functioning and conceptual change). In K. Finson & J. Pedersen (Eds.), Visual data and their use in science education (pp. 3–21). Charlotte: Information Age Publishing.Google Scholar
  8. Bennett, M. R. & Hacker, P. M. S. (2008). History of cognitive neuroscience. Hoboken: Wiley Blackwell.Google Scholar
  9. Bickle, J. (1998). Psychoneural reduction: The new wave. Cambridge: MIT Press.Google Scholar
  10. Blakemore, C. (2000). Achievements and challenges of the Decade of the Brain. EuroBrain, 2, 1–4.Google Scholar
  11. Brandoni, C. & Anderson, O. R. (2009). A new neurocognitive model for assessing divergent thinking: Applicability, evidence for reliability, and implications for educational theory and practice. Creativity Research Journal, 21, 326–337.CrossRefGoogle Scholar
  12. Corcoran, T., Mosher, F.A. & Rogat, A. (2009). Learning progressions in science: An evidence-based approach to reform. Teachers College, Columbia Univ., New York: Center on Continuous Instructional Improvement.Google Scholar
  13. Damasio, A. R. (1994). Descartes error: Emotion, reason, and the human brain. New York: G. P. Putnam’s Sons.Google Scholar
  14. Dietrich, A. & Kanso, R. (2010). A review of EEG, ERP, and neuroimaging studies of creativity and insight. Psychological Bulletin, 136, 822–848.CrossRefGoogle Scholar
  15. Doidge, N. (2008). Brain that Changes Itself. New York: Penguin.Google Scholar
  16. Duncan, R. G. & Rivet, A. (2013). Science learning progressions. Science, 339, 396–397.CrossRefGoogle Scholar
  17. Estes, W. K. (1994). Toward a statistical theory of learning. Psychological Review, 101, 282–289.CrossRefGoogle Scholar
  18. Fernandez-Miranda, J. C., Pathak, S. & Schneider, W. (2010). High-definition fiber tractography and language. Journal of Neurosurgery, 113, 156–157.CrossRefGoogle Scholar
  19. Fernandez-Miranda, J. C., Pathak, S. & Schneider, W. (2012). High-definition fiber tractography: Unraveling the connections of the human brain. University of Pittsburgh Neurosurgery News, 13, 1.Google Scholar
  20. Fodor, J. & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture. Cognition, 28, 3–71.CrossRefGoogle Scholar
  21. Hebb, D. O. (1949). The organization of behavior: A neuropsychological theory. New York: Wiley.Google Scholar
  22. Heiligenberg, W. (1991). The neural basis of behavior: A neuroethological view. Annual Review of Neuroscience, 14, 247–267.CrossRefGoogle Scholar
  23. Horgan, T. & Tienson, J. (1996). Connectionism and the philosophy of psychology. Cambridge: MIT Press.Google Scholar
  24. Kandel, E. R. (2001). The molecular biology of memory storage: a dialogue between genes and synapses. Science, 294, 1030–1038.CrossRefGoogle Scholar
  25. Kwon, Y.-J. & Lawson, A. E. (2000). Linking brain growth with scientific reasoning ability and conceptual change during adolescence. Journal of Research in Science Teaching, 37(1), 44–62.CrossRefGoogle Scholar
  26. Kwon, Y.-J., Lawson, A. E. & Hur, M. (1997). The role of the prefrontal lobes in scientific reasoning. Journal of the Korean Association for Research in Science Education, 17, 25–540.Google Scholar
  27. Lawson, A. E. (1986). A neurological model of sensory-motor problem solving with possible implications for higher-order cognition and instruction. Journal of Research in Science Teaching, 23, 503–522.CrossRefGoogle Scholar
  28. Lawson, A. E. (2003). The neurological basis of learning, development and discovery: Implications for teaching science and mathematics. Dordrecht: Kluwer Academic.Google Scholar
  29. Lawson, A. E. (2004). Reasoning and brain function. In R. J. Sternberg & J. P. Leighton (Eds.), The nature of reasoning. New York: Cambridge University Press.Google Scholar
  30. Lawson, D. I. & Lawson, A. E. (1993). Neural principles of memory and a neural theory of analogical insight. Journal of Research in Science Teaching, 30, 1327–1348.CrossRefGoogle Scholar
  31. Levi-Montalcini, R. & Calissano, P. (2006). The scientific challenge of the 21st century: From a reductionist to a holistic approach via systems biology. BMC Neuroscience, 7(Suppl. 1), S1. doi: 10.1186/1471-2202-7-S1-S1.CrossRefGoogle Scholar
  32. Lindquist, K. A., Wager, T. D., Kober, H., Bliss-Moreau, E. & Barrett, L. F. (2012). The brain basis of emotion: A meta-analytic review. Behavioral and Brain Sciences, 35, 121–202.CrossRefGoogle Scholar
  33. Longo, P., Anderson, O. R. & Wicht, P. (2004). Visual thinking networking promotes problem-solving achievement for 9th grade Earth Science students. Electronic Journal of Science Education, 7, 1–50.Google Scholar
  34. National Research Council (2011). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Washington: National Research Council.Google Scholar
  35. Ochsner, K. N. & Lieberman, M. D. (2001). The emergence of social cognitive neuroscience. American Psychologist, 56, 717–734.CrossRefGoogle Scholar
  36. Oppenheim, R. W. (1991). Cell death during development of the nervous system. Annual Review of Neuroscience, 14, 453–501.CrossRefGoogle Scholar
  37. Petri, H. L. & Mishkin, M. (1994). Behaviorism, cognitivism and the neuropsychology of memory. American Scientist, 82, 30–37.Google Scholar
  38. Quiroga, R. Q., Fried, I. & Koch, C. (2013). Brain cells for grandmother. Scientific American, 308, 30–35.CrossRefGoogle Scholar
  39. Richardson, R. C. (1999). Cognitive science and neuroscience: New wave reductionism. Philosophical Psychology, 12, 297–307.CrossRefGoogle Scholar
  40. Roberts, A. C. & Glanzman, D. L. (2003). Learning in Aplysia: Looking at synaptic plasticity from both sides. Trends in Neuroscience, 26, 662–667.CrossRefGoogle Scholar
  41. Rösler, F. (2012). Some unsettled problems in behavioral neuroscience research. Psychological Research, 76, 131–144.CrossRefGoogle Scholar
  42. Rumelhart, D. E. & Zipser, D. (1985). Feature discovery by competitive learning. Cognitive Science, 9, 75–112.CrossRefGoogle Scholar
  43. Schrag, F. (2011). Does neuroscience matter for education? Educational Theory, 61, 221–236.CrossRefGoogle Scholar
  44. Sullivan, J. A. (2009). The multiplicity of experimental protocols: A challenge to reductionist and non-reductionist models of the unity of neuroscience. Synthese, 167, 511–539.CrossRefGoogle Scholar
  45. Trott, C. T., Friedman, D., Ritter, W., Fabiani, M. & Snodgrass, J. G. (1999). Episodic priming and memory for temporal source: Event-related potentials reveal age-related differences in prefrontal functioning. Psychology and Aging, 14, 390–413.CrossRefGoogle Scholar
  46. Tsien, J. Z. (2007). The memory code. Scientific American, 297, 52–59.CrossRefGoogle Scholar
  47. Van Petten, C. (1993). A comparison of lexical and sentence-level context effects in event-related potentials. Language and Cognitive Processes, 8, 485–531.CrossRefGoogle Scholar

Copyright information

© National Science Council, Taiwan 2013

Authors and Affiliations

  1. 1.Columbia UniversityNew YorkUSA

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