The possibilities of cognitive enhancement have won popularity among commons and academic circles in recent decades. Cognitive enhancement has often been associated with advances in neuroscientific technologies aimed to improve cognitive and intellectual capacities of the brain. It focuses on the improvement of cognitive functions, such as attention, reasoning, memory and executive function.
Traditionally, enhancement of cognitive performance could be achieved through conventional way such as structured classroom teaching, individual learning, training (ex. abacus, foreign language, exercise), as well as the use of external information-processing devices. Contemporary attempts to improve cognitive performance often involve the consumptions of drugs, such as amphetamine, methylphenidate, and modafinil, or utilization of electrical brain stimulators.
Throughout the whole life span, the adaptations to the diverse contexts and changing social environments are very important to individuals. Capabilities of significant changes of brain and neural system in the complex environments have been referred to as neuroplasticity. During recent years, the literature on cognitive training has been growing rapidly. Neuroplasticity can be observed in mind and brain after training intervention, and the scope and pattern of training effects can be measured to identify the underling mechanism.
In general, this study aims to develop teaching and learning programs to enhance cognitive performance through the application of educational neuroscience into the optimization, generalization and integration of instructional techniques. Optimization refers to the improvement of existing techniques to achieve better results. Generalization refers to the application of existing techniques to different domains and integration refers to the combination of several existing techniques to create more effective techniques.
Topic One: Exploring Cognitive Enhancement through Teaching Programs. The Design of New Teaching Model of Neuroanatomy to Prevent Neurophobia in Preclinical Medical Students.
Topic Two: Exploring Cognitive Enhancement through Learning Programs. Six-month abacus training improves working memory performance in children: a functional MRI and behavior study. Exam the effects of short term abacus training on brain structure, activation pattern and behavior, as a potential learning program to improve working memory.

Buonomano, D. V. & Merzenich, M. M. Temporal information transformed into a spatial code by a neural network with realistic properties. Science 267, 1028–1030 (1995).
Clark, Ruth C. Building expertise: Cognitive methods for training and performance improvement. John Wiley & Sons, 2008.
Colvin, Robert. "Optimising, generalising and integrating educational practice using neuroscience." npj Science of Learning 1.1 (2016): 1-4.
Farah, Martha J. "The unknowns of cognitive enhancement." Science 350.6259 (2015): 379-380.
Feiler, Jacob B., and Maureen E. Stabio. "Three pillars of educational neuroscience from three decades of literature." Trends in neuroscience and education 13 (2018): 17-25.
Feldman Daniel, E. “The spike-timing dependence of plasticity.” Neuron 75, 556–571 (2012).
Goswami, Usha. "Neuroscience and education: from research to practice?." Nature reviews neuroscience 7.5 (2006): 406-413.
Meltzoff, Andrew N., et al. "Foundations for a new science of learning." science 325.5938 (2009): 284-288.
Murphy, M., Reid, K., Hilton, D. J. & Bartlett, P. F. “Generation of sensory neurons is stimulated by leukemia inhibitory factor.” Proc. Natl Acad. Sci. USA 88, 3498–3501 (1991).
Sachdev, Perminder S., et al. "Classifying neurocognitive disorders: the DSM-5 approach." Nature Reviews Neurology 10.11 (2014): 634.
Savulescu, J., R. ter Meulen, and G. Kahane, eds. 2011. Enhancing human capacities. Oxford: Wiley Blackwell.
Sweller, John. "Cognitive load during problem solving: Effects on learning." Cognitive science 12.2 (1988): 257-285.
Varma, Sashank, Bruce D. McCandliss, and Daniel L. Schwartz. "Scientific and pragmatic challenges for bridging education and neuroscience." Educational researcher 37.3 (2008): 140-152.
Vukovic, J. et al. Immature doublecortin-positive hippocampal neurons are important for learning but not for remembering. J. Neurosci. 33, 6603–6613 (2013).
Young, John Q., et al. "Cognitive load theory: implications for medical education: AMEE Guide No. 86." Medical teacher 36.5 (2014): 371-384.
Savulescu, Julian, Ruud Ter Meulen, and Guy Kahane, eds. Enhancing human capacities. John Wiley & Sons, 2011.

Beddington, J., Cooper, C.L., Field, J., Goswami, U., Huppert, F.A., Jenkins, R. et al. (2008). The mental wealth of nations. Nature, 455, 1057–60.
Schermer, M. 2012. Van genezen naar verbeteren. Inaugural lecture. Rotterdam: Erasmus Medical Centre.
Kass, L. 2002. Life, liberty and the defense of dignity. San Francisco, CA: Encounter Books.
Fukuyama, F. 2003. Our posthuman future: Consequences of the biotechnology revolution. London: Profile Books.

1. Miles, R.A. (2005). Diagnostic imaging in undergraduate medical education: an expanding role. Clin Radiol., 60(7):742-5.
2. Biasutto S.N. et al. (2006). Teaching anatomy: cadavers vs. computers? Ann Anat., 188(2):187-90.
3. Dobson HD, Pearl RK, Orsay CP, Rasmussen M, Evenhouse R, Ai Z, et al. Virtual reality: new method of teaching anorectal and pelvic floor anatomy. Dis Colon Rectum 2003; 46 (3):349-52.
4. Riva G. Applications of Virtual Environments in Medicine. Methods Inf Med 2003; 42: 524–3
5. Hart R. and Karthigasu K. The benefits of virtual reality simulator training for laparoscopic surgery. Current Opinion in Obstetrics and Gynecology 2007, 19:297–302
6. Stadie, A.T. Virtual reality system for planning minimally invasive neurosurgery. J Neurosurg 108:382–394, 2008
7. Gorman P.J. et al. Simulation and Virtual Reality in Surgical Education Real or Unreal? Arch Surg. 1999;134:1203-1208
8. Anil S.M. et al. (2005). Virtual 3-dimensional preoperative planning with the dextroscope for excision of a 4th ventricular ependymoma. Minim Invas Neurosurg 50: 65 – 70
9. Wong G.K.C. et al. (2007). Craniotomy and clipping of intracranial aneurysm in a stereoscopic virtual reality environment. Neurosurgery 61:564–569
10. Kockro R.A. et al. (2009).Virtual temporal bone: an interactive 3-dimentional learning aid for cranial base surgery. Neurosurgery 64[ONS Suppl 2]:ons216–ons230
11. Visible Human Project (VHP) page of the NLM: (http://www.nlm.nih.gov/research/visible/)
12. Visible Human 3D Anatomical Structure Viewer (EPA Lausanne):http://visiblehuman. epfl.ch/applet3D.php
13. Zinchuk A.V. et al. (2010). Attitudes of US medical trainees towards neurology education: "Neurophobia" - a global issue. BMC Medical Education, 10:49

1. Chinese Zhusuan, knowledge and practices of mathematical calculation through the abacus. UNESCO Culture Sector - Intangible Heritage. Retrieved 2013 Dec 6 from http://www.unesco.org/culture/ich/en/RL/00853
2. Tanaka S., Michimata C., Kaminaga T., Honda M., Sadato N. (2002) Superior digit memory of abacus experts: an event-related functional MRI study, NeuroReport 13 (2002) 2187–2191.
3. Hanakawa T., Honda M., Okada T., Yonekura Y., Fukuyama H., Shibasaki H. (2003) Neural correlates underlying mental calculation in abacus experts: a functional magnetic resonance imaging study, Neuroimage 19 (2003) 296–307.
4. Chen F.Y. et al. (2006) Neural correlates of serial abacus mental calculationin children: A functional MRI study. Neuroscience Letters 403: 46–51
5. Chen C.L. et al. (2006) Prospective demonstration of brain plasticity after intensive abacus-based mental calculation training: An fMRI study. Nuclear Instruments and Methods in Physics Research A 569: 567–571
6. Irwing P., Hamza A., Khaleefa O., Lynn R. (2008) Effects of abacus training on the intelligence of Sudanese children. Personality and Individual Differences. 2008;45(7):694-696. doi: 10.1016/j.paid.2008.06.011
7. Hu, Y. et al. (2011) Enhanced white matter tracts integrity in children with abacus training. Human Brain Mapping 32:10–21
8. Takeuchi, H. (2013) Effects of working memory training on functional connectivity and cerebral blood flow during rest. Cortex 49: 2106-2125
9. Statistical Parametric Mapping 5, Wellcome Trust Centre for Neuroimaging, UCL, UK. Retrieved 2013 Jan 24 from http://www.fil.ion.ucl.ac.uk/spm/
10. Song X.W., Dong Z.Y., Long X.Y., Li S.F., Zuo X.N., Zhu C.Z., He Y., Yan C.G., Zang Y.F. (2011) REST: A Toolkit for Resting-State Functional Magnetic Resonance Imaging Data Processing. PLoS ONE 6(9): e25031. doi:10.1371/journal.pone.0025031
11. Pilot study, ISMRM 2013, Cheng Y.S.
12. Tzourio-Mazoyer N., Landeau B., Papathanassiou D., Crivello F., Etard O., Delcroix N., Mazoyer B. and Joliot M. (January 2002). Automated Anatomical Labeling of activations in SPM using a Macroscopic Anatomical Parcellation of the MNI MRI single-subject brain. NeuroImage 15 (1): 273–289. doi:10.1006/nimg.2001.0978.
13. Li Y., Hu Y., Zhao M., Wang Y., Huang J., Chen F. (2013) The neural pathway underlying a numerical working memory task in abacus-trained children and associated functional connectivity in the resting brain. Brain Research 1539 (2013) 24-33.
14. Stigler J. W., Chalip L., Miller K. F. (1986) Consequences of skill: the case of abacus training in Taiwan. American Journal of Education Vol. 94, No. 4 (Aug., 1986), pp. 447-479

15. Hatano G., Miyake Y., Binks M.G. (1977). Performance of expert abacus operators. Cognition, 5, 47-55.
16. Hatano G., Amaiwa S., Shimizu K. (1987). Formation of a mental abacus for computation and its use as a memory device for digits: a developmental study. Developmental Psychology. 1987, Vol. 23, No. 6,832-838
17. Reisberg, D., Rappaport, I., & O'Shaughnessy, M. (1984). Limits of working memory: The digit digit-span. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10, 203-221.
18. Dehaene S., Piazza M., Pinel P., Cohen L. (2003) Three parietal circuits for number processing, Cogn. Neuropsychol. 20 (2003) 487–506.
19. Zamarian L., Ischebeck A., Delazer M. (2009) Neuroscience of learning arithmetic - evidence from brain imaging studies. Neurosci Biobehav Rev. 2009 Jun;33(6):909-25. doi: 10.1016/j.neubiorev.2009.03.005
20. Rivera S.M., Reiss A.L., Eckert M.A., Menon V. (2005) Developmental changes in mental arithmetic: evidence for increased functional specialization in the left inferior parietal cortex. Cereb Cortex. 15(11):1779-90.
21. Olesen P.J., Westerberg H., & Klingberg T. (2004) Increased prefrontal and parietal activity after training of working memory. Nature Neurosci. Vol. 7, No. 1 (Jan., 2004), pp. 75-79
22. Klingberg T. (2010) Training and plasticity of working memory. Trends Cogn Sci. 2010 Jul;14(7):317-24. doi: 10.1016/j.tics.2010.05.002.
23. Jaeggi S.M., Buschkuehl M., Jonides J., Shah P. (2011) Short- and long-term benefits of cognitive training. Proc Natl Acad Sci USA 108:10081-10086.
24. Owen A.M., et al. (2010) Putting brain training to the test. Nature 465:775–778.
25. Melby-Lervåg M., Hulme C. (2013) Is working memory training effective? A meta-analytic review. Developmental Psychology 2013, Vol. 49, No. 2, 270-291
26. Jaeggi SM, Buschkuehl M, Jonides J, Perrig WJ (2008) Improving fluid intelligence with training on working memory. Proc Natl Acad Sci USA 105:6829–6833.
27. Kundu B., Sutterer D.W., Emrich S.M., & Postle B.R. (2013) Strengthened effective connectivity underlies transfer of working memory training to tests of short-term memory and attention. J Neurosci. 2013 May 15;33(20):8705-15. Doi: 10.1523/JNEUROSCI.5565-12.20

1. UNESCO Culture Sector - Intangible Heritage. Retrieved 2013 Dec 6 from
http://www.unesco.org/culture/ich/en/RL/00853
2. Tanaka S. (2002) NeuroReport 13 (2002) 2187–2191.
3. Hanakawa T. (2003) Neuroimage 19 (2003) 296–307.
4. Chen F.Y. (2006) Neuroscience Letters 403: 46–51
5. Chen C.L. (2006) Nuclear Instruments and Methods in Physics Research A 569:
567–571
6. Irwing P. (2008) Personality and Individual Differences. 2008;45(7):694-696.
7. Hu, Y. (2011) Human Brain Mapping 32:10–21
8. Takeuchi, H. (2013) Cortex 49: 2106-2125
9. Statistical Parametric Mapping 5, Wellcome Trust Centre for Neuroimaging, UCL,
UK. Retrieved 2013 Jan 24 from http://www.fil.ion.ucl.ac.uk/spm/
10. Song X.W. (2011) REST: A Toolkit for Resting-State Functional Magnetic
Resonance Imaging Data Processing. PLoS ONE 6(9): e25031.
11. Pilot study, ISMRM 2013, Cheng Y.S.
12. Tzourio-Mazoyer N. (2002). NeuroImage 15 (1): 273–289.
13. Li Y. (2013) Brain Research 1539 (2013) 24-33.
14. Stigler J. W. (1986) American Journal of Education Vol. 94, No. 4, pp. 447-479
15. Hatano G. (1977). Cognition, 5, 47-55.
16. Hatano G. (1987). Developmental Psychology. 1987, Vol. 23, No. 6,832-838
17. Reisberg, D. (1984). Journal of Experimental Psychology: Learning, Memory, and
Cognition, 10, 203-221.
18. Dehaene S. (2003) Cogn. Neuropsychol. 20 (2003) 487–506.
19. Zamarian L. (2009) Neurosci Biobehav Rev. 2009 Jun;33(6):909-25.
20. Rivera S.M. (2005) Cereb Cortex. 15(11):1779-90.
21. Olesen P.J. (2004) Nature Neurosci. Vol. 7, No. 1 (Jan., 2004), pp. 75-79
22. Klingberg T. (2010) Trends Cogn Sci. 2010 Jul;14(7):317-24.
23. Jaeggi S.M. (2011) Proc Natl Acad Sci USA 108:10081-10086.
24. Owen A.M. (2010) Nature 465:775–778.
25. Melby-Lervåg M. (2013) Developmental Psychology 2013, Vol. 49, No. 2,
270-291
26. Jaeggi SM (2008) Proc Natl Acad Sci USA 105:6829–6833.
27. Kundu B. (2013) J Neurosci. 2013 May 15;33(20):8705-15.