1. Archibald, L.,Cardy, J.O., Joanisse, M.F.& Ansari, D (2013) Language, Reading, and Math Learning Profiles in an Epidemiological Sample of School Age Children, PLoS ONE Oct 14;8(10):e77463.
  2. Atteveldt, N. & Ansari, D. (2014) How symbols transform brain function: a review in memory of Leo Blomert. Trends in Neuroscience and Education, 3, 44-49.
  3. Bangerter, A., & Heath, C. (2004). The Mozart effect: Tracking the evolution of a scientific legend. British Journal of Social Psychology, 43(4), 605-623.
  4. Bartelet, D., Ansari, D., Vaessen, A. & Blomert, L. (2014) Cognitive Subtypes of Mathematics Learning Difficulties in Primary Education.  Research in Developmental Disabilities, 35, 657-670. Retrieved from https://drive.google.com/file/d/0B5JyY7bDUjP_Ql9FaEJ1ZVRONlE/edit?usp=sharing
  5. Bartelet, D., Vaessen, A. & Blomert, L. & Ansari, D (2014) What basic number processing measures in kindergarten explain unique variability in grade 1 arithmetic proficiency? Journal of Experimental Child Psychology, 117C, 12-28. Retrieved from https://drive.google.com/file/d/0B5JyY7bDUjP_WHZ1NUgyZGlUTWs/edit?usp=sharing
  6. Berch, D. B., & Mazzocco, M. M. M. (Eds.) (2007). Why is math so hard for some children? The nature and origins of mathematical learning difficulties and disabilities. Baltimore, MD: Paul Brooke.
  7. Bonny, J. W., & Lourenco, S. F. (2013). The approximate number system and its relation to early math achievement: Evidence from the preschool years. Journal of experimental child psychology, 114(3), 375-388. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3570698/
  8. Bugden, S., & Ansari, D. (2015). Probing the nature of deficits in the ‘Approximate Number System’in children with persistent Developmental Dyscalculia. Developmental Science.
  9. Bugden S. and Ansari D. (2014) When your brain can’t do 2+2: a case of developmental dyscalculia. Young Minds. 2:8. Retrieved from https://drive.google.com/file/d/0B5JyY7bDUjP_YklfZlBrTHdPUVU/edit?usp=sharing
  10. Butterworth, B., Varma, S., & Laurillard, D. (2011). Dyscalculia: from brain to education. Science, 332(6033), 1049-1053. Retrieved from http://www.lonklab.ac.uk/cms/files/jce/author/science-2011-butterworth-1049-53.pdf
  11. Butterworth, B. Dyscalculia - Numberphile.
  12. Cantlon, J. F. (2012). Math, monkeys, and the developing brain. Proceedings of the National Academy of Sciences, 109 (Supplement 1), 10725-10732. Retrieved from http://www.pnas.org/content/109/Supplement_1/10725.full
  13. Crawford, A. (2007). Learning to teach science as inquiry in the rough and tumble of practice. Journal of Research in Science Teaching, 44(4), 613–642. doi: 10.1002/tea.20157.
  14. Dehaene, S. (2011). The number sense: How the mind creates mathematics (Rev. and updated ed.). New York, NY: Oxford University Press.
  15. Dennis, M., & Barnes, M. (2002). Math and numeracy in young adults with spina bifida and hydrocephalus. Developmental neuropsychology, 21(2), 141-155.
  16. 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(1), 97–105.
  17. Devlin, K. (2010). The mathematical brain. Mind, brain and education: Neuroscience implications for the classroom, 163-178.
  18. Dyslexic Advantage. (2013). Neurobiology of Learning Disorders - Dyslexia ADHD Dyscalculia Dysgraphia. [video]. Retrieved from https://www.youtube.com/watch?v=CNTNypAG4S0 
  19. Evans, T. M., Kochalka, J., Ngoon, T. J., Wu, S. S., Qin, S., Battista, C., & Menon, V. (2015). Brain Structural Integrity and Intrinsic Functional Connectivity Forecast 6 Year Longitudinal Growth in Children's Numerical Abilities. The Journal of Neuroscience, 35(33), 11743-11750. Retrieved from https://www.researchgate.net/profile/Tanya_Evans3/publication/281241171_Brain_Structural_Integrity_and_Intrinsic_Functional_Connectivity_Forecast_6_Year_Longitudinal_Growth_in_Children's_Numerical_Abilities/links/55dc8e1608aeb38e8a8d20cf.pdf
  20. Halber, D. (1999). Different kinds of math use different parts of brain, research finds. MIT News. Retrieved from http://news.mit.edu/1999/math-0512
  21. Hiebert, J., Stigler, J. W., Jacobs, J. K., Givvin, K. B., Garnier, H., Smother, M., et al. (2005). Mathematics teaching in the United States today (and tomorrow): Results from the TIMSS 1999 video study. Educational Evaluation and Policy Analysis, 27(2), 111–132.
  22. Johnstone, A. H. (1991). Why is science difficult to learn? Things are seldom what they seem. Journal of Computer Assisted Learning, 7(2), 75–83.
  23. Jones, W. J., Childers, T. L., & Jiang, Y. (2012). The shopping brain: Math anxiety modulates brain responses to buying decisions. Biological psychology, 89(1), 201-213. Retrieved from https://www.researchgate.net/profile/Yang_Jiang/publication/51745135_The_shopping_brain_Math_anxiety_modulates_brain_responses_to_buying_decisions/links/55e72b8108ae21d099c148e5.pdf
  24. Kang, N.-H. (2008). Learning to teach science: Personal epistemologies, teaching goals, and practices of teaching. Teaching and Teacher Education, 24(2), 478–498. doi: 10.1016/j.tate.2007.01.002
  25. Lebel, C., Rasmussen, C., Wyper, K., Andrew, G., & Beaulieu, C. (2010). Brain microstructure is related to math ability in children with fetal alcohol spectrum disorder.Alcoholism: Clinical and Experimental Research, 34(2), 354-363.
  26. Leibovich, T., Vogel, S. E., Henik, A., & Ansari, D. (2015). Asymmetric Processing of Numerical and Nonnumerical Magnitudes in the Brain: An fMRI Study. Journal of Cognitive Neuroscience. Retrieved from https://www.researchgate.net/profile/Stephan_Vogel3/publication/282760257_Asymmetric_Processing_of_Numerical_and_Nonnumerical_Magnitudes_in_the_Brain_An_fMRI_Study/links/563c55a708ae34e98c485095.pdf
  27. Lowery, N. V. (2002). Construction of teacher knowledge in context: Preparing elementary teachers to teach mathematics and science. School Science and Mathematics, 102(2), 68–83. doi: 10.1111/j.1949-8594.2002.tb17896.x
  28. Lozano, M. (2005). Programas y experiencias en popularización de la ciencia y la tecnología. Bogotá, Colombia: Andrés Bello.
  29. Lyons, I.M., Ansari, D. & Beilock, S.L. (2015) Qualitatively different coding of symbolic and nonsymbolic numbers in the human brain. Human Brain Mapping, 26, 475-488. Retrieved from https://drive.google.com/file/d/0B5JyY7bDUjP_aU42N3NjTVJ1MmM/view
  30. Lyons, I.M. & Ansari, D. (2015) Numerical Order Processing in Children: From Reversing the Distance-Effect to Predicting Arithmetic. Mind, Brain and Education, 9, 207-21. Retrieved from https://drive.google.com/open?id=0B9pmFjYNdQeSNlJmaFo4XzV0OEE
  31. Lyons, I.M., Price, G.R., Vaessen, A., Blomert, L. & Ansari, D. (2014) Numerical predictors of arithmetic success in grades 1-6. Developmental Science, 17, 714-26. Retrieved from https://drive.google.com/file/d/0B5JyY7bDUjP_OGpGdjRkRHJrNE0/edit?usp=sharing
  32. Matejko, A. & Ansari, D. (2015) Drawing Connections Between White Matter and Numerical and Mathematical Cognition: A Literature Review. Neuroscience & Biobehavioral Reviews, 48C, 35-52. Retrieved from https://drive.google.com/file/d/0B5JyY7bDUjP_bFVZcUFXbzltdk0/view?usp=sharing
  33. Millar, R. (1991). Why is science hard to learn? Journal of Computer Associated Learning, 7(2), 66–74. doi: 10.1111/j.1365-2729.1991.tb00229.x
  34. Miranda, L. (2010). On trends and rhythms in scientific and technological knowledge evolution: A quantitative analysis. International Journal of Technology Intelligence and Planning, 6(1), 76–109.
  35. National Center for Learning Disabilities. (2012). Dyscalculia. [video]. Retrieved from https://www.youtube.com/watch?v=HVf_OHK2hHQ&list=PL60hj6d9o_BaLC3TZ_lar8gGTqbqk5lNR&index=2 
  36. National Mathematics Advisory Panel. (2008). Foundations for success: The final report of the National Mathematics Advisory Panel. Washington, DC: U.S. Department of Education.
  37. Numerical Cognition Laboratory. (n.d.). Numeracy Screener. Available at http://www.numeracyscreener.org/home.html
  38. O'Boyle, M. W., Cunnington, R., Silk, T. J., Vaughan, D., Jackson, G., Syngeniotis, A., & Egan, G. F. (2005). Mathematically gifted male adolescents activate a unique brain network during mental rotation. Cognitive Brain Research, 25(2), 583-587. Retrieved from https://www.depts.ttu.edu/hs/hdfs/docs/faculty/mathematically-gifted-male-adolescents.pdf
  39. Prescott, J., Gavrilescu, M., Cunnington, R., O'Boyle, M. W., & Egan, G. F. (2010). Enhanced brain connectivity in math-gifted adolescents: An fMRI study using mental rotation.Cognitive neuroscience, 1(4), 277-288.
  40. Price, G. R., Mazzocco, M. M., & Ansari, D. (2013). Why mental arithmetic counts: brain activation during single digit arithmetic predicts high school math scores. The Journal of Neuroscience, 33(1), 156-163. Retrieved from http://www.jneurosci.org/content/33/1/156.full
  41. Ryve, A. (2011). Discourse research in mathematics education: A critical evaluation of 108 journal articles. Journal of Research in Mathematics Education, 42(2), 167–199.
  42. Sjoberg, S., & Schreiner, C. (2008). Young people, science and technology attitudes, values and interest and possible recruitment. Brussels: ROSE.
  43. Steele, J. R., & Ambady, N. (2006). “Math is hard!” The effect of gender priming on women’s attitudes. Journal of Experimental Social Psychology, 42(4), 428–436. doi: 10.1016/j.jesp.2005.06.003
  44. Suárez-Pellicioni, M., Núñez-Peña, M. I., & Colomé, À. (2013). Abnormal error monitoring in math-anxious individuals: evidence from error-related brain potentials. PloS one,8(11), e81143. Retrieved from http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0081143
  45. Supekar, K., Iuculano, T., Chen, L., & Menon, V. (2015). Remediation of Childhood Math Anxiety and Associated Neural Circuits through Cognitive Tutoring. The Journal of Neuroscience, 35(36), 12574-12583. Retrieved from http://scsnl.stanford.edu/documents/supekar_2015_jneuro.pdf
  46. Supekar, K., Swigart, A. G., Tenison, C., Jolles, D. D., Rosenberg-Lee, M., Fuchs, L., & Menon, V. (2013). Neural predictors of individual differences in response to math tutoring in primary-grade school children. Proceedings of the National Academy of Sciences, 110(20), 8230-8235. Retrieved from http://www.pnas.org/content/110/20/8230.full
  47. Thomson Reuters. (2011). National science indicators. Retrieved January 12, 2011,from http://thomsonreuters.com/products_services/science/science_products/az/national_science_indicators/
  48. Titus, G. (2008). U.S. competitiveness in science and technology. Pittsburgh, PA: RAND.
  49. University of Western Ontario. (2016). Numerical cognition lab. Available at http://www.numericalcognition.org/
  50. Williams, J. (2011). Looking back, looking forward: Valuing post-compulsory mathematics education. Research in Mathematics Education, 13(2), 213–222. doi: 10.1080/14794802.2011.585831
  51. Yanowitz, K. L. (2010). Using analogies to improve elementary school students’ inferential reasoning about scientific concepts. School Science and Mathematics, 101(3), 133–142. doi: 10.1111/j.1949-8594.2001.tb18016.x
  52. Young, C. B., Wu, S. S., & Menon, V. (2012). The neurodevelopmental basis of math anxiety. Psychological Science, 0956797611429134. Retrieved from https://www.researchgate.net/profile/Math_Cuajungco/publication/10969791_Zinc_takes_the_center_stage_its_paradoxical_role_in_Alzheimer's_disease/links/09e4150306a678e488000000.pdf