Human Cognitive Architecture Through the Lens of Cognitive Load Theory

  • Jimmie Leppink
  • José Hanham


In this chapter, we explore how we think, learn and solve problems through the lens of cognitive load theory. Cognitive load theory is a contemporary theory for the design of education and training that incorporates principles derived from research on human cognitive architecture and evolutionary psychology. In cognitive load theory, two key components of human cognitive architecture are long-term memory and working memory. Long-term memory represents the knowledge base or information store that consists of knowledge structures or cognitive schemas that are the products of either evolutionary adaptation (biologically primary knowledge) or cultural advancement (biologically secondary knowledge). These structures or cognitive schemas typically comprise multiple elements of information that represent concepts, procedures and problem solutions. Expertise is intimately linked to that knowledge base in long-term memory. Working memory is the conscious information processing centre of our cognitive architecture and has natural processing constraints. The load arising from that information processing is also called working memory load or cognitive load. This chapter discusses types of cognitive load identified in a traditional and in a recently proposed framework and argues why the recent framework should be preferred. This chapter constitutes the theoretical foundation for Chaps.   2 (on expertise and problem solving) and   10 (on design guidelines) of this book.


  1. Aldekhyl, S., Cavalcanti, R. B., & Naismith, L. M. (2018). Cognitive load predicts point-of-care ultrasound simulator performance. Perspectives on Medical Education, 7, 23–32. CrossRefGoogle Scholar
  2. Antonenko, P., & Niederhauser, D. S. (2010). The effects of leads on cognitive load and learning in a hypertext environment. Computers in Human Behavior, 26, 140–150. CrossRefGoogle Scholar
  3. Antonenko, P., Paas, F., Grabner, R., & Van Gog, T. (2010). Using electroencephalography to measure cognitive load. Educational Psychology Review, 22, 425–438. CrossRefGoogle Scholar
  4. Baddeley, A. D. (2000). The episodic buffer: A new component of working memory? Trends in Cognitive Sciences, 4(11), 417–423. CrossRefGoogle Scholar
  5. Baddeley, A. D., & Hitch, G. J. (1974). Working memory. In G. H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 8, pp. 47–89). New York: Academic Press.Google Scholar
  6. Baddeley, A. D., Allen, R. J., & Hitch, G. J. (2011). Binding in visual working memory: The role of the episodic buffer. Neuropsychologia, 49, 1393–1400. CrossRefGoogle Scholar
  7. Barouillet, P., Bernardin, S., Portrat, S., Vergauwe, E., & Camos, V. (2007). Time and cognitive load in working memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33, 570–585. CrossRefGoogle Scholar
  8. Barouillet, P., Gavens, N., Vergauwe, E., Gaillard, V., & Camos, V. (2009). Working memory span development: A time-based resource-sharing model account. Developmental Psychology, 45, 477–490. CrossRefGoogle Scholar
  9. Bergman, E. M., De Bruin, A. B. H., Vorstenbosch, M. A. T. M., Kooloos, J. G. M., Puts, G. C. W. M., Leppink, J., et al. (2015). Effects of learning content in context on knowledge acquisition and recall: A pretest-posttest control group design. BMC Medical Education, 15, 133. CrossRefGoogle Scholar
  10. Bjork, R. A., Dunlosky, J., & Kornell, N. (2013). Self-regulated learning: Beliefs, techniques, and illusions. Annual Review of Psychology, 64, 417–444. CrossRefGoogle Scholar
  11. Chi, M., Glaser, R., & Rees, E. (1982). Expertise in problem solving. In R. Sternberg (Ed.), Advances in the psychology of human intelligence (pp. 7–75). Hillsdale, NJ: Erlbaum.Google Scholar
  12. Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Science, 24, 152–153. CrossRefGoogle Scholar
  13. Cowan, N. (2005). Working memory capacity. New York: Psychology Press.Google Scholar
  14. De Jong, T. (2009). Cognitive load theory, educational research, and instructional design: Some food for thought. Instructional Science, 38, 105–134. CrossRefGoogle Scholar
  15. Duncan, R. G. (2007). The role of domain-specific knowledge in generative reasoning about complicated multileveled phenomena. Cognition & Instruction, 25, 271–336. CrossRefGoogle Scholar
  16. Eagleman, D., & Downar, J. (2016). Brain and behaviour: A cognitive neuroscience perspective. New York: Oxford University Press.Google Scholar
  17. Ericsson, K. A., & Charness, N. (1994). Expert performance – Its structure and acquisition. American Psychologist, 49, 725–747. CrossRefGoogle Scholar
  18. Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. Psychological Review, 102, 211–245.CrossRefGoogle Scholar
  19. Geary, D. (2007). Educating the evolved mind: Conceptual foundations for an evolutionary educational psychology. In J. S. Carlson & J. R. Levin (Eds.), Psychological perspectives on contemporary educational issues (pp. 1–99). Greenwich, CT: Information Age Publishing.Google Scholar
  20. Geary, D. (2008). An evolutionary informed education science. Educational Psychologist, 43, 179–195. CrossRefGoogle Scholar
  21. Geary, D. (2012). Evolutionary educational psychology. In K. Harris, S. Graham, & T. Urdan (Eds.), APA educational psychology handbook (Vol. 1, pp. 597–621). Washington, DC: American Psychological Association.Google Scholar
  22. Hambrick, D. Z., & Meinz, E. J. (2013). Working memory capacity and musical skill. In T. P. Alloway & R. G. Alloway (Eds.), Working memory: The connected intelligence (pp. 137–156). New York: Taylor & Francis. CrossRefGoogle Scholar
  23. Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., & Van de Weijer, J. (2011). Eye-tracking: A comprehensive guide to methods and measures. Oxford: Oxford University Press.Google Scholar
  24. Kalyuga, S. (2011). Cognitive load theory: How many types of load does it really need? Educational Psychology Review, 23, 1–19. CrossRefGoogle Scholar
  25. Kalyuga, S. (2013). Enhancing transfer by learning generalized domain knowledge structures. European Journal of Psychology of Education, 28, 1477–1493. CrossRefGoogle Scholar
  26. Kalyuga, S. (2015). Instructional guidance: A cognitive load perspective. Charlotte, NC: Information Age Publishing.Google Scholar
  27. Kalyuga, S., & Hanham, J. (2011). Instructing in generalized knowledge structures to develop flexible problem solving skills. Computers in Human Behavior, 27, 63–68. CrossRefGoogle Scholar
  28. Kalyuga, S., & Singh, A. M. (2015). Rethinking the boundaries of cognitive load theory in complex learning. Educational Psychology Review, 2015, 831. CrossRefGoogle Scholar
  29. Kostons, D., Van Gog, T., & Paas, F. (2012). Training self-assessment and task-selection skills: A cognitive approach to improving self-regulated learning. Learning and Instruction, 22, 121–132. CrossRefGoogle Scholar
  30. Lafleur, A., Côté, L., & Leppink, J. (2015). Influences of OSCE design on students’ diagnostic reasoning. Medical Education, 49, 203–214. CrossRefGoogle Scholar
  31. Leppink, J. (2014). Managing the load on a reader’s mind. Perspectives on Medical Education, 3, 327–328. CrossRefGoogle Scholar
  32. Leppink, J. (2016). Cognitive load measures mainly have meaning when they are combined with learning outcome measures. Medical Education, 50, 979. CrossRefGoogle Scholar
  33. Leppink, J. (2017a). Cognitive load theory: Practical implications and an important challenge. Journal of Taibah University Medical Sciences, 12, 385–391 doi:10/1016/j.jtumed.2017.05.003.CrossRefGoogle Scholar
  34. Leppink, J. (2017b). Managing the load on a learner’s mind: A cognitive load theory perspective. Medical Science Educator, 27, 5. CrossRefGoogle Scholar
  35. Leppink, J., & Duvivier, R. (2016). Twelve tips for medical curriculum design from a cognitive load theory perspective. Medical Teacher, 38, 669–674. CrossRefGoogle Scholar
  36. Leppink, J., & Van den Heuvel, J. (2015). The evolution of cognitive load theory and its application to medical education. Perspectives on Medical Education, 4, 119–127. CrossRefGoogle Scholar
  37. Leppink, J., Paas, F., Van der Vleuten, C. P. M., Van Gog, T., & Van Merriënboer, J. J. G. (2013). Development of an instrument for measuring different types of cognitive load. Behavior Research Methods, 45, 1058–1072. CrossRefGoogle Scholar
  38. Leppink, J., Paas, F., Van Gog, T., Van der Vleuten, C. P. M., & Van Merriënboer, J. J. G. (2014). Effects of pairs of problems and examples on task performance and different types of cognitive load. Learning and Instruction, 30, 32–42. CrossRefGoogle Scholar
  39. Leppink, J., Van Gog, T., Paas, F., & Sweller, J. (2015). Chapter 18: Cognitive load theory: Researching and planning teaching to maximise learning. In J. Cleland & S. J. Durning (Eds.), Researching medical education (pp. 207–218). Chichester, UK: Wiley & Blackwell.CrossRefGoogle Scholar
  40. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81–97. CrossRefGoogle Scholar
  41. Naismith, L. M., & Cavalcanti, R. B. (2017). Measuring germane load requires correlation with learning. Medical Education, 51, 228. CrossRefGoogle Scholar
  42. Naismith, L. M., Cheung, J. J. H., Ringsted, C., & Cavalcanti, R. B. (2015). Limitations of subjective cognitive load measures in simulation-based procedural training. Medical Education, 49, 805–814. CrossRefGoogle Scholar
  43. Paas, F., & Sweller, J. (2012). An evolutionary upgrade of cognitive load theory: Using the human motor system and collaboration to support the learning of complex cognitive tasks. Educational Psychology Review, 24, 27–45. CrossRefGoogle Scholar
  44. Paas, F., Ayres, P., & Pachman, M. (2008). Assessment of cognitive load in multimedia learning: Theory, methods and applications. In D. H. Robinson & G. Schraw (Eds.), Recent innovations in educational psychology that facilitate student learning (pp. 11–35). Charlotte, NC: Information Age Publishing.Google Scholar
  45. Peterson, L., & Peterson, M. J. (1959). Short-term retention of individual verbal items. Journal of Experimental Psychology, 58, 193–198. CrossRefGoogle Scholar
  46. Sewell, J. L., Boscardin, C. K., Young, J. Q., Ten Cate, O., & O’Sullivan, P. S. (2016). Measuring cognitive load during procedural skills training with colonoscopy as an exemplar. Medical Education, 50, 682–692. CrossRefGoogle Scholar
  47. Shipstead, Z., Lindsey, D., Marshall, R., & Engle, R. (2014). The mechanisms of working memory capacity: Primary memory, secondary memory, and attention control. Journal of Memory and Language, 72, 116–141. CrossRefGoogle Scholar
  48. Sibbald, M., De Bruin, A. B. H., & Van Merriënboer, J. J. G. (2014). Twelve tips on engaging learners in checking health care decisions. Medical Teacher, 36, 111–115. CrossRefGoogle Scholar
  49. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285. CrossRefGoogle Scholar
  50. Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4, 295–312. CrossRefGoogle Scholar
  51. Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22, 123–138. CrossRefGoogle Scholar
  52. Sweller, J. (2012). Human cognitive architecture: Why some instructional procedures work and others do not. In K. Harris, S. Graham, & T. Urdan (Eds.), APA educational psychology handbook (Vol. 1, pp. 295–325). Washington, DC: American Psychological Association.Google Scholar
  53. Sweller, J. (2018). Measuring cognitive load. Perspectives on Medical Education, 7, 1–2. CrossRefGoogle Scholar
  54. Sweller, J., & Chandler, P. (1994). Why some material is difficult to learn. Cognition and Instruction, 12, 185–223. CrossRefGoogle Scholar
  55. Sweller, J., Chandler, P., Tierney, P., & Cooper, M. (1990). Cognitive load as a factor in the structuring of technical material. Journal of Experimental Psychology, 119, 176–192. CrossRefGoogle Scholar
  56. Sweller, J., Van Merriënboer, J. J. G., & Paas, F. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10, 251–296. CrossRefGoogle Scholar
  57. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. New York: Springer.CrossRefGoogle Scholar
  58. Tremblay, M. L., Lafleur, A., Leppink, J., & Dolmans, D. H. J. M. (2017). The simulated clinical environment: Cognitive and emotional impact among undergraduates. Medical Teacher, 39, 181–187. CrossRefGoogle Scholar
  59. Tricot, A., & Sweller, J. (2014). Domain-specific knowledge and why teaching generic skills does not work. Educational Psychology Review, 26, 265–283. CrossRefGoogle Scholar
  60. Underwood, G., Jebbert, L., & Roberts, K. (2004). Inspecting pictures for information to verify a sentence: Eye movements in general encoding and in focused search. Quarterly Journal of Experimental Psychology Section A-human Experimental Psychology, 57A, 165–182. CrossRefGoogle Scholar
  61. Van Gog, T., & Jarodzka, J. (2013). Eye tracking as a tool to study and enhance cognitive and metacognitive processes in computer-based learning environments. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 143–156). New York: Springer.Google Scholar
  62. Van Gog, T., & Scheiter, K. (2010). Eye tracking as a tool to study and enhance multimedia learning. Learning and Instruction, 20, 95–99. CrossRefGoogle Scholar
  63. Van Merriënboer, J. J. G., & Sweller, J. (2010). Cognitive load theory in health professions education: Design principles and strategies. Medical Education, 44, 85–93. CrossRefGoogle Scholar
  64. Whelan, R. R. (2007). Neuroimaging of cognitive load in instructional multimedia. Educational Psychology Review, 2, 1–12. CrossRefGoogle Scholar
  65. Young, J. Q., Van Merriënboer, J. J. G., Durning, S. J., & Ten Cate, O. (2014). Cognitive load theory: Implications for medical education. AMEE Guide No. 86. Medical Teacher, 36, 371–384. CrossRefGoogle Scholar
  66. Young, J. Q., Irby, D. M., Barilla-LaBarca, M. L., Ten Cate, O., & O’Sullivan, P. S. (2016). Measuring cognitive load: Mixed results from a handover simulation for medical students. Perspectives on Medical Education, 5, 24–32. CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Jimmie Leppink
    • 1
  • José Hanham
    • 2
  1. 1.Maastricht UniversityMaastrichtThe Netherlands
  2. 2.Western Sydney UniversityPenrithAustralia

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