Learning by observing and imitating others has long been recognized as constituting a powerful learning strategy for humans. Recent findings from neuroscience research, more specifically on the mirror neuron system, begin to provide insight into the neural bases of learning by observation and imitation. These findings are discussed here, along with their potential consequences for the design of instruction, focusing in particular on the effectiveness of dynamic vs. static visualizations.
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The terms observational learning and imitation learning are often used interchangeably, but they may be distinguished in that learning may occur without imitation taking place, that is, we may learn by observing and generating inferences beyond the observation without actually imitating the observed model (Bandura 1986). Because it is broader, we will use the term “observational learning” throughout this article.
It is important to define expertise here. Some authors define “experts” as being individuals who excel in a domain (Ericsson and Lehmann 1996), others as individuals with extensive experience in a domain (Chi et al. 1988), but in educational research, it is also often used to refer to individuals who can perform a particular task really well (e.g., as in the “expertise reversal effect”; Kalyuga et al. 2003). This can have important consequences for the effectiveness of models, because domain experts differ enormously from students in the amount of knowledge they have, in the way this knowledge is organized, and the extent to which experts have automated problem-solving procedures (Chi et al. 1988). Therefore, having domain experts as a model might not help students, because the knowledge gap is too large, whereas task experts might be effective models. The issue can be resolved by consistently using the term “expertise” in a relative rather than absolute sense. In this paper, the term “expertise” should be considered in terms of “levels of expertise” rather than absolute expertise. One instructional technique may facilitate expertise more than another because it increases knowledge more irrespective of the absolute level of expertise, and an expert can be someone with a higher level of expertise than the learner.
Arguel, A., & Jamet, E. (2008). Using video and static pictures to improve learning of procedural contents. Computers in Human Behavior, in press.
Ayres, P., Marcus, N., Chan, C., & Qian, N. (2008). Learning hand manipulative tasks: When instructional animations are superior to equivalent static representations. Computers in Human Behavior, in press.
Ayres, P., & Paas, F. (Eds.). (2007a). A cognitive load approach to the learning effectiveness of instructional animation. Applied Cognitive Psychology, 21(6), special issue.
Ayres, P., & Paas, F. (2007b). Making instructional animations more effective: A cognitive load approach. Applied Cognitive Psychology, 21, 695–700. doi:10.1002/acp.1343.
Ayres, P., & Sweller, J. (2005). The split-attention principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 135–146). New York: Cambridge University Press.
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.
Barouillet, P., & Camos, V. (2007). The time-based resource-sharing model of working memory. In N. Osaka, R. H. Logie, & M. D’Esposito (Eds.), The cognitive neuroscience of working memory (pp. 59–80). Oxford: Oxford University Press.
Braaksma, M., Rijlaarsdam, G. C. W., Van den Bergh, H., & Van Hout-Wolters, B. A. M. (2004). Observational learning and its effects on the orchestration of the writing process. Cognition and Instruction, 22, 1–36. doi:10.1207/s1532690Xci2201_1.
Buccino, G., Vogt, S., Ritzl, A., Fink, G., Zilles, K., Freund, H., et al. (2004). Neural circuits underlying imitation learning of hand actions: An event-related fMRI study. Neuron, 42, 323–334. doi:10.1016/S0896-6273(04)00181-3.
Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13, 145–182.
Chi, M. T. H., Glaser, R., & Farr, M. (Eds.).(1988). The nature of expertise. Hillsdale, NJ: Erlbaum.
Cooper, G., & Sweller, J. (1987). The effects of schema acquisition and rule automation on mathematical problem-solving transfer. Journal of Educational Psychology, 79, 347–362. doi:10.1037/0022-06126.96.36.1997.
Cooper, G., Tindall-Ford, S., Chandler, P., & Sweller, J. (2001). Learning by imagining. Journal of Experimental Psychology. Applied, 7, 68–82. doi:10.1037/1076-898X.7.1.68.
Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. The Behavioral and Brain Sciences, 24, 87–114. doi:10.1017/S0140525X01003922.
Craighero, L., Bello, A., Fadiga, L., & Rizzolatti, G. (2002). Hand action preparation influences the responses to hand pictures. Neuropsychologia, 40, 492–502. doi:10.1016/S0028-3932(01)00134-8.
De Jong, T., Van Gog, T., Jenks, K., Manlove, S., Van Hell, J., Jolles, J., et al. (2008). Explorations in learning and the brain: On the potential of cognitive neuroscience for education. in press. Berlin: Springer. Also available (downloaded July 28, 2008) from http://www.nwo.nl/files.nsf/pages/NWOA_7GFD3Y/$file/Explorations_in_Learning_and_the_Brain.pdf.
Detterman, D. K., & Sternberg, R. J. (Eds.).(1993). Transfer on trial: Intelligence, cognition, and instruction. Norwood, NJ: Ablex.
Di Pellegrino, G., Fadiga, L., Fogassi, L., Gallese, V., & Rizzolatti, G. (1992). Understanding motor events: A neurophysiological study. Experimental Brain Research, 91, 176–180. doi:10.1007/BF00230027.
Ericsson, K. A., & Lehmann, A. C. (1996). Expert and exceptional performance: Evidence for maximal adaptation to task constraints. Annual Review of Psychology, 47, 273–305. doi:10.1146/annurev.psych.47.1.273.
Fadiga, L., Fogassi, L., Pavesi, G., & Rizzolatti, G. (1995). Motor facilitation during action observation: A magnetic stimulation study. Journal of Neurophysiology, 73, 2608–2611.
Fischer, S., Lowe, R. K., & Schwan, S. (2008). Effects of presentation speed of a dynamic visualization on the understanding of a mechanical system. Applied Cognitive Psychology, in press.
Gazzola, V., Rizzolatti, G., Wicker, B., & Keysers, C. (2007). The anthropomorphic brain: The mirror neuron system responds to human and robotic actions. NeuroImage, 35, 1674–1684. doi:10.1016/j.neuroimage.2007.02.003.
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.
Gergely, G., Bekkering, H., & Király, I. (2002). Rational imitation in preverbal infants: Babies may opt for a simpler way to turn on a light after watching an adult do it. Nature, 415, 755.
Goswami, U. (2004). Neuroscience and education. The British Journal of Educational Psychology, 74, 1–14. doi:10.1348/000709904322848798.
Grèzes, J., & Decety, J. (2001). Functional anatomy of execution, mental simulation, observation, and verb generation of actions: A meta-analysis. Human Brain Mapping, 12, 1–19. doi:10.1002/1097-0193(200101)12:1<1::AID-HBM10>3.0.CO;2-V.
Hegarty, M., Kriz, S., & Cate, C. (2003). The roles of mental animations and external animations in understanding mechanical systems. Cognition and Instruction, 21, 325–360. doi:10.1207/s1532690xci2104_1.
Höffler, T. N., & Leutner, D. (2007). Instructional animation versus static pictures: A meta-analysis. Learning and Instruction, 17, 722–738. doi:10.1016/j.learninstruc.2007.09.013.
Hurley, S. (2008). The shared circuits model (SCM): How control, mirroring, and simulation can enable imitation, deliberation, and mindreading. The Behavioral and Brain Sciences, 31, 1–58. doi:10.1017/S0140525X07003123.
Iacoboni, M., Woods, R., Brass, M., Bekkering, H., Mazziotta, J., & Rizzolatti, G. (1999). Cortical mechanisms of human imitation. Science, 286, 2526–2528. doi:10.1126/science.286.5449.2526.
Jeon, U. H., & Branson, R. K. (1981). Performance and simulated performance test results as a function of instruction by still and motion visuals. Journal of Educational Technology Systems, 10, 33–44.
Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38, 23–32. doi:10.1207/S15326985EP3801_4.
Katzir, T., & Paré-Blagoev, J. (2006). Applying cognitive neuroscience research to education: The case of literacy. Educational Psychologist, 41, 53–74. doi:10.1207/s15326985ep4101_6.
Keysers, C., & Gazzola, V. (2007). Integrating simulation and theory of mind: From self to social cognition. Trends in Cognitive Sciences, 11, 194–196. doi:10.1016/j.tics.2007.02.002.
Kitsantas, A., Zimmerman, B. J., & Cleary, T. (2000). The role of observation and emulation in the development of athletic self-regulation. Journal of Educational Psychology, 92, 811–817. doi:10.1037/0022-06188.8.131.521.
Leahy, W., & Sweller, J. (2005). Interactions among the imagination, expertise reversal and element interactivity effects. Journal of Experimental Psychology. Applied, 11, 266–276. doi:10.1037/1076-898X.11.4.266.
Leahy, W., & Sweller, J. (2008). The imagination effect increases with an increased intrinsic cognitive load. Applied Cognitive Psychology, 22, 273–283. doi:10.1002/acp.1373.
Lowe, R. K., & Schnotz, W. (Eds.). (2008). Learning with animation: Research implications for design. New York: Cambridge University Press.
Mayer, R. E. (Ed.).(2005). The Cambridge handbook of multimedia learning. New York: Cambridge University Press.
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity to process information. Psychological Review, 63, 81–97. doi:10.1037/h0043158.
OECD. (2007). Understanding the brain: The birth of a learning science. Paris: Office of Economic Cooperation and Development.
Osaka, N., Logie, R. H., & D’Esposito, M. (Eds.).(2007). The cognitive neuroscience of working memory. Oxford: Oxford University Press.
Paas, F. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive load approach. Journal of Educational Psychology, 84, 429–434. doi:10.1037/0022-06184.108.40.2069.
Paas, F., & Van Merriënboer, J. J. G. (1994). Variability of worked examples and transfer of geometrical problem-solving skills: A cognitive load approach. Journal of Educational Psychology, 86, 122–133. doi:10.1037/0022-06220.127.116.11.
Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38, 1–4. doi:10.1207/S15326985EP3801_1.
Paas, F., Renkl, A., & Sweller, J. (2004). Cognitive load theory: Instructional implications of the interaction between information structures and cognitive architecture. Instructional Science, 32, 1–8. doi:10.1023/B:TRUC.0000021806.17516.d0.
Park, O., & Hopkins, R. (1993). Instructional conditions for using dynamic visual displays: A review. Instructional Science, 21, 427–449. doi:10.1007/BF00118557.
Renkl, A. (1997). Learning from worked-out examples: A study on individual differences. Cognitive Science, 21, 1–29.
Rizzolatti, G. (2005). The mirror neuron system and its function in humans. Anatomy and Embryology, 210, 419–421. doi:10.1007/s00429-005-0039-z.
Rizzolatti, G., & Craighero, L. (2004). The mirror-neuron system. Annual Review of Neuroscience, 27, 169–192. doi:10.1146/annurev.neuro.27.070203.144230.
Schnotz, W., & Lowe, R. K. (Eds.). (2003). External and internal representations in multimedia learning. Learning and Instruction, 13(2), special issue.
Stern, E., Grabner, R., & Schumacher, R. (2006). Educational research and neurosciences - Expectations, evidence, research prospects. Berlin: Federal Ministry of Education and Research.
Sweller, J. (1988). Cognitive load during problem-solving: Effects on learning. Cognitive Science, 12, 257–285.
Sweller, J. (2004). Instructional design consequences of an analogy between evolution by natural selection and human cognitive architecture. Instructional Science, 32, 9–31. doi:10.1023/B:TRUC.0000021808.72598.4d.
Sweller, J., & Cooper, G. A. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2, 59–89. doi:10.1207/s1532690xci0201_3.
Sweller, J., & Sweller, S. (2006). Natural information processing systems. Ecological Psychology, 4, 434–458.
Sweller, J., Van Merriënboer, J. J. G., & Paas, F. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10, 251–295. doi:10.1023/A:1022193728205.
Tai, Y., Scherfler, C., Brooks, D., Sawamoto, N., & Castiello, U. (2004). The human premotor cortex is “mirror” only for biological actions. Current Biology, 14, 117–120. doi:10.1016/j.cub.2004.01.005.
Tettamanti, M., Buccino, G., Saccuman, M. C., Gallese, V., Danna, M., Scifo, P., et al. (2005). Listening to action-related sentences activates fronto-parietal motor circuits. Journal of Cognitive Neuroscience, 17, 273–281. doi:10.1162/0898929053124965.
Tversky, B., Morrison, J. B., & Betrancourt, M. (2002). Animation: can it facilitate? International Journal of Human–Computer Studies, 57, 247–262. doi:10.1006/ijhc.2002.1017.
Van Gog, T., Paas, F., & Van Merriënboer, J. J. G. (2004). Process-oriented worked examples: Improving transfer performance through enhanced understanding. Instructional Science, 32, 83–98. doi:10.1023/B:TRUC.0000021810.70784.b0.
Van Gog, T., Paas, F., & Van Merriënboer, J. J. G. (2006). Effects of process-oriented worked examples on troubleshooting transfer performance. Learning and Instruction, 16, 154–164. doi:10.1016/j.learninstruc.2006.02.003.
Van Gog, T., Paas, F., & Van Merriënboer, J. J. G. (2008). Effects of studying sequences of process-oriented and product-oriented worked examples on troubleshooting transfer efficiency. Learning and Instruction, 18, 211–222. doi:10.1016/j.learninstruc.2007.03.003.
Van Merriënboer, J. J. G., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17, 147–177. doi:10.1007/s10648-005-3951-0.
Vogt, S., Taylor, P., & Hopkins, B. (2003). Visuomotor priming by pictures of hand postures: perspective matters. Neuropsychologia, 41, 941–951. doi:10.1016/S0028-3932(02)00319-6.
Wetzel, C. D., Radtke, P. H., & Stern, H. W. (1994). Instructional effectiveness of video media. Hillsdale, NJ: Erlbaum.
Wong, A., Marcus, N., Ayres, P., Smith, L., Cooper, G.A., Paas, F., et al. (2008). Instructional animations can be superior to statics when learning human motor skills. Computers in Human Behavior, in press.
Wouters, P., Paas, F., & Van Merriënboer, J. J. G. (2008). How to optimize learning from animated models? A review of guidelines based on cognitive load. Review of Educational Research, 78, 645–675.
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van Gog, T., Paas, F., Marcus, N. et al. The Mirror Neuron System and Observational Learning: Implications for the Effectiveness of Dynamic Visualizations. Educ Psychol Rev 21, 21–30 (2009). https://doi.org/10.1007/s10648-008-9094-3
- Mirror neuron system
- Observational learning
- Cognitive load