Cognitive Contributions to Adolescent Motor Learning
A Literature Review
DOI:
https://doi.org/10.58445/rars.4166Keywords:
Working Memory, Processing Speed, Motor Sequence Acquisition, AdolescentsAbstract
Motor skill acquisition underlies both everyday and specialized human activities, yet individuals differ substantially in the rate at which they learn new movements. Although Fitts and Posner's (1967) three-stage model describes a universal trajectory from conscious to automatic performance, it does not explain why some learners progress through these stages faster than others. Cognitive ability has been proposed as one source of this variability, with working memory (WM) and processing speed implicated as the components most relevant to motor learning. However, most existing research has sampled children or adults, with adolescents underrepresented, and few studies directly compare higher and average-intelligence groups on learning rate rather than final performance. This review outlines currently known contributions of working memory and processing speed to motor learning rate, which appear to vary across learning stages, task types, and practice conditions. Implications for how cognitive ability should be considered in research and practice with adolescent populations are discussed.
References
Ackerman, P. L., & Beier, M. E. (2007). Further explorations of perceptual speed abilities in the context of assessment methods, cognitive abilities, and individual differences during skill acquisition. Journal of Experimental Psychology. Applied, 13(4), 249-272. https://doi.org/10.1037/1076-898X.13.4.249
Ackerman, P. L., & Cianciolo, A. T. (2000). Cognitive, perceptual-speed, and psychomotor determinants of individual differences during skill acquisition. Journal of Experimental Psychology: Applied, 6(4), 259-290. https://doi.org/10.1037//1076-898x.6.4.259
Anderson, D. I., Lohse, K. R., Lopes, T. C. V., & Williams, A. M. (2021). Individual differences in motor skill learning: Past, present and future. Human Movement Science, 78(1), 102818. https://doi.org/10.1016/j.humov.2021.102818
Anguera, J. A., Reuter-Lorenz, P. A., Willingham, D. T., & Seidler, R. D. (2010). Contributions of spatial working memory to visuomotor learning. Journal of Cognitive Neuroscience, 22(9), 1917-1930. https://doi.org/10.1162/jocn.2009.21351
Baddeley, A. (2012). Working memory: Theories, models, and controversies. Annual Review of Psychology, 63(1), 1-29. https://doi.org/10.1146/annurev-psych-120710-100422
Bao, R., Wade, L., Leahy, A. A., Owen, K. B., Hillman, C. H., Jaakkola, T., & Lubans, D. R. (2024). Associations between motor competence and executive functions in children and adolescents: A systematic review and meta-analysis. Sports Medicine, 54(8), 2141-2156. https://doi.org/10.1007/s40279-024-02040-1
Bo, J., & Seidler, R. D. (2009). Visuospatial Working Memory Capacity Predicts the Organization of Acquired Explicit Motor Sequences. Journal of Neurophysiology, 101(6), 3116-3125. https://doi.org/10.1152/jn.00006.2009
Buszard, T., Farrow, D., Verswijveren, S. J. J. M., Reid, M., Williams, J., Polman, R., Ling, F. C. M., & Masters, R. S. W. (2017). Working memory capacity limits motor learning when implementing multiple instructions. Frontiers in Psychology, 8, 1350. https://doi.org/10.3389/fpsyg.2017.01350
Chiaravalloti, N. D., Stojanovic-Radic, J., & DeLuca, J. (2013). The role of speed versus working memory in predicting learning new information in multiple sclerosis. Journal of Clinical and Experimental Neuropsychology, 35(2), 180-191. https://doi.org/10.1080/13803395.2012.760537
Choo, L., Novak, A., Impellizzeri, F. M., Porter, C., & Fransen, J. (2024a). Skill acquisition interventions for the learning of sports-related skills: A scoping review of randomised controlled trials. Psychology of Sport and Exercise, 72(1), 102615. https://doi.org/10.1016/j.psychsport.2024.102615
Fitts, P. M., & Posner, M. I. (1967). Human performance. Oxford, England: Brooks/Cole.
Ghorbanzadeh, B., Orangi, B. M., & Sahin, T. (2025). The relationship between motor competence and executive function as influenced by age, sex, and family socio-economic status. Frontiers in Psychology, 16(1). https://doi.org/10.3389/fpsy.g.2025.1544168
Guadagnoli, M. A., & Lee, T. D. (2004). Challenge point: A framework for conceptualizing the effects of various practice conditions in motor learning. Journal of Motor Behavior, 36(2), 212-224. https://doi.org/10.3200/jmbr.36.2.212-224
Han, Y. C., Thompson, K. R., & Reber, P. J. (2025). Evaluating individual differences in implicit perceptual-motor learning: A parallel assessments approach. Journal of Intelligence, 13(9), 115. https://doi.org/10.3390/jintelligence13090115
Kurre, M. L. (2018). Effect of brain hemisphere domination upon motor skill learning of cast. International Journal Peer Reviewed Journal Refereed Journal Indexed Journal UGC Approved Journal Impact Factor, 4(4), 50-51. https://wwjmrd.com/upload/effect-of-brain-hemisphere-domination-upon-motor-skill-learning-of-cast_1525867306.pdf
Lage, G. M., Bicalho, L. E. A., Machado, S., Lelis-Torres, N., Fernandes, L. A., & Apolinário-Souza, T. (2024). Motor learning and the interactions between working memory and practice schedule. Journal of Motor Behavior, 1(1), 1-11. https://doi.org/10.1080/00222895.2024.2374010
Maxwell, J. P., Masters, R. S. W., & Eves, F. F. (2003). The role of working memory in motor learning and performance. Consciousness and Cognition, 12(3), 376-402. https://doi.org/10.1016/s1053-8100(03)00005-9
Meissner, S. N., Keitel, A., Südmeyer, M., & Pollok, B. (2016). Implicit motor sequence learning and working memory performance changes across the adult life span. Frontiers in Aging Neuroscience, 8. https://doi.org/10.3389/fnagi.2016.00089
Ranganathan, R., Cone, S., & Fox, B. (2022). Predicting individual differences in motor learning: A critical review. Neuroscience and Biobehavioral Reviews, 141, 104852. https://doi.org/10.1016/j.neubiorev.2022.104852
Schneider, W. J., & McGrew, K. S. (2018). The Cattell-Horn-Carroll theory of cognitive abilities. In D. P. Flanagan & E. M. McDonough (Eds.), Contemporary intellectual assessment: Theories, tests, and issues (4th ed., pp. 73–130). Guilford Press.
Shishov, N., Melzer, I., & Bar-Haim, S. (2017). Parameters and measures in assessment of motor learning in Neurorehabilitation; a systematic review of the literature. Frontiers in Human Neuroscience, 11(82). https://doi.org/10.3389/fnhum.2017.00082
Skalaban, L. J., Cohen, A. O., Conley, M. I., Lin, Q., Schwartz, G. N., Ruiz-Huidobro, N. A. M., Cannonier, T., Martinez, S. A., & Casey, B. ਜ. (2022). Adolescent-specific memory effects: Evidence from working memory, immediate and long-term recognition memory performance in 8-30 yr olds. Learning & Memory, 29(8), 223-233. https://doi.org/10.1101/lm.053539.121
Sutapa, P., Pratama, K. W., Rosly, M. M., Ali, S. K. S., & Karakauki, M. (2021). Improving motor skills in early childhood through goal-oriented play activity. Children, 8(11), 1099. https://doi.org/10.3390/children8110994
Wang, L., & Wang, L. (2024). Relationships between motor skills and academic achievement in school-aged children and adolescents: A systematic review. Children, 11(3), 336. https://doi.org/10.3390/children11030336
Wu, Y. C., & Coulson, S. (2015). Iconic gestures facilitate discourse comprehension in individuals with superior immediate memory for body configurations. Psychological Science, 26(11), 1717-1727. https://doi.org/10.1177/0956797615597671
Downloads
Posted
Categories
License
Copyright (c) 2026 Research Archive of Rising Scholars

This work is licensed under a Creative Commons Attribution 4.0 International License.