Updated: Apr 6
Strength & Conditioning coaches, personal trainers and many other practitioners in the field are often challenged with teaching their athletes and clients a new skill. A skill reflects the ability to control the body accurately, efficiently and in a timely manner (1). Teaching a new skill to someone can be a challenging task. The use of different practice scheduling techniques can have a huge impact on the learning curve of the practitioner and a systematic approach that orientates itself relative to the practitioner’s skill level can potentially promote efficient and long-term skill acquisition. Traditionally, three common practice scheduling designs exist that are being investigated in the scientific literature: blocked, serial and random (2). In a blocked practice design the same movement is repeated for a defined number of repetitions before moving on to another movement (e.g.: AAA – BBB – CCC). In the serial practice design several movements are being practiced directly after one another and that sequence is repeated for a defined number of sets (e.g.: ABC – ABC – ABC). Lastly, in random practice, as the name suggests, we observe un-planned or irregular scheduling and create a scenario where the learner cannot or will not anticipate or plan which movement is going to be executed next (e.g.: ACB – CCA – ABA).
A phenomenon, known as the contextual interference effect (CI), is often discussed alongside skill acquisition and practice scheduling designs and proposed to be advantageous for learning (3). It is defined as follows:
“The effect on learning on the degree of functional interference found in practice situation when several tasks must be learned and practiced together.” (2)
“The interference in performance and learning that arises from practicing one task in the context of other tasks.” (4)
The research investigating this field is primarily suggesting cognitive processes being associated with the CI effect (3). The two theories that are predominantly discussed are:
1. Forgetting-Reconstruction hypothesis (5):
Lee and Magill (5) discuss that the learning advantage comes from the frequent reconstruction processes in between practice trials. In other words, high amounts of CI cause the learner to constantly forget movement relevant information between practice trials and therefore requiring him to reconstruct these before every new trial and this process is proposed to improve skill acquisition (3). Example: Skills A, B, C are being practiced serially. Because of the high complexity of skill B and C, the practitioner forgets how to exactly perform skill A in the next attempt and must reconstruct an action plan for A.
2. Elaboration hypothesis (6,7):
Another theory is discussed by Shea and colleagues (6,7) according to which high CI causes the performer to contrast and compare the skills being practiced consequently leading to a more elaborative and distinctive representation of motor skill memory (3). Example: Skills A, B, C are being practiced serially. All three skills are different but share some common characteristics. As the practitioner now moves through the series of skills, they compare A to B to C and find similarities and differences which, in turn, allows them to better understand the individual skills themselves and improves the process of skill acquisition.
More recently, neurophysiological research has been able to support these theories proposing that increased neural activity is associated with greater cognitive processes (3) and high CI was found to lead to increased excitability in the primary cortex (8,9). However, the transferability of these findings to the applied setting is still questionable and warrants further research (3). It can be assumed that blocked practice designs offer the least amount of CI and a random design the highest amounts of CI with serial practice being somewhere in the middle.
Before moving on to contemporary research in the field of skill acquisition and practice scheduling techniques let us look at figure 1 visualising the most important information so far discussed.
Ollis and colleagues (10) investigated the effect of CI on skill acquisition, retention and transfer abilities in knot tying skills. They investigated skill acquisition, assessing the performance of the skill practiced, retention, assessing the ability to still perform the skill after a period without practice, and transfer, assessing the ability to transfer the skill performance in a different or varied task-context. Interestingly, the subjects practicing under the highest CI conditions were reported to have better results in the retention and transfer tests than their counterparts practicing with lower CI conditions which generally supports the idea of high task complexity being beneficial in learning and skill acquisition.
Despite a general trend favouring random practice design with high complexity in closed skill settings, caution must be taken regarding the transferability from very controlled and laboratory settings to the applied sports scenario. In their review Barreiros and colleagues (11) discuss how around 60% of the studies in applied sport setting fail to report positive effects CI on long-term skill acquisition. It was proposed that tasks in sports settings are hard to compare to very controlled laboratory conditions and athletes could be overwhelmed with too much complexity in the early phase of learning. According the Gentile’s two stage model of learning (12) it is crucial in the first phase of learning to understand what the movement is and what actions it involves before reaching the three goals of adaptability, consistency and efficiency in the second stage.
To give an example deliberatively exaggerating a novice (not used to weightlifting) trying to learn the snatch is considered. Theoretically, if that person does not understand how to simultaneously triple extend in the ankles, knees and hips they will have a very hard time practicing the snatch. If that person is then asked to not only perform a snatch but also a clean and jerk followed by complex of weightlifting derivatives only to create more complexity it is not going to help that person acquiring a new skill but just hinder their ability to learn. This is not to say that working with complexity is wrong. It should rather leave room to the question of how much complexity is needed to create an effective learning environment?
Two concepts that might prove an answer to this question will be discussed as follows (see figure 2a).
1. The concept of desirable difficulty suggests that an optimal learning environment challenges the practitioner at an appropriate level without overloading them (13).
2. The challenge point hypothesis explains an equation of how to assume the difficulty of a task (14).
Task difficulty (TD) = nominal TD x functional TD.
Nominal TD is fixed being the task that is practiced. Functional TD is variable being the dependent on the skill of learner and can therefore change as the learner might become more skilful leading to a decrease in functional TD for that person while the nominal TD remains the same.
Bringing these two concepts together it is suggested that the complexity of the practice environment should be relative to the skill of the learner and can be increased over time as the learner becomes more skilled. This approached is called increased practice design (see figure 2b). Porter and Magill (15) compared increased practice scheduling, comprising of initial blocked, followed by serial and then random practice, with traditional blocked-only and random-only designs. In this study, the subjects practicing with gradual increase in CI had superior performances in skill acquisition as well as retention and transfer tests than the control groups indicating that systematic increases in complexity facilitate skill learning.
Figure 2- a) Overview of concept of desirable difficulty and challenge-point hypothesis. b) Graphic visualisation of increased practice design. Orange curve representing nominal TD (fixed) and green curve representing functional TD (variable). As more time is spent practicing, the learner becomes more skilful in what he is practicing leading to a reduction in functional TD. To maintain a level of complexity and add contextual interference a more demanding skill is introduced (represented by the black arrows) leading to an increase in functional TD.
The increased practice approach is frequently used in the applied setting. For instance, Jeffreys (16) created a framework for skill acquisition in team sport agility which resembles this approach. The first step is taken by identifying and developing target movement patterns in a blocked design, such as basic movement patterns, like running straight. Secondly, key movement combinations will be trained serially, such as side-shuffling and then accelerating to a straight sprint. And thirdly, key stimuli and subsequent reactions will be added to create a competition-like chaotic scenario with open drills and constantly changing demands, such as partner shadow-runs, which accounts for the random practice approach.
Another anecdotal example is taken from the work of the renowned movement teacher Ido Portal. His method comprises of the elements of isolation, integration and improvisation.
Isolation being comparable to blocked practice, where a single movement is practiced until a certain level of competency is achieved (see below isolation practice for three elements: bridge, QDR, backspin).
Next comes integration, being representative of serial practice, it entails the connection of single movement elements leading to a flow of movements (see below).
Lastly, improvisation (i.e. random practice) represents the highest form of practice requiring the practitioner to move without planning or anticipating while being in constant interaction with their environment (see below).
In conclusion, there is a clear trend from both research (15) and the applied setting (16; Ido Portal) indicating that an increased practice scheduling relative to the competence of the learner leads to superior skill acquisition as well as retention and transfer performance when compared to more traditional and fixed practice scheduling approaches.
1. Cleather, D. (2018). The Little Black Book of Training Wisdom: How to Train to Improve at Any Sport. CreateSpace Independent Publishing Platform.
2. Magill, R. A., & Hall, K. G. (1990). A review of the contextual interference effect in motor skill acquisition. Human Movement Science, 9(3–5), 241–289. https://doi.org/10.1016/0167-9457(90)90005-X
3. Farrow, D., & Buszard, T. (2017). Exploring the applicability of the contextual interference effect in sports practice. In Progress in Brain Research (Bd. 234, S. 69–83). Elsevier. https://doi.org/10.1016/bs.pbr.2017.07.002
4. Schmidt, R. A., & Lee, T. D. (2005). Motor control and learning: A behavioral emphasis, 4th ed. Human Kinetics.
5. Lee, T. D., & Magill, R. A. (1985). Can Forgetting Facilitate Skill Acquisition? In D. Goodman, R. B. Wilberg, & I. M. Franks (Hrsg.), Advances in Psychology (Bd. 27, S. 3–22). North-Holland. https://doi.org/10.1016/S0166-4115(08)62528-5
6. Shea, J. B., & Morgan, R. L. (1979). Contextual interference effects on the acquisition, retention, and transfer of a motor skill. Journal of Experimental Psychology: Human Learning and Memory, 5(2), 179–187. https://doi.org/10.1037/0278-73188.8.131.52
7. Shea, J. B., & Zimny, S. T. (1983). Context Effects in Memory and Learning Movement Information. In R. A. Magill (Hrsg.), Advances in Psychology (Bd. 12, S. 345–366). North-Holland. https://doi.org/10.1016/S0166-4115(08)61998-6
8. Cross, E. S., Schmitt, P. J., & Grafton, S. T. (2007). Neural Substrates of Contextual Interference during Motor Learning Support a Model of Active Preparation. Journal of Cognitive Neuroscience, 19(11), 1854–1871. https://doi.org/10.1162/jocn.2007.19.11.1854
9. Lin, C.-H. (Janice), Winstein, C. J., Fisher, B. E., & Wu, A. D. (2010). Neural Correlates of the Contextual Interference Effect in Motor Learning: A Transcranial Magnetic Stimulation Investigation. Journal of Motor Behavior, 42(4), 223–232. https://doi.org/10.1080/00222895.2010.492720
10. Ollis, S., Button, C., & Fairweather, M. (2005). The influence of professional expertise and task complexity upon the potency of the contextual interference effect. Acta Psychologica, 118(3), 229–244. https://doi.org/10.1016/j.actpsy.2004.08.003
11. Barreiros, J., Figueiredo, T., & Godinho, M. (2007). The contextual interference effect in applied settings. European Physical Education Review, 13(2), 195–208. https://doi.org/10.1177/1356336X07076876
12. Gentile, A. M. (1972). A Working Model of Skill Acquisition with Application to Teaching. Quest, 17(1), 3–23. https://doi.org/10.1080/00336297.1972.10519717
13. Bjork, R. A. (1999). Assessing our own competence: Heuristics and illusions. In Attention and performance XVII: Cognitive regulation of performance: Interaction of theory and application (S. 435–459). The MIT Press.
14. 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
15. Porter, J. M., & Magill, R. A. (2010). Systematically increasing contextual interference is beneficial for learning sport skills. Journal of Sports Sciences, 28(12), 1277–1285. https://doi.org/10.1080/02640414.2010.502946
16. Jeffreys, I. (2006). Motor Learning—Applications for Agility, Part 2. Strength & Conditioning Journal, 28(6), 10–14.