A recent study on the physical and psychological practice and competition strategies of U.S. athletes showed that mental imagery use is one of the three “strongest predictors of successful Olympic performance” (the other two are emotional control and self-talk) (Taylor, Gould et al. 2008). This 3-part blog series will show you why.
Part 01: Motor Imagery Basics
Part 02: Types of Motor Imagery
Part 03: Motor Imagery in Practice
Imagine if there was a way to improve your double axel technique, make some needed strength gains, and even improve your flexibility all without doing any physical work? Would you make time for this activity during your training week? Research shows that, through the use of motor imagery, athletes can accomplish these tasks and more (2010, Guillot, Tolleron et al. 2010, Reiser, Busch et al. 2011, Rozand, Lebon et al. 2014). Motor Imagery is the act of visualizing one’s performance – without actually performing. The action takes place in the athlete’s mind. Motor imagery could very well be the most cost effective, safest, and beneficial activity to support training. Yet, few athletes actually practice motor imagery. Of those few athletes who do, even fewer seem to know how to use motor imagery effectively. Many coaches promote the activity but how many actually understand it? Do they know motor imagery is a skill that must be taught to athletes and that it takes time to develop effectively? We will explain what motor imagery is, how it works, and how to effectively apply it to support training.
Subconscious Prediction
To understand motor imagery first requires a basic understanding of what happens just before an individual performs an intended motor behavior. All planned motor behaviors, including an axel jump, combination spin, or picking up a cup of coffee from the table, are preceded by a prediction of the sensory consequences of the movement (Bar 2009, Hohwy 2013, Clark 2015, Engel, Friston et al. 2016). The prediction is sent in the form of a signal from the brain that travels down the nervous system and is met by incoming sensory information. The two signals are thought to meet and all the predictive information that accurately matches the incoming information is cancelled out. The error information (the incoming sensory signals not ‘explained away’ by the prediction) continues its way up the nervous system to inform the body, if time permits, to make adjustments. Ultimately, the predictive signal prepares the corresponding motor units responsible for the behavior to get ready for action and then the movement begins. This is called ‘priming’. It is almost as if the motor units start their engines to prepare for takeoff.
When a skater moves along into a triple axel, for example, the predictive activity begins. As they hold their back edge, the nervous system compares predictive signals with the actual sensory information coming in regarding how they should ‘feel’ and makes necessary adjustments. These feelings include the conditions of the ice, the skates, the type of lean the skater holds on the edge and so on. Then, just before the skater steps, their nervous system ‘predict’s the sensations of the triple Axel.
This explains how more experience (unique and variable learning experiences) leads to greater adaptability discussed in other blogs (Be Mindful of Repetition and Moves in the Field: A Substitute for Learning the Basics?). The more variety an athlete experiences during learning, the more broadly their nervous systems will understand the performance environment and better predict the consequences of an intended behavior. An athlete who practices that triple axel, for example, from a variety of set-ups, speeds, and locations equips their nervous system with greater ‘predictive power’ overall than an athlete who does not. The greater predictive power lends to greater adaptability in the event that something changes or feels ‘different’.
Prediction Through Motor Imagery
Interestingly, the predictive process also serves us in the absence of movement altogether. “In other words,” write Dijkstra and Zwaan (2014), “the experience of an event and the reconstruction of an event when it is being retrieved occurs in a similar way and with the same brain activation and same systems involved as during the original.” This includes when we contemplate our actions (Decety 1996, 2010), watch others perform (Buccino, Binofski et al. 2004, Buccino, Riggio et al. 2005, Tettamanti, Buccino et al. 2005, Boulenger, Roy et al. 2006), read an intense book or watch an exciting movie (2007), and when we dream (Revonsuo 2000, Coolidge and Wynn 2009). Motor Imagery leverages the sensory prediction process. When the imager deeply visualizes an axel, for example, their nervous system fires off the sensory predictions for that axel jump just as if the athlete were about to perform the skill!
As Max reads this book on off-ice jump technique the same sectors of his brain that correspond to the actual movements are activated. This activity forms the basis for motor imagery (photo artwork by Niko Cohen @hikikonekkon).
In part 02 we will discuss different types of motor imagery athletes can use to improve their skills.
Following the Imagery of Success series, we will present a massive 6 part series on ‘external focus of attention’ – a mode of attentional focus that applies motor imagery basics and we strongly endorse. All in all, you will get a whopping 9-part blog series that breaks down one of the most effective ways for athletes to train, that most people know about, but few people really understand!
References
(2007). Cognitive Psychology: mind and brain. Upper Saddle River, NJ, Pearson/ Prentice Hall.
(2010). The Neurophysiological Foundations of Mental and Motor Imagery. Oxford, UK, Oxford University Press.
Bar, M. (2009). “The proactive brain: memory for predictions.” Philosophical Transactions of the Royal Society of London B: Biological Sciences364(1521): 1235-1243.
Boulenger, V., A. C. Roy, Y. Paulignan, V. Deprez, M. Jeannerod and T. A. Nazir (2006). “Cross-talk between Language Processes and Overt Motor Behavior in the First 200 msec of Processing.” Journal of Cognitive Neuroscience18(10): 1607-1615.
Buccino, G., F. Binofski and L. Riggio (2004). “The miror neuron system and action recognition.” Brain Language89(2): 370-376.
Buccino, G., L. Riggio, G. Melli, F. Binkofski, V. Gallese and G. Rizzolatti (2005). “Listening to action-related sentences modulates the activity of the motor system: a combined TMS and behavioral study.” Brain Research Cognitive Brain Research24(3): 355-363.
Clark, A. (2015). Surfing uncertainty: Prediction, action, and the embodied mind, Oxford University Press.
Coolidge, F. L. and T. Wynn (2009). The Rise of Homo Sapiens: The Evolution of Modern Thinking, Wiley-Blackwell.
Decety, J. (1996). “The neurophysiological basis of motor imagery.” Behavioral Brain Research77(1-2): 45-52.
Dijkstra, K. and R. A. Zwaan (2014). Memory and Action. The Routledge Handbook of Embodied Cognition. L. Shapiro. New York, NY, Routledge.
Engel, A. K., K. J. Friston and D. Kragic (2016). “The pragmatic turn: Toward action-oriented views in cognitive science.”
Guillot, A., C. Tolleron and C. Collet (2010). “Does motor imagery enhance stretching and flexibility?” Journal of Sports Sciences28(3): 291-298.
Hohwy, J. (2013). The predictive mind, Oxford University Press.
Reiser, M., D. Busch and J. Munzert (2011). “Strength gains by motor imagery with different ratios of physical to mental practice.” Front Psychol2: 194.
Revonsuo, A. (2000). “The reinterpretation of dreams: An evolutionary hypothesis of the function of dreaming.” Behavioral and Brain Sciences23: 793-1121.
Rozand, V., F. Lebon, C. Papaxanthis and R. Lepers (2014). “Does a Mental-Training Session Induce Neuromuscular Fatigue?” Medicine and Science in Sports and Exercise.
Taylor, M. K., D. Gould and C. Rolo (2008). “Performance strategies of US Olympians in practice and competition.” High Ability Studies19(1): 19-36.
Tettamanti, M., G. Buccino, M. C. Saccuman, V. Gallese, M. Danna, P. Scifo, F. Fazio, G. Rizzolatti, S. F. Cappa and D. Perani (2005). “Listening to action-related sentences activates fronto-parietal motor circuits.” Journal of Cognitive Neuroscience17(2): 273-281.