Motor Skill Learning Concept: Literature Review


Motor skill acquisition is a complex process that has been researched for decades. Diverse aspects of this type of learning have been researched in detail including the exact mechanisms utilized to perform tasks, processes that take place in the brain, memory, data processing, to name a few. The research on these processes and concepts started as far back as in the nineteenth century, but there are still various gaps (Benedict, 2016). It has been acknowledged that memory and anticipation are two important components leading to the improvements in people’s performance as individuals choose the most appropriate patterns to perform a task (Afonso, Garganta, & Mesquita, 2012). Motor skill learning is also closely related to the concept of action control that consists of two phases (Elsner & Hommel, 2001). First, the action is conducted based on a set of external factors and internal capabilities. During the second stage, the obtained knowledge is utilized to produce the required activities. This literature review highlights some theoretical frameworks related to the concepts in question, as well as the most recent findings associated with information processing and motor time.

Closed-Loop Theory

According to motor learning theories, attentional, as well as cognitive, demands of the execution of a task decrease if practice increases (Ille, Selin, Do, & Thon, 2013). A closed-loop theory developed by Jack Adams in the 1970s was one of the first motor learning theories that offered testable hypotheses that gave considerable impetus to the research related to the acquisition of skills (Schmidt & Lee, 2011). This theoretical framework is associated with perceptual and cognitive constructs that initiate and tune movement separately and operate in a closed-loop system (Benedict, 2016). Perception is involved in the assessment of errors in motor learning tasks and is the first construct. Feedback is seen as the key element in the process of a person’s movement modification.

Based on this theory, such neural components as perceptual and memory traces are central to the process of motor learning. The former is seen as the reference instrument employed to assess errors when learning skills (Benedict, 2016). During every trial, the perceptual trace is a record of a movement performed several times. The learner utilizes knowledge of results to improve performance during every trial, and each record strengthens the existing memory regarding the expected response (Schmidt & Lee, 2011). If necessary, certain adjustments to movement are made based on the gained knowledge of results, so perceptual trace also serves as a correctness reference. When the necessary number of trials is implemented, the perceptual trace is created. Combined with knowledge or results and feedback, the perceptual trial leads to the movement changes, so learning occurs.

The other construct involved in the process is the memory trace that identifies and initiates the most appropriate response. The memory trace does not depend on the perceptual trace and feedback (Benedict, 2016). Therefore, it is possible to note that the closed-loop theory is deeply rooted in the assumptions that the process of detecting and correcting errors is central to the learning process. Benedict (2016) stated that this theoretical paradigm was highly applicable with simple graded movements, but was less effective with other types of moves.

Schema Theory

The schema theory was one of the theoretical frameworks that addressed the limitations of the closed-loop theory. Approximately five years after the development of Adams’s theory, Schmidt introduced his theoretical model as a response to the closed-loop theory (Schmidt & Lee, 2011). The major criticism was related to the primary role of feedback and the lack of attention to response variability (Benedict, 2016). Schmidt argued that action sequences could be controlled centrally rather than facilitated by feedback. The researcher also stressed that the response could adapt flexibly to new circumstances (Benedict, 2016). Schmidt’s assumption was that action effects were predicted, so the comparison of planned action with expected outcomes enabled this internal test to monitor movement execution (Harrison & Ziessler, 2016). According to schema theory, the anticipation of responses is also instrumental in detecting and correcting errors (Ziessler, Nattkemper, & Vogt, 2012). It is noteworthy that the two theories mentioned above bore some similarities.

For instance, Schmidt introduced the concepts of recognition schema and recall schema that were similar to Adams’s perceptual and memory traces (Benedict, 2016). According to the schema theory, the recall schema selects response movements based on previous trials to improve the existing pattern. This concept is similar to Adams’s memory traces that implied the focus on memory capability. The recognition schema assesses the correctness of the chosen movement and creates sensory consequences adding the new outcome to the current recognition mechanism. It is necessary to note that these theoretical perspectives are instrumental in explaining the processes that take place during data processing and response. They address different facets of the matter, which enables researchers to view the processes from different angles.

Effector Independence and Generalized Motor Program

As mentioned above, the schema theory assumes that movements can be structured centrally and are not tied to feedback. The motor program concept is closely related to this assumption. The motor program can be defined as a “sequence of stored commands” established and selected before the implementation of an action that ensures the completion of the necessary sequence (Benedict, 2016, p. 17). Researchers described the exact areas of the brain where this process could take place (Anson, 1989). Schmidt and Lee (2011) noted that each movement needed a program, which led to serious issues related to memory and novelty.

It seemed logical that longer and more complex sequences required more storage space, so it was unclear how this bulk of information can be stored (Anson, 1982). For instance, researchers estimated the required memory capacity for different processes. It was reported that comparatively easy operations such as linguistic required 10,000 programs, which would need considerable brain capacity. At the same time, the novelty problem was also quite relevant because researchers could not explain the human ability to perform new tasks that were not associated with survival (Benedict, 2016). The explanation was provided in the 1970s although the experiments justifying such assumptions had been implemented as far back as the 1940s.

Schmidt assumed that generalized motor programs existed, which provided a solution to the novelty and storage problems (Benedict, 2016). One of the first experiments that provided the background for such hypotheses was the one conducted by Lashley in 1942 (Benedict, 2016). The participants wrote certain extracts using both hands and mouth. It was found that the major features of handwriting (such as the length of lines) were preserved irrespective of the utilized part of the body. Therefore, it was suggested that sequences were effector independent and the movement was stored in memory rather than muscle. Effector independence refers to an individual’s capacity to implement sequences using different muscle groups (Benedict, 2016). The introduction of the concept of generalized motor programs facilitated further exploration of motor skill acquisition and other areas.

The existence of generalized motor programs leads to the decreased use of memory capacity and enables people to perform novel tasks that may seem unnecessary for functioning or survival. Generalized motor programs are also linked to decreased motor time and people’s ability to learn faster and more effectively. They learn new sequences based on the existing knowledge and external factors. The understanding of the mechanisms related to generalized motor programs is critical for motor skill learning research.

Hierarchical Organization of the Brain

The complex structure of the brain ensures the simultaneous completion of diverse tasks as different areas of the brain control specific functions. Movement control is closely linked to the hierarchical structure of the brain as people’s actions may be controlled at different levels. For instance, sensory information and motor data are controlled at the highest levels of the structure and are defined as association areas that provide “a way to associate sensory feedback to motor output” (Benedict, 2016, p. 21). For instance, the visual data received by the retina are sent to the parvocellular layers in the thalamus and further transmitted to the occipital lobe, primary visual cortex, in particular.

Eventually, the data is sent to the frontal lobe where the necessary functions are selected. In this area, the information received from the retina is used to develop a program, which is linked to premotor and supplementary motor areas. The developed program is transferred to the primary motor cortex, and the corresponding movements are completed (Anson, 1989). The implementation of the action is also related to the analysis of external factors that takes place at higher layers of the brain. It has been found that although similar areas of the brain are involved in specific processes, humans have a different brain composition, which has an influence on their learning capacity.

Electromechanical Delay and Motor Time

Apart from brain structure and central processing time, peripheral mechanisms also attracted considerable attention as these aspects were instrumental in explaining reaction time differences. The central processing time is associated with data processing delays, encoding, and other events. The peripheral factors include such events as the initiation of the contractions of muscles, as well as anatomical units such as the forearm or finger (Anson, 1982). Electromechanical delay is referred to as the “electromechanical and biochemical occurrences, in concert with the muscles’ morphological properties, which are responsible for the delay in muscular tension development” (Benedict, 2016, p. 24). This notion involves mechanical aspects and electrochemical processes. An illustration of the delay is the fact that a muscle with a larger mass requires a more significant net force to start an action of the corresponding body part.

Information processing, the identification of the most appropriate program, and the implementation of the movement require time. Researchers started paying increased attention to motor time in the 1960s, but the investigation of this notion is still ongoing. It has been acknowledged that response time depends on the complexity of the required action (Christina & Rose, 1985; Kendall, 2018). Premotor time is the time span between the start of the response stimuli and the start of the electromyographic activity (Davranche, Burle, Audiffren, & Hasbroucq, 2005). Motor time can be defined as the interval between the start of the electromyographic activity and the necessary motor response (Davranche et al., 2005). Benedict (2016) noted that motor time could depend on central processing peculiarities. Yang, Bender, and Raz (2015) note that age differences in brain composition have a certain impact on people’s response time and accuracy.

In this respect, the electromechanical delay is seen as the time interval between the start of the stimuli and the creation of the tension in the corresponding muscles. This process included four major elements responsible for the conduction of the movement (Benedict, 2016). The first component is the implementation of the “motor unit action potential along with the T-tubule system” (Benedict, 2016, p. 24). The following elements are calcium release and the creation of tension in the contractile component. Finally, the stretching of the series elastic element takes place. Benedict (2016) noted that researchers attempted to measure the electromechanical delay. According to these inquiries, the electromechanical delay could fluctuate between 25 and 85 milliseconds. Benedict (2016) added that only male participants took part in the research, which is a considerable limitation to be addressed in further studies.

External factors also have an impact on motor time, which was analyzed in several studies. For example, Theeuwes, Liefooghe, and De Houwer (2014) explored the effects of task-irrelevant aspects, such as stimuli position on response time. The participants made spatial responses to non-spatial stimuli. The researchers found that people performed better if spatial clues matched the response position. Anticipation and memory played an important role in the process, which needs to be further considered.

Many researchers investigated the possibility to reduce motor time through different practices, including motor skill learning. These inquiries led to the development of several laws such as Hick’s law (Logan, Ulrich, & Lindsey, 2016). According to this law, response time increases when stimuli reaction pairings increase. Logan et al. (2016) conducted a study that involved the analysis of typists’ skills. The researchers found that typists’ automaticity depended on their attention and response control type. These findings are relevant to the investigations associated with learning in diverse settings.

Conclusion and Further Research

The review of the literature on the matter shows that diverse aspects of motor skill learning have been explored. Researchers have identified and examined the links between memory, information processing, attention, response time, and skill acquisition. Major mechanisms and involved instruments were analyzed in detail and various insights have been provided. Some of the primary findings include the correlation between movement sequence complexity and motor time, as well as the existence of generalized motor programs that explains people’s memory capacity and new skill acquisition.

Nevertheless, numerous gaps in research are still apparent and yet to be addressed. Some of the major blanks to be filled are associated with diversity as different cohorts have physiological and anatomic peculiarities that have an impact on their learning abilities. It is important to investigate the peculiarities of motor time in different groups such as females, males, children, older people, as well as individuals with different backgrounds. Researchers often focus on the role biochemical or external factors play in the process, but it is also important to gain more insights into the correlation between these concepts.


Afonso, J., Garganta, J., & Mesquita, I. (2012). Decision-making in sports: The role of attention, anticipation and memory. Revista Brasileira de Cineantropometria & Desempenho Humano, 14(5), 592-601.

Anson, J. G. (1982). Memory drum theory: Alternative tests and explanations for the complexity effects on simple reaction time. Journal of Motor Behavior, 14(3), 228-246.

Anson, J. G. (1989). Effects of the moment of inertia on simple reaction time. Journal of Motor Behavior, 21(1), 60-71.

Benedict, I. R. J. (2016). Effects of foreperiod regularity and muscle size on fractionated reaction time. Web.

Christina, R. W., & Rose, D. J. (1985). Premotor and motor reaction time as a function of response complexity. Research Quarterly for Exercise and Sport, 56(4), 306-315.

Davranche, K., Burle, B., Audiffren, M., & Hasbroucq, T. (2005). Information processing during physical exercise: A chronometric and electromyographic study. Experimental Brain Research, 165(4), 532-540.

Elsner, B., & Hommel, B. (2001). Effect anticipation and action control. Journal of Experimental Psychology: Human Perception and Performance, 27(1), 229-240.

Harrison, N. R., & Ziessler, M. (2016). Effect anticipation affects perceptual, cognitive, and motor phases of response preparation: Evidence from an event-related potential (ERP) study. Frontiers in Human Neuroscience, 10, 5. doi:10.3389/fnhum.2016.00005

Ille, A., Selin, I., Do, M. C., & Thon, B. (2013). Attentional focus effects on sprint start performance as a function of skill level. Journal of Sports Sciences, 31(15), 1705-1712.

Kendall, B. J. (2018). The effects of acute exercise on postural control, information processing, motor skill acquisition, and executive function (Unpublished doctoral dissertation). Wayne State University, Detroit, Michigan.

Logan, G. D., Ulrich, J. E., & Lindsey, D. R. (2016). Different (key) strokes for different folks: How standard and nonstandard typists balance Fitts’ law and Hick’s law. Journal of Experimental Psychology: Human Perception and Performance, 42(12), 2084-2102.

Schmidt, R. A., & Lee, T. D. (2011). Motor control and learning: A behavioral emphasis (5th ed.). Champaign, IL: Human Kinetics.

Theeuwes, M., Liefooghe, B., & De Houwer, J. (2014). Eliminating the Simon effect by instruction. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40(5), 1470-1480.

Yang, Y., Bender, A. R., & Raz, N. (2015). Age related differences in reaction time components and diffusion properties of normal-appearing white matter in healthy adults. Neuropsychologia, 66, 246-258.

Ziessler M., Nattkemper D., Vogt S. (2012). The activation of effect codes in response preparation: New evidence from an indirect priming paradigm. Frontiers in Psychology, 3, 585. doi:10.3389/fpsyg.2012.00585