Motor Learning: Reflections on the Past 40 Years of Research

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The authors reflect on the dire state of motor learning at the time of Brooks’s book and consider reasons why research was resurrected in the 1980s and flourished in the ensuing years. In so doing, they provide an overview of the various research topics that have been studied, discuss the influence of motor learning on other fields of study, and consider the future of motor learning research both within and outside the academic study of kinesiology.

At the time of publication of Brooks’s (1981) book on the academic discipline of kinesiology, the field of motor learning had died or at least been given last rites. Many investigators who had studied how motor skills are improved with practice had since turned their attention to movement control, the process by which movement is regulated. Graduate students and other young investigators were following their lead. One chapter in Brooks’s book delivered the motor learning eulogy quite succinctly:

There has been an accelerating trend away from research on skill acquisition and learning variables toward research on motor control processes. Because learning cannot be adequately characterized without an understanding of the structural and functional means of control, it appears that addressing processes rather than products is a more basic task for research. (Stelmach et al., 1981, pp. 273–274)

Forty years later, the situation has changed dramatically and motor learning has reemerged stronger than ever, albeit in a different guise. The intent of this paper is to answer five questions: (a) How was motor learning research resurrected? (b) What key research areas have emerged? (c) How has motor learning research influenced other research areas? (d) How has motor learning research addressed issues of economic and societal concern? and (e) What does the future hold?

How Was Motor Learning Research Resurrected?

The publication of Brooks’s book in 1981 roughly coincided with what we believe were three influential research areas that emerged in the 1980s: contextual interference, augmented feedback, and constraints-led practice. In turn, these areas led either directly or indirectly to the plethora of motor learning research that we are seeing today. The most important contribution arose from the publication of one paper by Shea and Morgan (1979) on contextual interference, which not only resulted in an explosion of follow-up studies within kinesiology, but in many other research areas, such as psychology, neuroscience, and various applied disciplines.

Contextual Interference

The goal of Shea and Morgan’s study was simple: to compare the effectiveness of two practice schedules. Participants in both groups practiced three, spatially distinct arm movement patterns with the goal of reacting and moving as fast as possible to a signal to respond. All aspects of the experiment (patterns, numbers of trials per pattern, and so on) were identical for all participants except for one key variable—the order by which the patterns were practiced. One group practiced the patterns in a “blocked” order in which all trials on one pattern were performed consecutively before trials on another pattern were practiced. The other group practiced the patterns in a “random” order in which no more than two trials on any pattern were performed consecutively. Not surprisingly, practicing the same pattern repeatedly without the potential interference incurred from performing the other patterns resulted in faster and larger reductions in reaction and movement time than occurred when continuously switching patterns. That is, blocked practice showed better immediate improvements than random practice.

In hindsight, had the experiment been completed at that point perhaps no one would have been surprised by the result and indeed, its publication would not have had much impact. But Shea and Morgan took the experiment one important step further—they conducted several tests of retention and transfer. In most of these tests, the participants who earlier had practiced in a random order performed better (faster and with fewer errors) than those who had practiced in a blocked order. Indeed, this was a rather shocking result because it ran counter to dominant thinking at the time—interference during practice was considered to disrupt, not to facilitate learning. The findings raised very important questions for further research, including (but not limited to) the following: (a) What do immediate performance changes, compared with those revealed in retention and transfer, suggest about the process of learning? (b) Why does random practice enhance learning and/or why does blocked practice degrade learning? (c) Do the findings extend beyond the participants and task used by Shea and Morgan? and (d) Do these findings have importance for learning real-world motor skills?

These questions began a research inquiry into what became known as the “contextual interference” effect and were addressed almost immediately by researchers at the University of Georgia, Louisiana State University, and  John Shea and his students at both the University of Colorado and the Pennsylvania State University. For example, questions about extending the effect to other tasks and sample participants were addressed by Del Rey et al. (1982) and Del Rey et al. (1983). Experiments designed to address how specific practice orders influenced learning were conducted by Lee and Magill (1983), Lee et al. (1985), and Shea and Zimny (1983). These studies represented initial attempts to explore methodological limitations about how contextual interference practice schedules influenced performance and learning.

Goode and Magill (1986) designed an experiment to explore the application of contextual interference to the learning of a sport skill. Their findings—that a nonrepeating (serial) order of practicing three badminton serves resulted in better retention (from the same serve location) and transfer (to the opposite serve location) than blocked practice—provided inspiration to other researchers that the contextual interference effect could impact instruction and training on a much broader scale. We will return to this issue later.

The Shea and Morgan results also had an important influence on how researchers theorized the motor learning process. At the time, motor learning was dominated by two theories published in the 1970s (Adams, 1971; Schmidt, 1975). Both theories presented learning as the direct result of movement—the accumulation and variety of movement experiences playing critical roles. But the differences between blocked and random practice emerged despite both groups having practiced the identical number of movement experiences and variations. Thus, the Shea and Morgan findings opened a new avenue for theory, which drew largely on cognitive mechanisms. Shea and Morgan (1979) and Shea and Zimny (1983) offered the view that a random practice order promoted a greater contrast between the patterns to be learned, leading to more elaborate and distinctive memory representations compared with a blocked practice order. A different view was presented by Lee and Magill (1983, 1985), who suggested that switching between tasks in a random order resulted in short-term forgetting of previous task solutions but enhanced long-term retention when the task problem was resolved again and again over the practice session. Both views received support over the 40 years of research to follow (Wright & Lin, 2020). As we will see, cognitive-based ideas would play a role in a larger reemergence of motor learning research.

Augmented Feedback

The Shea and Morgan (1979) findings became even more impactful when considered in tandem with another motor learning issue that emerged at roughly the same time. Research on the roles of augmented feedback (sometimes called KR, or knowledge of results) in motor learning had been a hot topic decades earlier because of the strong influence of behaviorism in psychology and the role of reinforcement in behaviorist theories. Motor learning researchers at that time (many of them trained as psychologists) studied topics such as the effects of frequency, delay, scheduling, and presentation modes of augmented feedback on motor learning. Upon review of those findings, Schmidt (1982) and Salmoni et al. (1984) reported a startling anomaly in the evidence. The effect of a feedback variable at the time it was manipulated, was often reversed when examined in a no feedback retention test. Methods that often enhanced short-term performance changes were poor for retention, and vice versa. A study by Annett (1959) provides a good example.

By the time Annett’s (1959) study was published, research had established that providing augmented feedback frequently and/or precisely led learners to quickly establish proficiency in a motor skill compared with less frequent and/or less precise feedback (Ammons, 1956). However, over a series of experiments, Annett found that these findings were reversed in tests of retention when augmented feedback was removed. In one experiment, retention was enhanced when practice trials that were followed by augmented feedback were separated by trial(s) that were not followed by augmented feedback (i.e., reducing the relative frequency of feedback). In another experiment, Annett found that retention was enhanced when the feedback was provided only when outside of a tolerance level of precision (i.e., a feedback “bandwidth”). In both experiments, Annett found that the critical feedback variable had reversed effects in retention compared with the effects of that variable during practice.

The findings reviewed by Salmoni et al. (1984) regarding the above feedback variables, plus other anomalies in the literature (e.g., Lavery, 1962; Lavery & Suddon, 1962), had two very important consequences. First, their findings led to a resurgence of research on the roles of augmented feedback in motor learning that continues today (e.g., see Anderson et al., 2020, for a review). Second, Salmoni et al. reiterated the important distinction between learning and performance, one that had been demonstrated so dramatically by Shea and Morgan (1979). This distinction emphasizes that what occurs during a practice or training regime should not be considered as prima facie evidence for learning. On one hand, evidence of rapid improvements in performance can be reversed in tests of retention and transfer, leading to the mistaken impression that a practice condition had been successful for learning (as in blocked practice or frequent feedback). Conversely, the absence or slowing of progress can sometimes mask an underlying improvement that transfers well or will last beyond a layoff of no practice (as in random practice or infrequent feedback). These findings were very important because they disrupted a comfort level among motor learning researchers, renewed interest in the study of practice variables, provided significant association with related areas of research, and stoked conjecture about how these mechanisms might evoke better, more efficient training in real-world applications. We will return to several of these themes later.

Constraints-Led Practice

Along with the renewed interest in learning that had been generated by the contextual interference and augmented feedback findings, another influential research theme emerged that considered the learner as one element of an interactive system of constraints that impacts learning (Higgins, 1977). This idea of performance being impacted by constraints was influenced greatly by the views of Bernstein (1967). Motor learning, according to Bernstein, was a process by which goal attainment is achieved in increasingly more diversified ways by the central nervous system. Because the environment is never the same on any two occasions, Bernstein regarded practice as a problem-solving process that involved “repetition without repetition”—that is, achieving a desired goal on repeated occasions without repeating the identical movement processes each time.

The premise for Higgins’s (1977) view of learning is that human movement is a dynamic process that evolves over time, influenced strongly by the constraints of the environment and task, but is unique to the particular individual. Learning to use a computer mouse, for example, is an interaction of constraints imposed by the workspace (an environmental constraint), the sensitivity of the mouse (a task constraint), and the size of the user’s hand (an organismic constraint). Each attempt at using the mouse never repeats the same set of movement processes, even though the output goal is often the same. Learning, therefore, is not simply a matter that concerns only the individual, but rather how the individual discovers and exploits the available environmental and task constraints to achieve the intended action goal.

An important theme in this type of research is to establish how learning is impacted through the exploration and exploitation of the available constraints. For example, a series of studies by Singer showed that learning was enhanced when an environmental workspace was established that encouraged the active discovery of tool use (e.g., Singer & Pease, 1976). Programmed instruction-based views of learning were found to be better than discovery constraints for initial acquisition rates but poorer than discovery methods when assessed in retention, reminiscent of the contextual interference and feedback literature discussed in previous sections.

The constraints-led view also served well as a framework for considering the process of motor development (Newell, 1986). For example, Leavitt (1979) observed that children of differing ages and skating skill resulted in varied levels of puckhandling precision. In order to level the skating rink for weaker skaters and younger children, Leavitt increased the puck size, making it easier to manipulate (Fitts, 1954). In these ways, Leavitt was using a task constraint to promote the learning of stick-handling skills across the various ages and skills of young hockey players. Changing the dimensions of the task as appropriate to suit the needs of children who differ in size and skill fits the framework well.

The constraints-led practice framework also shares some fundamental basis with the dynamic systems framework which views motor behavior as an emergent feature of interacting physical systems over time. In some instances, the emergent behavior occurs over the short-term, displaying systematic properties that share commonalities with the dynamics of physical systems (Kelso, 1984). With practice, these changes emerge over the long term as permanent changes due to learning (Zanone & Kelso, 1992).

Key Research Areas

By the early 1990s, the resurgence in motor learning research was well underway and about to explode in many directions on levels of inquiry that included basic theory and direct application as primary goals (Christina, 1989). Many of these areas of research grew directly or indirectly from the findings of the three research areas discussed in the previous section. Our goal here is not to present an exhaustive review of this literature, but rather to highlight certain directions taken in the research. References cited were selected for the purpose of directing the interested reader to a source that could be used as a suitable springboard to dive into the extant research on the topic. In this section, we restrict our interest to the growth of motor learning research largely within the academic discipline of kinesiology. In the following two sections, we expand the discussion to include how the research impacted other disciplines in both theory and application.

Contextual Interference Research

Like any topic, the more questions that are addressed by research, the more complex the answers become; contextual interference was no exception. Although random (now referred to often as “interleaved”) practice was found repeatedly in studies to benefit learning, the magnitude of that benefit depended on several variables, such as the skill level of the individual, demands and nature of the task, amount of practice, and perhaps a few others. These and other research issues related to contextual interference are reviewed by Wright and Lin (2020).

Practice that is entirely blocked (now often referred to as “repetitive”) and practice that is entirely interleaved represent extreme forms of contextual interference. One of the conundrums facing researchers is the discord between perceived versus actual effects of these extreme schedules. Individuals who engage in repetitive practice typically believe that they are learning more and that they will retain performance better than those who engage in interleaved practice (Simon & Bjork, 2001). This metacognitive issue raises a practical concern for applying contextual interference in training environments because of the potential negative influence on motivation to continue practice if an individual believes it to be ineffective. Therefore, some have searched for hybrid practice schedules that combine repetitive and interleaved orders that could produce the learning benefits of interleaving without the degrading effects on immediate performance (such as interleaving repetitive blocks of trials or providing repetitive trials early in practice and interleaved trials later). Another approach, which used algorithms to determine when to repeat or interleave tasks, was later devised as an alternative to prescribing hybrid schedules a priori. In such a way, so-called “win-switch, lose-stay” algorithms could be used to customize the amount of contextual interference to suit the specific needs of the learner (e.g., Wadden et al., 2019). A framework for conceptualizing hybrid accounts of contextual interference that supported performance yet created challenges for learning was suggested by Guadagnoli and Lee (2004), and recently summarized by Yan et al. (2020).

Augmented Feedback Research

Throughout the history of motor learning research, the role of augmented feedback has been hailed as one of the most important influences on learning (Adams, 1987). A cautionary note emerging from the review of Salmoni et al. (1984) was that, under some conditions, augmented feedback may become too important or dominant when it guides the learner toward an effective task performance during training (Schmidt, 1991). This negative impact is observed when the learner is called upon later to perform in the absence of augmented feedback. The issue is an important one for both theory and application. For theory, a key question concerns the role of augmented feedback in influencing how a person learns to use one’s own sources of feedback (termed inherent or intrinsic feedback). A problem arises when the need to attend to inherent sources of feedback is overshadowed by augmented feedback—the learner is disadvantaged when later compelled to rely on inherent feedback after augmented feedback is withdrawn. This is particularly important in terms of application, because moving from training with augmented feedback to performance in its absence represents the typical scenario in sport and industrial training—learners are provided with augmented feedback during training but expected to support performance on the job or during the game using inherent sources of feedback.

The research jump started by the Salmoni et al. review continued throughout the 1990s and 2000s, with studies on myriad variables undertaken to evaluate questions about when to provide augmented feedback. Concomitant with the increased availability of easy-to-use recording capabilities, research on issues such as how feedback was delivered assumed renewed importance as well. The capability to edit visual images and recordings, such as contrasting a novice’s performance with that of an expert’s, provided new tools by which feedback capabilities could be examined. Auditory feedback, often assuming a secondary role in efficacy to visual feedback, became a source of inquiry as sonification techniques were used increasingly in research. The review by Anderson et al. (2020) is recommended for the interested reader.

The role of augmented feedback as a motivator to enhance learning also became an important topic in two types of paradigms. False positive feedback, such as reporting back to learners that their performance had been better than most of their peers, was found to give a boost to learning regardless of their actual performance. Augmented feedback positively influenced learning when given after training trials that were performed relatively well, compared with those trials performed relatively poorly. These two sets of findings steered the role of augmented feedback in a new direction, giving rise to a theoretical position in which the learner’s motivational state had a much more impactful role than in previous motor learning theories (Wulf & Lewthwaite, 2016).

Lastly, the role of physical guidance as a source of augmented feedback deserves special mention. Long used (and oftentimes overused) in sport and training, physical guidance has been a research topic in motor learning for many years. Similar to the augmented feedback findings reported in Salmoni et al. (1984), small amounts of guidance support performance and learning, especially when provided early in training. However, if performance becomes dependent upon physical guidance, then the learner suffers when it is no longer available (Hodges & Campagnaro, 2012). In theory, the guidance supplied by a device that physically restricts movement attracts attention away from inherent sources of feedback, such as haptic feedback, which will remain available once the guidance is removed. An exciting, new research area has consequently emerged in recent years that uses physical devices to amplify errors, instead of restricting errors (which is the traditional role of most guidance devices). Here, the device amplifies the inherent feedback signals that accompany errors, making them more salient to the performer and presumably, better learned. The increased salience and better understanding of inherent feedback should then be available to correct movement errors that are made in the future. Studies of error amplification have shown impressive results when applied in laboratory and applied contexts (reviewed in Heuer & Lüttgen, 2016).

Constraints-Led Practice Research

The constraints-led practice research conducted over the past 20 years or more and influenced by the ideas in Higgins (1977) and Newell (1986), has been nicely summarized in two impressive volumes (most recently in Button et al., 2021). Some of these studies have involved case study analyses (Renshaw et al., 2020). Other studies reveal that discovery methods of learning achieved through the adjustment of environmental goal constraints have a significant advantage over more restrictive types of instructional techniques. For example, Gray (2018) found that baseball practice, during which batters simply attempted to hit the ball over a physical barrier located in the playing field, resulted in much better learning of desirable launch angles than did two other types of instructional strategies that were designed to increase launch angles. These types of experimental approaches hold considerable promise for the future of the constraints-led practice approach.

One criticism of motor learning research over the years was the failure to study “real” motor skills over prolonged periods of training. Learning was often studied using contrived tasks, which (arguably) reduced all the experimental participants to novices at the start of the training trials. Dynamic systems research represented a unique alternative to this strategy. In this framework, the acquisition of new coordination patterns was studied amidst the observation of natural coordination tendencies. For example, cyclical upper limb motions reveal two fundamentally stable timing patterns—moving in synchrony (in 0° relative phase) and in opposition (in 180° relative phase)—patterns that can be performed accurately and consistently with little to no practice. Out-of-phase coordination patterns (e.g., 45°, 90°, 135° relative phase) can be acquired with considerable practice (and augmented feedback). But, in so doing, how do the existing, fundamental patterns (0° and 180°) influence the acquisition of new patterns, and in turn, how does new learning impact the existing patterns? This research has uncovered a rather complex set of results that depend largely on the constraints present in the learning environment. Excellent reviews by Shea et al. (2016) and Swinnen and Wenderoth (2004) present important insights into the complex interaction of environmental, task, and individual constraints that influence the learning process.

Emerging Research Topics

A good sign for a field of study is the emergence of new research that does not have direct ties to previous work. We believe this is true in some areas of motor learning that have received a significant amount of research devotion in the past 40 years.

Focus of attention is the most often studied of these new topics. A common anecdotal view among high-level performing musicians and athletes is that performance would be disrupted if you began to think about how you were doing something. Wulf, her colleagues, and other researchers have tested that idea in many experimental situations and found the adage to hold true under most circumstances. When directed to think about how to move (an internal focus of attention), individuals perform and learn new actions more poorly than when directed to think about the intended effect of movement. For example, in baseball pitching, one is likely to be more successful when attention is directed at the target (e.g., a catcher’s mitt) than when thinking about some aspect of the pitching motion (e.g., the ball release point). Several hypotheses have been suggested regarding why these effects occur and represent a fertile avenue for future work and application (Wulf, 2013; Wulf & Lewthwaite, 2016).

Although modeling and observational learning are not strictly new topics of investigation, they had received only sporadic study until the 1990s when the interest of both motor learning researchers and sport psychologists merged to forge a collaborative effort to better understand how skill acquisition is influenced by observing others (e.g., McCullagh & Weiss, 2001). The result was a large body of literature on the various features of the modeling/observation environment, such as the characteristics of the model (e.g., peer vs. instructor), the dynamics of the environment (e.g., live vs. video), scheduling issues (e.g., alternating practice and observation with a partner), and so on (Karlinski et al., 2020; Ste-Marie et al., 2012).

Picking up cues from the environment through visual observation, which then can be used to influence or improve performance, is a skill that represents a vibrant field of study that was initiated by the pioneering work of Abernethy and Russell (1987). Many motor skills are performed in dynamic environments in which prediction and movement anticipation are requirements for successful performance. Experts in their sport domain have been found to excel at prediction by focusing on the most important cues that foreshadow future events (and to ignore those that do not; Baker & Farrow, 2015; Williams & Jackson, 2019). The implication is that if virtual environments can be created in which the predictive advantages that experts have gained can be learned by novices, then there would be enormous advantages for practical reasons (e.g., training can be done offsite without the need for live opponents or actual environments, thereby adding convenience and, potentially, reducing costs). The recent developments of virtual environment technology together with effective experimental design techniques have seen a recent emergence of investigation in this exciting research area (Schorer et al., 2015).

In concluding this section, we acknowledge other topics that have received considerable research attention over the past 40 years, such as deliberate practice, implicit learning, self-controlled learning, specificity of training, simulation training, and more. For more information, the interested reader is advised to consult recent, edited volumes (e.g., Baker & Farrow, 2015; Hodges & Williams, 2020), textbooks (e.g., Magill & Anderson, 2021; Schmidt et al., 2019), or podcasts (e.g., https://perceptionaction.com/).

Relation to Other Research Areas

Understandably, motor learning researchers have teamed with researchers in other kinesiology disciplines to advance mutual goals. Previously, we discussed how the constraints-led practice framework merged motor learning with motor development (Higgins, 1977; Newell, 1986) and would be remiss if we failed to acknowledge the important contributions made by Esther Thelen and how her work impacted motor learning theory (Galloway, 2005). We also previously mentioned the roles of motivation and observation, two cornerstone topics in sport and exercise psychology that have become mainstream study in motor learning (Ste-Marie et al., 2012; Wulf & Lewthwaite, 2016). The role of minimization of energy expenditure as a function of learning provides a good example of the merging of motor learning and exercise physiology (e.g., Sparrow & Newell, 1998). Of course biomechanics, long considered a cohort in understanding movement control (see Latash, this volume), also has forged a partnership in understanding changes in forces and intersegmental dynamics that accompany practice (e.g., Williams et al., 2016; Wu & Latash, 2014).

Another sign of the growth and maturity of a field of study is when it becomes recognized beyond the scientific borders that define it. Perhaps one of the most celebrated instances relative to motor learning came with the publication of the book Outliers (Gladwell, 2008). In the book, Gladwell extolled the virtues of practice in the learning of motor skills, famously (or “unfortunately,” some may say) positing a “10,000-hr rule” for the development of expertise. That idea—that expertise was simply a matter of accumulated practice time—was debunked in favor of the original theoretical formulations concerning deliberate practice (e.g., Ericsson, 2020, for review). However, there is no denying that the immense popularity of the book brought thinking about motor skill learning into public focus.

Cognitive psychology has acknowledged the lessons learned by motor learning researchers and sometimes followed their lead. For example, the performance versus learning distinction has played a key role in modern views about the effects of study methods on memory (Soderstrom & Bjork, 2015). Moreover, some of those modern views have developed from research that borrowed methods first appearing in motor learning experiments. The roles of interleaving versus repeating items during study have become a frequent topic of investigation in cognitive psychology. Those efforts have now become popular in memory theory and application to educational practice (e.g., Bjork & Bjork, 2019; Brown et al., 2014).

Motor learning has also become a popular field of study in the neurosciences. In fact, some of the issues and methods of study previously investigated by motor learning researchers have found renewed interest among neuroscientists. For example, memory consolidation, once a popular topic to investigators of practice distribution, has become a bedrock paradigm for those interested in how memories change with sleep (Kantak & Winstein, 2012). Contextual interference has also been a frequent topic, with the beneficial behavioral effects of interleaved practice now linked to specific neural brain activity (Lage et al., 2015; Lin et al., 2018).

Influences on Issues of Economic and Societal Concern

The relationship between basic and applied science is complementary: good basic and theoretical foundations are required for meaningful applied research. Similarly, well-designed applied research allow for the limits of models based only on laboratory observation and experimentation to be tested. Both are equally important in helping us understand a phenomenon as complex as human motor learning.

Sport was the dominant application of motor learning research for many years (Cratty, 1964; Knapp, 1963). Although ergonomics was concerned primarily with human performance, issues concerned with learning were also discussed (Fitts & Posner, 1967) and examined empirically (Baddeley & Longman, 1978). More recently, the role of motor learning in physical rehabilitation has become a mainstay in the application of theory to practice (Gentile, 2000; Marteniuk, 1979; Winstein, 1987; Wishart et al., 2000). Discussion about application of motor learning theory now includes many other fields of study, such as music, medicine, dentistry, and industrial skills training. But the application of motor learning findings has obvious limitations and is open to criticism.

Wulf and Shea (2002) reviewed papers that examined the learning of complex motor skills. The types of complex skills they examined included laboratory tasks or simple sport skills. Based on their review, Wulf and Shea concluded that very simple tasks that have low processing demands will benefit from practice conditions that increase processing requirements. However, this premise does not fit with the learning of complex motor skills and highlights the need to conduct more research with skills that have real-world complexity to gain better insight into the processes involved in motor learning. There has been a growth in motor learning research in real-world settings (i.e., health care, industrial), but the drivers of this have nothing to do with advancing the science of motor learning, but instead has been driven by factors such as economics, safety, and accountability.

Using very complex skills in a motor learning experiment may have an appeal because of the ecological validity; however, one of the reasons they had not been used frequently are the challenges that exist in measurement. For both the purposes of providing feedback and measuring performance outcomes, objective assessment of a technical skill is important for skill learning. In medical education, for example, techniques have been developed for the reliable and valid assessments of technical skills. One such effort was formalized by the development of a clinical exam where trainees perform a simulated medical procedure at various workstations (Martin et al., 1997). Performance is evaluated using gross checklists (either right or wrong), and a global rating score consisting of high-level assessments evaluated on a 5-point scale. Related to this was the development of the surgical checklist. This checklist was modeled after the safety checklists used in the aviation industry and was implemented to improve the safety of procedures or processes by bringing together teams in the workplace to perform key safety checks. These validated checklists and global rating scales are now also being used as research tools to measure or quantify complex tasks when learning is studied in work settings (e.g., Sanli et al., 2018). For example, Walsh et al. (2015) developed a checklist and global rating scale for the measurement of colonoscopy. This tool can quantify skill performance and is used both in research and for assessing clinician competence. A common addition to the checklist and global rating is the measurement of the speed at which a skill is performed. However, it is obvious to the motor learning researcher that these measurement instruments are not very sensitive to small improvements that might occur with practice.

Accountability has been an important factor in the adoption and mandated use of simulation-based training in many settings. Simulation-based learning is a technique, not a technology (Lateef, 2010) and it involves creating a learning environment that resembles the real scenario. It is commonly accepted that simulation training will lead to better performance and outcomes and is mandated for many industries such as aviation and medicine. For example, surgical skill centers must have simulation-based training that includes a research program to receive the highest level of accreditation. This has led to a large increase in research addressing the learning of technical skills in surgery. For example, a search in PubMed for the terms “surgery + learning” leads to 5,945 hits in 2020 compared with only 112 in 1980.

Many studies involve replications of paradigms that were used in the basic motor learning literature. For example, Moulton et al. (2006) had groups of surgical residents learn to perform a microvascular anastomosis (suturing very small vessels) either in a massed (four sessions in one day) or distributed (one session per week over 4 weeks) schedule with performance being assessed pretraining, immediate posttraining, or 1-month posttraining. Both groups showed immediate improvement in performance, but the distributed group performed better on the one-month retention and transfer tests using measures of time, number of hand movements, and global rating and checklists scores. The findings from this study did not change our understanding of the phenomenon of massed versus distributed practice, but it did revolutionize how continuing medical education and residency curricula are now delivered. The publication of this paper lead to a call to restructure training schedules to allow for distributed practice. This is only one example where a motor learning research paradigm has been borrowed and applied in other domains, resulting in a change in everyday practice.

Another domain where motor learning research is currently being highlighted is related to safety culture. Whenever there is a major catastrophic accident such as the sinking of a ship or an oil rig, or the crash of an aircraft that results in a loss of life and infrastructure, there is always a formal inquiry to see what can be learned from the incident to ensure it is not repeated. The issue of whether there was sufficient and appropriate training is always evaluated and is often cited as a contributing factor to the incident. A frequent response to these types of major incidents is a flow of funds into training centers with a focus on delivering evidence-based training. These training centers provide a unique opportunity to study questions that are difficult to study in the laboratory environment. For example, Sanli et al. (2019) tested over 500 people who visited a training center for required marine safety training. This training was mandated and scheduled by an external governing organization, which allowed Sanli et al. to address questions of forgetting and long-term retention of skills. The sample for this study included participants who had received the same standardized training ranging from 1 year ago to 43 years ago. These types of analyses would not be possible in a laboratory setup, demonstrating how some applied research can allow us to address interesting theoretical questions such as the mechanisms of skill forgetting.

What Does the Future Hold?

At the time of this writing, we just past one full year into the COVID-19 pandemic; its negative impact on research in both the short term and the long term is emerging. Research involving human participants who attend a physical laboratory has been severely restricted, which will impact research output, new findings, career progress, and so on. Nevertheless, we are optimistic that there will be positive outcomes as well. Advances in video conferencing technology have the potential to facilitate more collaborations among researchers in different fields of study in varying parts of the world. Innovations in conducting online data collection hold promise for larger and more diverse populations from which to draw participants.

The emergence of COVID-19 has transformed industrial training very quickly. The content and delivery of courses are typically mandated by regulatory agencies to take place with a specified number of classroom hours and a specified amount of hands-on training with generally no flexibility on these regulations. In response to COVID-19, the industrial training community has had to make modifications that were accepted by regulators because there were no other options. The changes to these training programs need to be evidence-based to ensure effectiveness in protecting worker health, safety, and infrastructure. This is an opportunity for motor learning research to shape industrial training; such a living lab can help inform models of skill learning. Concepts such as observational learning, feedback delivery, and practice schedules are currently being integrated into remote motor learning.

The current state of publishing also gives us pause for reflection. The proliferation of predatory journals that “are driven by self-interest, usually financial, at the expense of scholarship” (Grudniewicz et al., 2019), should be a concern for all researchers who wish to avoid the danger of nonreplicable studies, or worse. Motor learning researchers must continue to enhance rigor in experimental design and statistical power (Lohse et al., 2016). On the other hand, the proliferation of open-access (but peer-reviewed) articles, journals, and platforms, plus registered protocols and other prepublication repositories, are positive advancements in moving the research forward.

Our view of the last 40 years of motor learning research has been seen through rose-tinted glasses. The resurgence that we have documented has occurred amidst many changes in how the research is being done and who is doing it. An informal, unscientific poll of some leading researchers in kinesiology departments at U.S. and Canadian universities revealed a surprising consensus: required courses in motor learning at the undergraduate level have been reduced leading to fewer pressures on universities to fund faculty renewals. The growth in importance of motor learning in other areas of study notwithstanding (as described in the previous sections), motor learning, once considered a core field of study, has diminished within the academic discipline of kinesiology. Whether or not this is a temporary moment in the cyclical nature of academic flavors, or a permanent change in the landscape of kinesiology, we are confident that the field of study itself will continue to grow and add important information for theory and application.

Acknowledgment

The authors thank the following colleagues for their comments on an earlier draft of this article: Dick Magill, Carolee Winstein, Howie Zelaznik, John Shea, Nikki Hodges, Danielle Levac, Diane Ste-Marie, David Anderson, and Rich Van Emmerik.

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Lee is professor emeritus with the Dept. of Kinesiology, McMaster University, Hamilton, ON, Canada. Carnahan is with the Marine Inst. of Memorial University of Newfoundland, St. John’s, NL, Canada.

Lee (scapps@mcmaster.ca) is corresponding author.
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