Understanding: Training and Variability in Human Performance: Navigating Specificity, Adaptability, and the Unknown
A Dive into the Doctrine of Specificity, Skill Transfer, and Performance Variability in Real-World Scenarios
Note: This essay was prepared with the research assistance and ghostwriting of ChatGPT 4.0.
Author's Preface
I have taught various subjects for decades, both informally and formally. Over the years, I have taught aspects of computing, and for a time, I even taught introductory computer science courses for pay. Beyond that, I have also taught guitar professionally, and I have spent decades teaching various martial arts, specifically karate and Balintawak Eskrima.
Throughout my teaching career, I have always been deeply concerned with how to best teach any topic, but my focus has centered on teaching motor skills and athletic activities. During graduate school, my interest in athletics led me to study a related field: psychophysics and reaction time. This academic pursuit only deepened my interest in understanding how people learn and improve their physical skills.
I’ve read extensively about motor skills and learning, but I often find myself realizing how many questions remain unanswered. While I’ve had countless discussions and debates with colleagues and peers, and I’ve read articles exploring live training versus stylized training in martial arts, I recognize that much of what exists on the topic is opinion-based. There is, unfortunately, very little systematic study on how best to teach and learn these types of skills.
This realization prompted me to revisit the topic today, using ChatGPT to generate an introductory essay on learning skills. In this essay, I explore key considerations such as human variability, the specificity of training, and the importance of generalization in the learning process.
Introduction:
In the world of sports, education, and professional training, the tension between specificity and generalization in skill development remains a subject of intense debate. The doctrine of specificity asserts that training should mirror the desired performance as closely as possible to ensure maximum skill transfer. However, real-world scenarios are unpredictable and variable, raising the question of how effectively specialized skills translate to novel situations. Performance is inherently variable, not just because of external factors like environment and opposition, but also due to internal randomness within human neurological processes. This essay will explore the balance between specificity and generalization in training, the challenges of transferring learned skills to new contexts, and the mystery of variability in both human performance and cognition.
Doctrine of Specificity in Athletic Training
The doctrine of specificity asserts that training should closely mirror the desired performance outcomes to yield the best results (Haff & Triplett, 2016). The idea is simple: practicing a skill in the environment or under the conditions in which it will be used ensures better transfer of training to real-world performance. This is a central tenet in many sports, where athletes and trainers emphasize the need to simulate game-like conditions to optimize performance gains (Issurin, 2010).
However, this principle faces challenges when applied to the inherent variability in real-world situations. No two performances are identical due to factors such as changing environmental conditions, internal physiological states, or psychological stress. For example, in basketball, no two shots are exactly alike, and in rock climbing, every cliff face presents new challenges, even if it is one the climber has scaled before (Schmidt, R. A., & Lee, T. D.2019).
Specific vs. Generalized Training Patterns
There is an ongoing debate between specificity and generalization in training (Ericsson, 2016). Proponents of specificity argue that training should focus on developing the precise skills needed for performance under controlled conditions. However, real-world situations often demand a higher degree of generalized adaptability. In sports like martial arts or soccer, generalized training, which focuses on building agility, decision-making, and spatial awareness, becomes essential when athletes encounter unpredictable circumstances (Schmidt & Lee, 2019).
The effectiveness of generalized versus specific training also applies across domains outside of sports. For instance, in professional disciplines like medicine or emergency response, training in generalized problem-solving skills can be just as valuable as learning specific procedures, given the unpredictable nature of real-world challenges (Feltovich, Prietula, & Ericsson, 2006).
Evidence on Generalized vs. Specific Patterns
There is evidence that generalized training may enhance long-term performance by fostering greater adaptability. For example, a study by Carson and Collins (2016) on elite athletes found that those who engaged in a blend of generalized and specific training displayed better adaptability in competitive situations. This contrasts with athletes who focused solely on specific drills and were less capable of responding to unexpected changes during performance.
Variability and Adaptability in Real-World Scenarios
Consider sports like rock climbing or basketball. In rock climbing, no two ascents are the same. Factors such as the weather, the texture of the rock, or the climber's mental state on any given day introduce variability into each performance (Ericsson, 2016). Similarly, in basketball, while athletes may train specific drills like shooting or dribbling, the dynamic nature of the game—where defenders and situations change rapidly—requires the player to adapt constantly. This necessity to adapt ties directly into the doctrine of skill generalization.
In essence, real-world performance is about balancing between practiced, specific actions and the ability to transfer those skills to novel scenarios. This is also true in cognitive fields, where students of mathematics, for instance, learn core concepts but must adapt and apply these concepts to different problems, each with its own peculiarities (Ericsson, 2016).
Challenges in Applying Knowledge and Skill Transfer
In many fields, there is a gap between rote learning and real-world application (Schmidt & Bjork, 1992). While individuals can master specific skills or knowledge through deliberate practice, they often struggle to apply this knowledge in unpredictable real-world situations. This is particularly problematic when learners or athletes are trained in isolated or controlled environments but then must perform in situations where novelty is a constant factor (Schmidt & Lee, 2019).
This brings us to the challenge of skill transfer. The ability to apply knowledge or a skill learned in one context to a new and novel situation is crucial. Research shows that while it is possible to transfer skills across contexts, it is much easier when the new situation shares common elements with the original learning context (Thorndike, 1906). However, the more novel the situation, the harder it becomes to apply previously learned knowledge, particularly when training was specific rather than generalized.
For example, an athlete who practices dribbling on a flat basketball court will struggle to adapt when playing on uneven surfaces. In contrast, an athlete trained to dribble under various environmental constraints will perform better in unexpected circumstances. Similarly, in fields like engineering or medicine, practitioners often face unique problems that do not perfectly align with their training, forcing them to draw on general principles rather than specific techniques learned during their education (Chi, 2006).
Novelty and Commonality in Real-World Situations
The real world is full of commonalities and novelties. Without some degree of commonality, we would not be able to generalize learned skills across different contexts (Gick & Holyoak, 1983). However, it is the novel aspects of each situation that challenge our capacity to adapt. For example, while a mathematician might recognize a familiar equation structure in a new problem, the particular context of the problem introduces novel elements that require flexible thinking (Feltovich et al., 2006).
In physical activities like rock climbing, the commonality might be the climber's grip technique, while the novelty comes from the unique features of each climb. Similarly, in team sports, there is commonality in strategic formations, but the individual decisions made by each player introduce constant novelty, necessitating on-the-fly adaptation.
External Situations vs. Internal Variability in Performance
It is important to distinguish between external variability—the variability introduced by changes in the environment—and internal variability—the variability inherent to human performance. No matter how consistent an athlete's training, their performance will always fluctuate due to factors like fatigue, mental state, and subtle differences in muscle coordination (Ericsson, 2016). Likewise, in intellectual tasks, cognitive performance can vary depending on factors such as stress, fatigue, or distractions (Ericsson, 2016).
The Mystery of Human Processing and Performance Variability
The variability in human performance also remains a mystery. Neural plasticity allows for remarkable adaptability, but it also introduces variability that cannot always be predicted or controlled (Rothwell, 2012). This randomness parallels the behavior of large language models and other complex systems, where even identical inputs do not always produce identical outputs. Similarly, the human brain—while capable of recognizing patterns and generalizing knowledge—often performs inconsistently under real-world conditions, even in tasks that have been over-learned through extensive practice (Haff & Triplett, 2016).
Applying Training and Skills in Novel Situations
Given the variability both in external situations and internal human processing, the challenge in any training program is to optimize the transfer of skills to novel contexts (Schmidt & Lee, 2019). This is especially true in fields like military training, where soldiers must adapt to rapidly changing environments and unpredictable scenarios. Studies on military training suggest that soldiers perform better when they are exposed to a variety of generalized scenarios that challenge their adaptability, rather than when they are trained in specific, controlled environments (Ericsson, 2016).
In other contexts, such as problem-solving in mathematics or engineering, the process of applying skills to novel situations is referred to as far transfer. The ability to achieve far transfer depends heavily on the degree to which general principles have been internalized, allowing the learner to recognize patterns across different contexts (Chi, 2006).
The Role of Metacognition in Problem-Solving
In some cases, individuals have time to engage in metacognition—thinking about their own thinking processes. Metacognition allows for deliberate problem-solving and reflection, which can lead to improved performance in novel situations (Schmidt & Bjork, 1992). However, in high-pressure situations, such as athletic performance or emergency response, there is often no time for metacognitive strategies. In these cases, quick, instinctual reactions based on over-learned skills become essential (Collins et al., 2016).
Human Cognition and the Mystery of Emergent Properties
Finally, much like in machine learning, human cognition exhibits emergent properties—the ability to recognize patterns, paraphrase, summarize, and apply knowledge in ways that cannot always be fully explained by our understanding of brain mechanisms (Rothwell, 2012). This parallels the randomness and variability observed in complex systems, where outcomes often seem unpredictable despite following known principles.
Here’s an improved version of the summary:
Summary
In any form of training—whether for sports, intellectual pursuits, or professional disciplines—variability is an inevitable factor, stemming from both external circumstances and internal human processes. While the doctrine of specificity stresses the importance of training under conditions that closely replicate real-world performance, the ability to generalize skills provides greater adaptability in the face of unpredictable scenarios. Performance variability, shaped by both neurological factors and environmental influences, remains a complex phenomenon that is not yet fully understood.
As we explore the balance between specificity and generalization, it becomes clear that no single training method can address all aspects of human performance. Training that focuses too narrowly on specific patterns may fail when faced with the real-world unpredictability of dynamic situations. On the other hand, generalized training that promotes broader adaptability offers flexibility but may not fully develop the refined skills required for specific tasks. To optimize performance across various contexts, an effective training regimen must strike a balance between precise skill-building and fostering adaptability, recognizing that performance is inherently variable and shaped by a myriad of factors beyond our control.
References
Carson, H. J., & Collins, D. (2016).
Implementing the Five-A Model of Technical Refinement: Key Roles of the Sport Psychologist. Journal of Applied Sport Psychology, 28(4), 469–482. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5020347/
Author Note: H. J. Carson and D. Collins are sport psychologists who focus on skill refinement and psychological approaches in sports training.
Reading Note: This paper discusses the Five-A Model of Technical Refinement, a framework for improving technical skills in athletes, with an emphasis on the role of sport psychology in performance enhancement.
Chi, M. T. H. (2006).
Two approaches to the study of experts’ characteristics. In K. A. Ericsson, N. Charness, P. J. Feltovich, & R. R. Hoffman (Eds.), The Cambridge handbook of expertise and expert performance (pp. 21–30). Cambridge University Press. https://psycnet.apa.org/record/2006-10094-002
Author Note: Michelene Chi is a cognitive scientist known for her work on learning and expertise.
Reading Note: This chapter presents two different approaches for studying the characteristics of experts, focusing on how expertise is developed and maintained.
Ericsson, K. A. (2016).
The influence of experience and deliberate practice on the development of superior expert performance. In K. A. Ericsson, N. Charness, P. J. Feltovich, & R. R. Hoffman (Eds.), The Cambridge handbook of expertise and expert performance (pp. 683–704). Cambridge University Press. https://www.researchgate.net/publication/237063322_The_Influence_of_Experience_and_Deliberate_Practice_on_the_Development_of_Superior_Expert_Performance
Author Note: K. Anders Ericsson was a leading researcher on expertise and performance, most known for his work on deliberate practice.
Reading Note: This chapter examines the importance of deliberate practice and experience in the development of expert performance, highlighting how sustained, focused effort leads to superior results.
Feltovich, P. J., Prietula, M. J., & Ericsson, K. A. (2006).
Studies of expertise from psychological perspectives. In K. A. Ericsson, N. Charness, P. J. Feltovich, & R. R. Hoffman (Eds.), The Cambridge handbook of expertise and expert performance (pp. 41–67). Cambridge University Press. https://www.researchgate.net/publication/200772882_Studies_of_expertise_from_psychological_perspectives
Author Note: P. J. Feltovich and M. J. Prietula, along with K. A. Ericsson, are scholars focused on cognitive psychology and expertise.
Reading Note: This reading delves into how psychological perspectives contribute to the understanding of expertise, exploring cognitive processes that underpin expert performance.
Gick, M. L., & Holyoak, K. J. (1983).
Schema induction and analogical transfer. Cognitive Psychology, 15(1), 1–38. https://psycnet.apa.org/record/1983-20819-001
Author Note: Mary L. Gick and Keith J. Holyoak are cognitive psychologists who have contributed significantly to research on learning, reasoning, and problem-solving.
Reading Note: This classic study investigates how schema induction supports analogical reasoning, providing key insights into how people transfer knowledge across different domains.
Haff, G. G., & Triplett, N. T. (2016).
Essentials of strength training and conditioning. Human Kinetics. https://www.amazon.ca/Essentials-Strength-Training-Conditioning-Resource/dp/149250162X
Author Note: Gregory G. Haff and N. Travis Triplett are experts in strength and conditioning, with significant contributions to sports science education.
Reading Note: This textbook is a comprehensive guide to strength and conditioning principles, covering exercise science, training programs, and the role of conditioning in athletic performance.
Rothwell, J. C. (2012).
Control of human voluntary movement. Springer. https://www.amazon.ca/Control-Human-Voluntary-Movement-Rothwell/dp/1468476904
Author Note: John C. Rothwell is a researcher specializing in motor control and neurophysiology.
Reading Note: This book explores the mechanisms that control voluntary movement in humans, with an emphasis on the neurological and physiological processes involved in motor control.
Schmidt, R. A., & Bjork, R. A. (1992).
New conceptualizations of practice: Common principles in three paradigms suggest new concepts for training. Psychological Science, 3(4), 207–217. https://psycnet.apa.org/record/1993-00369-001
Author Note: Richard A. Schmidt and Robert A. Bjork are cognitive psychologists known for their work on motor learning and memory.
Reading Note: This article introduces new frameworks for understanding practice and training, challenging traditional concepts with insights from multiple psychological paradigms.
Schmidt, R. A., & Lee, T. D. (2019).
Motor learning and performance: From principles to application. Human Kinetics. https://www.amazon.ca/Motor-Learning-Performance-Principles-Application/dp/1492571180
Author Note: Richard A. Schmidt and Timothy D. Lee are prominent researchers in motor behavior and learning.
Reading Note: This text extensively covers motor learning principles and their application to performance, particularly focusing on how variability in learning affects performance outcomes.
Thorndike, E. L. (1906).
The principles of teaching based on psychology. Seiler. https://www.amazon.ca/Motor-Learning-Performance-Principles-Application/dp/1492571180
Author Note: Edward L. Thorndike was a pioneering educational psychologist whose work on learning theory laid the foundation for modern educational psychology.
Reading Note: This classic work explores the principles of teaching from a psychological perspective, emphasizing the importance of learning through experience and structured practice.