The ability to learn defines much that is unique about human behavior and underlies many aspects of sport and exercise psychology (SEP). Attempts to develop sweeping laws of learning have generally been unsuccessful, and it is unlikely that a universal theory of learning can be developed. Learning is often described as a process during which lasting changes occur in the potential that an individual has for a specific behavior. Such changes are a consequence of experience within a particular environment, rather than attributes of growth or development or temporary changes caused by fatigue, boredom, injury or even drugs or aging. Some authors define learning as a “biological device” that facilitates primarily adaptive changes that extend an individual’s capability to survive.
This entry provides a much-condensed summary of learning as it has been understood over the past120 years of study. Influential concepts and theories of learning are discussed in a relatively chronological sequence, and an effort is made to show how the theories culminate in recent approaches to learning in sport and exercise. Behaviorist theories regard learning to be an observable effect of the environment on an organism’s behavior, whereas cognitive theories regard learning to be relatively permanent storage of knowledge as processes or representations in the brain. Constructivist theories consider learning to be the active construction of knowledge about the world. Jerome Bruner (1915– ), for example, argued that learning is primarily driven by an active process of discovery about the meaning of information.
Learning by Association
A forerunner to any of these approaches, and probably the firstborn theory of learning, held that learning was a consequence of the formation of associations through experience. A person may feel pain on the first occasion that he or she goes running and thereafter associate exercise with pain as a consequence of learning the stimulus–response (S–R) relationship. Learning by association primarily is a function of conditioning. Ivan Pavlov (1849–1936) first demonstrated this as classical conditioning. By presenting food at the same time that he sounded a bell, Pavlov conditioned dogs to associate the idea of food with the sound of a bell. The dogs thereafter salivated when they heard a bell, regardless of whether food was present. Pavlov’s work paved the way for behaviorism, as conceptualized by John Watson (1878–1959), who rejected subjective inferences about the influence of cognition on behavior in favor of objective measurement of external, overt actions. Watson argued that conditioning is the key mechanism that underlies learning in animals and humans. He argued that all are born tabula rasa (a blank slate), and the environment governs how each learns to behave. Watson (1930) famously claimed that the aim of psychology is “to predict, given the stimulus, what reaction will take place; or, given the reaction, state what the situation or stimulus is that has caused the reaction” (p. 11).
Shaping to Learn
An important building block of behaviorism was provided by Edward Thorndike’s (1874–1949) “Law of Effect,” which states that behaviors are likely to be repeated if they are followed by favorable (e.g., pleasant) consequences but will eventually cease if they are followed by unfavorable (e.g., disagreeable) consequences. Thorndike showed that cats trying to reach a food outside a puzzle box eventually pressed a lever that gave them access to the food and that over trials they became faster at pressing the lever because of the favorable outcome associated with that response. In essence, the cats had learned from the consequences of their behaviors. Thorndike’s work gave rise to operant (instrumental) conditioning, as demonstrated by B. F. Skinner (1904–1990). Skinner showed that behaviors could be modified by the type of consequence that followed a desired response. That is, a behavior would occur with greater frequency if it was reinforced and with less frequency if it was punished or even be extinguished if there was no consequence. Skinner also showed that behaviors could be gradually modified (shaped) by reinforcing iterations that approximated the desired response. Coaches often use shaping to modify an inappropriate technique in sport, or chaining to link appropriate responses together. A coach might use verbal praise to reinforce gradual increases in the height of the ball toss when a child serves at tennis, for example, and chaining might involve linking the ball toss to knee bend, followed by the swing of the tennis racquet, the snap of the wrist at contact, and finally the follow-through.
Many criticisms have been directed at behaviorism. For example, even when a behavior has been learned through conditioning, individuals, and animals, clearly can change the behavior if new information becomes available. Gestalt psychology abandoned the step-by-step S–R approach to learning in favor of an approach in which behaviors were seen to be driven by dynamic patterns of information available in the environment as a whole. But the great criticism of behaviorism, as embodied in Sigmund Freud’s (1836–1939) psychodynamic approach, was that by considering only objective, observable behaviors, behaviorism ignored the influence of cognition, internal mental states of mind, on behavior.
Thinking to Learn
Cognitive approaches to learning differ from the behaviorist approach in that they define learning in terms of relatively permanent changes in organization and storage of information as a consequence of experience, rather than relatively permanent changes in behavior itself. Consequently, internal mental processes, such as information encoding and processing, perception and memory, and insight or intuition are seen to be key factors in learning, which mediate the relationship between a stimulus and a response.
Cognitive theories therefore try to account for the influence of internal thought processes on learning. Albert Bandura (1925– ) proposed that internal psychological factors (the person), external observational factors (behavior), and the situation all interact to influence social-interpersonal forms of learning. Social learning theory, now called social cognitive theory (SCT), proposed that most learning occurred observationally, via modeling, and the tendency for a person to persist at it was governed by factors such as the person’s sense of their own capability to carry out the required behavior effectively (i.e., self-efficacy).
Multistore models of memory that propose separate sensory, short-term, and long-term stores for information and multicomponent models of memory that explain how task-relevant information is temporarily stored and manipulated have provided a popular framework for examining the role of internal mental processes in learning. Evidence suggests that long-term memories (LTMs) present as rewired patterns of activation that require a process of consolidation to be laid down permanently, whereas short-term memories present as patterns of neural activation that are somehow prolonged by working memory mechanisms such as subvocal rehearsal. Working memory facilitates a crucial component in most cognitive approaches to learning, which is verbal hypothesis testing about contingencies associated with actions, especially in terms of reasoning and problem solving. B. F. Skinner even delineated between rule-governed behavior and contingency-shaped behavior, because behaviorist approaches have difficulty accommodating the tendency for humans to verbally mediate behavior. Skinner argued that behaviors that solve a problem can arise from direct shaping by contingencies (operant condition) or from rules that are hypothesized by the person solving the problem or from instructions provided by an agent with prior experience of the problem. In sport, a particular behavior may be shaped gradually by its consequences or by verbal rules that the athlete acquires by hypothesis testing or by instructions from a coach who has previously acquired the relevant information.
Conscious and Unconscious Awareness of Learning
An important distinction that arises from the cognitive approach to learning is between conscious and unconscious aspects of behavior, most recently approached within the context of implicit and explicit learning. Much of our interaction with the environment is implicit, resulting in accrual of knowledge without conscious awareness and sometimes without even intent to learn. Experimental studies of implicit learning began in the 1960s, when Arthur Reber used Markovian grammar chains to study the way in which participants learned knowledge underlying complex tasks. When participants memorized lists of exemplars (letter strings) created using the artificial grammars, they could distinguish between grammatically correct and incorrect exemplars that they had not seen previously, even though they were unable to consciously express knowledge of the grammatical rules that supported their decisions.
The double dissociation between performance of a task and the ability to express knowledge that guides performance of that task has been demonstrated using other paradigms. For example, in the serial reaction time task (SRTT), participants are required to rapidly depress keys that match positions indicated on a monitor. When the same sequence of positions (usually about 12–15) is repeated on trials, participants learn to anticipate each position in the sequence and thus respond by depressing the matching keys very rapidly. Few participants become consciously aware that they are responding to a specific sequence of key presses and fewer still can report the sequence, suggesting that the sequence may have been learned implicitly.
Implicit and Explicit Motor Learning
The conscious and unconscious dichotomy has been applied to learning in SEP in the context of implicit and explicit motor learning. Left to their own devices, humans display a pervasive tendency to acquire declarative knowledge explicitly when they learn motor skills. Usually, this knowledge is accrued by instructions from an agent (such as a teacher or coach) and conscious hypothesis testing during a trial-and-error process in which the learner makes attempts to move in a way that solves the motor problem. In particular, visual feedback about the outcome of each attempt is used to confirm or refute the hypotheses that are tested. Take, for example, a father and son at a golf driving range. The father may instruct his son to hold the golf club in a particular way. The son may use this grip but see that the ball travels to the left. Consequently, the son may try a different grip and watch closely to see if the ball travels in the desired direction. If the grip works, it is likely that the information will be stored as declarative knowledge for further use.
The ability to test hypotheses and store and manipulate information that can be used to make motor responses is made possible by the information-processing capabilities of working memory. Implicit motor learning tries to discourage hypothesis testing about motor responses or disrupt working memory storage of information that can be used for hypothesis testing, thereby limiting the amount of declarative knowledge that is accumulated during learning. For example, when motor learners carry out a secondary working memory task, such as tone counting or random letter generation, they tend to be unable to test hypotheses about the primary motor task that they are practicing. Consequently, they learn the primary motor task implicitly. Other methods devised in the context of SEP cause implicit motor learning by reducing the commission of errors during practice or providing reduced feedback about the outcomes of the movement. These methods prevent working memory involvement in learning by removing the necessity or the ability, respectively, to test hypotheses. If a performer does not make an error when executing a movement, there is little point in testing a hypothesis; if a performer does not become aware of the outcome of each movement, it is not possible to test the outcome of a hypothesis. Another method that has been used to facilitate implicit motor learning entails presentation of a movement analogy (e.g., “kick like a dolphin”), which describes an appropriate technique by which to achieve the desired movement response, without the need to present explicit instructions. Analogy learning is only effective if the similar concept upon which it is based (a dolphin’s tail movement) is understood by the learner. While it is unlikely that any form of human motor learning is purely implicit or explicit, implicit motor learning techniques appear to reduce conscious access to task-relevant knowledge and thus reduce potential destabilization of automatic movement by conscious thought processes.
- Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In K. W. Spence & J. T. Spence (Eds.), The psychology of learning and motivation: Advances in research andtheory (Vol. 2, pp. 89–195). New York: Academic Press. Baddeley, A. D., & Hitch, G. J. (1974). Working memory. In G. A. Bower (Ed.), Recent advances in learning and motivation (Vol. 8, pp. 47–89). New York: Academic Press.
- Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.
- Bruner, J. (1960). The process of education. Cambridge, MA: Harvard University Press.
- Masters, R. S. W., & Maxwell, J. (2008). The theory of reinvestment. International Review of Sport and Exercise Psychology, 1, 160–183.
- Masters, R. S. W., & Poolton, J. M. (2012). Advances in implicit motor learning. In N. J. Hodges & A. M. Williams (Eds.), Skill acquisition in sport: Research, theory and practice (2nd ed., pp. 59–75). London: Routledge.
- Pavlov, I. P. (1927). Conditioned reflexes: An investigation of the physiological activity of the cerebral cortex (G. V. Anrep, Trans. & Ed.). London: Oxford University Press.
- Reber, A. S. (1967). Implicit learning of artificial grammars.Journal of Verbal Learning and Verbal Behavior, 5,855–863.
- Skinner, B. F. (1984). An operant analysis of problem solving. The Behavioral and Brain Sciences, 7,583–613.
- Thorndike, E. (1932). The fundamentals of learning.New York: AMS Press.
- Watson, J. (1930). New York: Norton.