The effect of variability in the level of execution redundancy for different skill levels on basketball free throw learning

Dissertation for Master's Degree

Physical Education and Sports Sciences

General Orientation

Introduction

Man is a creature that is constantly learning. is In the field of movement, learning is so important that it can even be said that human life depends on it. Coaches in sports fields are looking for optimal methods of teaching skills to their students, methods that spend less money and time and students can learn skills in the best way. Usually, people practice skills in the hope that they will perform that skill again in the future and in a similar environment (Megill[1] 2011). One of the methods that coaches use to teach skills is variable practice. In this training method, the person changes the movement parameters or environmental conditions in each performance (Mogil 2011). The general opinion is that with this training method, a person learns a general rule for different future situations, which will facilitate the execution of the movement (Schmidt and Lee [2] 2011, Magill 2011). But these results are based on research that considers variability in the goal of movement, and it is possible that when a person experiences variability in how to reach the goal, the results may be different from the previous statements (Ranjanathan and Newell[3] 2013). Few researches have been done in this field, and previous researches have various shortcomings in some cases. This research aims to help clarify this issue by considering various conditions, including the skill level of people. They try to bring people to a high level of skill through different training methods. Motor skill is usually defined as performance at very high levels consistently (Guthrie [4] 1952). Therefore, the definition of high skill level has an inverse relationship with variability. Although this emphasis is placed on reducing variability more at the target level (such as hitting a target), sometimes it is implicitly assumed that variability at the target level is necessarily accompanied by a decrease in variability at the execution level (Ranjanathan and Newell 2013). But various researches have shown that reduction in variability in goal attainment is not necessarily associated with reduction in variability in performance level (Ranjanathan and Newell 2013). Also, research has shown that there is variability in the level of performance in highly skilled people (Bernstein [5] 1967). These findings led to the statement that variability is necessary for performance at both expert and novice levels (Davids, Glaser, Araujo, and Bartlett [6] 2003). However, an important question that arises is whether the meaning of this evidence is that training programs need to have variability in order to facilitate learning or not?

An important point in this context is to distinguish between the internal variability of the motor system and the variability that is applied by an external agent such as a coach. According to Bernstein (1967), the human body is a system with many redundancies, and its degrees of freedom are usually more than the degrees of freedom required for most tasks, and there are many different ways to achieve the same result. Usually, the variability seen in the behavior of skilled people is very low and at the same time it is quite useful to compensate for small deviations (Ranjanathan and Newell 2013). Ranjanathan and Newell (2013) divide variability into two levels. According to them, variability is either at the goal level or at the implementation redundancy level [7]. But the variability itself at the target level is also divided into two sub-branches, structured and unstructured. They (Ranjanathan and Newell 2013) define unstructured variability as a noise-like variability in which several parameters change from one effort to another at the same time, but in structured variability, a specific parameter changes systematically from one effort to another. In general, there is evidence that shows that variability in the target level systematically leads to generalization and transfer to new task conditions (Van Rossam [8] 1990).One explanation for this variability effect is that variability improves the relationship between motor parameters and task outcomes, which leads to the development of a summary rule that can be generalized to other unpracticed conditions (Schmidt [9] 1975). Suppose that in a force production task by two fingers the target force is 10 N (Latash, Scholes and Schooner[10] 2002). Variability in the level of performance can be applied in such a way that each finger applies a certain amount of force in each effort, the total of which is 10 newtons. For example, a number of different states that can exist are as follows: (2,8), (4,6), (3,7), (1,9). Unfortunately, not much work has been done in the field of introducing variability at the execution level (Ranjanathan and Newell 2010, 2013).

Although motor learning studies measure learning through memory and transfer tests, this type of manipulation allows us to measure another aspect of learning, which is flexibility [11] (Ranjanathan and Newell 2013). The implication is whether participants can quickly find an alternative solution when a well-practiced solution is no longer available. Researchers believe that this type of variability will have at least two effects on learning: first, it can lead to the emergence of a more optimal solution because there is a search element in this variability, and second, it can increase flexibility by allowing participants to use alternative solutions in cases where certain solutions are not available due to changes in the environment or body (Ranjanathan and Newell 2013). According to the thought of practice specificity (Protheo [12] 1992), forcing people to use different solutions during practice increases the flexibility of the task. Also, according to the practice variability hypothesis, introducing variability in practice leads to the formation of a stronger law between the result and the selected parameters (Schmidt 1975). However, the opposite idea of ??this view is that the flexibility to use different solutions is a result of learning task-specific parameters or control parameters. For example, studies in the field of the equilibrium point hypothesis show that when an organ is suddenly disturbed, it has the ability to achieve the desired goal (Kelso and Holt [13] 1980). These results can be interpreted in such a way that the realization of this is due to the learning of a parameter that affects the equilibrium point (Ranjanathan and Newell 2010). Also, the hypothesis of optimal feedback control (Tadoro, Jordan [14] 2002) states that only deviations related to task execution (related to achieving the goal) are corrected.  But recently, some researches have been conducted, the results of which are not consistent with the hypothesis of exercise variability and exercise specificity. Ranjanathan and Newell (2010) found in a research that practicing at the level of performance redundancy does not cause flexibility in the task. In their research, two groups of subjects practiced a task of reaching the goal in a variable way in the ways to reach the goal and in a fixed way (low variability). At the end, the groups were transferred to two tests, one with fixed conditions and the other with variable conditions (at the level of implementation redundancy). The results showed that the group that trained consistently had less variability in its movement pattern. These results were similar in both tests. The researchers concluded that the flexibility in the inhibition task (such as the task in question) comes from learning a specific parameter of the task that is related to the movement goal. In another research, Ranjanathan and Newell (2010) compared the variability in the goal level and the redundancy level of implementation. Their goal was whether introducing variability at the level of execution redundancy, like variability at the target level, would improve learning or not. In their research, there were four groups, the first two groups experienced variability (high and low) at the target level during practice, and the other two groups experienced variability (high and low) at the level of execution redundancy. Regarding the variability in the target level, the results of the research were similar to the previous results and the variable group performed better. But regarding the variability in the implementation level, the variable group did not perform better.