Background: Controversy exists about whether breaking pitches are more stressful than are fastballs. Previous biomechanical studies compared kinematics but not kinetics. Hypothesis: Elbow and shoulder forces and torques are statistically different among the fastball, curveball, change-up, and slider. Study Design: Descriptive laboratory study. Methods: Twenty-one healthy collegiate pitchers were studied with a high-speed automated digitizing system. All subjects threw fastballs (n = 21), most threw curveballs (n = 20) and change-ups (n = 19), and a few threw sliders (n = 6). Wrist, elbow, and shoulder kinetics were calculated using inverse dynamics. Nine kinetic and 26 kinematic parameters were compared among the different pitch types using repeated-measures analysis of variance. Results: At the shoulder, internal rotation torque, horizontal adduction torque, abduction torque, and proximal force were significantly less in the change-up than in the other 3 pitches. Shoulder horizontal adduction torque was greater in the fastball than in the curveball and slider. Shoulder proximal force was greater in the slider than in the curveball. Elbow proximal force was less in the change-up than in the other 3 pitches. Elbow varus torque was greater in the fastball and curveball than in the changeup. Elbow flexion torque was greater in the curveball than in the change-up. The curveball and change-up demonstrated kinematic differences from the fastball, consistent with previous studies. Conclusion: There were significant kinematic differences between the fastball and curveball but few kinetic differences. The change-up had lower joint kinetics, lower angular velocities, and different body positions than the other 3 pitch types had. Results for the slider were inconclusive because of small sample size. Clinical Relevance: Because the resultant joint loads were similar between the fastball and curveball, this study did not indicate that either pitch was more stressful or potentially dangerous for a collegiate pitcher. The low kinetics in the change-up implies that it is the safest.

Variability is often introduced by an external agent (e.g., an instructor) during practice with the purpose of enhancing motor learning. Using a task analysis approach, we provide a framework to examine the effects of intervention-induced variability. We propose that variability may have markedly different consequences on learning depending on the task level at which it is introduced.

The authors examined 10 expert and 10 novice baseball batters' ability to distinguish between a fastball and a change-up in a virtual environment. They used 2 different response modes: (a) an uncoupled response in which the batters verbally predicted the type of pitch and (b) a coupled response in which the batters swung a baseball bat to try and hit the virtual ball. The authors manipulated visual information from the pitcher and ball in 6 visual conditions. The batters were more accurate in predicting the type of pitch when the response was uncoupled. In coupled responses, experts were better able to use the first 100 ms of ball flight independently of the pitcher's kinematics. In addition, the skilled batters' stepping patterns were related to the pitcher's kinematics, whereas their swing time was related to ball speed. Those findings suggest that specific task requirements determine whether a highly coupled perception-action environment improves anticipatory performance. The authors also highlight the...

Body-machine interfaces establish a way to interact with a variety of devices, allowing their users to extend the limits of their performance. Recent advances in this field, ranging from computer interfaces to bionic limbs, have had important consequences for people with movement disorders. The authors provide an overview of the basic concepts underlying the body-machine interface with special emphasis on their use for rehabilitation and for operating assistive devices. They outline the steps involved in building such an interface and highlight the critical role of body-machine interfaces in addressing theoretical issues in motor control as well as their utility in movement rehabilitation.

A hallmark of skilled motor performance is behavioral flexibility – i.e., experts can not only produce a movement pattern to reliably and efficiently achieve a given task outcome, but also possess the ability to change that movement pattern to fit a new context. In this perspective article, we briefly highlight the factors that are critical to understanding behavioral flexibility, and its connection to movement variability, stability, and learning. We then address how practice strategies should be developed from a motor learning standpoint to enhance behavioral flexibility. Finally, we highlight some important future avenues of work that are needed to advance our understanding of behavioral flexibility. We use examples from sport as a context to highlight these issues, especially in regard to elite performance and development.

Prior learning of a motor skill creates motor memories that can facilitate or interfere with learning of new, but related, motor skills. One hypothesis of motor learning posits that for a sensorimotor task with redundant degrees of freedom, the nervous system learns the geometric structure of the task and improves performance by selectively operating within that task space. We tested this hypothesis by examining if transfer of learning between two tasks depends on shared dimensionality between their respective task spaces. Human participants wore a data glove and learned to manipulate a computer cursor by moving their fingers. Separate groups of participants learned two tasks: a prior task that was unique to each group and a criterion task that was common to all groups. We manipulated the mapping between finger motions and cursor positions in the prior task to define task spaces that either shared or did not share the task space dimensions (x-y axes) of the criterion task. We found that if the prior task shared task dimensions with the criterion task, there was an initial facilitation in criterion task performance. However, if the prior task did not share task dimensions with the criterion task, there was prolonged interference in learning the criterion task due to participants finding inefficient task solutions. These results show that the nervous system learns the task space through practice, and that the degree of shared task space dimensionality influences the extent to which prior experience transfers to subsequent learning of related motor skills.