Challenge point framework
The challenge point framework, created by Mark A. Guadagnoli and Timothy D. Lee (2004), provides a theoretical basis to conceptualize the effects of various practice conditions in motor learning. This framework relates practice variables to the skill level of the individual, task difficulty, and information theory concepts. The fundamental idea is that “motor tasks represent different challenges for performers of different abilities” (Guadagnoli and Lee 2004, p212). Any task will present the individual with a certain degree of challenge. However, the learning potential from this task difficulty level will differ based on the: Importantly, though increases in  task difficulty may increase learning potential, increased task difficulty is also expected to decrease performance. Thus, an optimal challenge point exists when learning is maximized and detriment to performance in practice is minimized. Importance and applications. Practice has been proposed as the most important factor for the “relatively permanent” improvement in the ability to perform motor skills (Adams 1964; Annett 1969; Fitts 1964; Magill 2001; Marteniuk 1976; Newell 1981; Salmoni et al. 1984; Schmidt and Lee 1999; Guadagnoli and Lee 2004). With all other variables held constant, skill increases with practice (Guadagnoli and Lee 2004). However, time devoted to practice  can be made more efficient by careful consideration of practice conditions. The challenge point framework presents a theoretical perspective to consider the roles of the level of the performer, the complexity of the task and the environment in regulating the learning potential during practice. Adjustment of these components to enhance motor learning can be applied to variety of contexts, including rehabilitation (Descarreaux et al. 2010; Onla-or & Winstein 2008) and simulation-based health professions education (Gofton 2006). History. The challenge point framework involves concepts generated through various lines of research including information theory, communications theory, and information processing (Lintern and  Gopher 1978; Martenuik 1976; K.M. Newell et al. 1991; Wulf and Shea 2002). Specific notions borrowed from prior research important to understanding the theoretical framework include: Components. It follows from the description of the challenge point framework that: Information available and task difficulty. Learning is fundamentally a problem-solving process. It has been proposed that with practice, there is reduced information available to the participant because better expectations are formed (i.e. practice = redundancy, therefore less uncertainty; Marteniuk 1976). However, increasing functional task difficulty results in less certainty about the predicted success of the action plan and the nature of  the feedback. At low levels of functional difficulty, the potential available information is low for performers in all skill levels. As functional task difficulty increases, the potential information available increases exponentially for beginners and less rapidly for intermediate and skilled performers. For experts, the potential information available increases only at the highest levels of functional task difficulty. Task difficulty and skill. Task difficulty has received considerable attention in prior research (Fleighman and Quaintance 1984; Gentile 1998). Important to the challenge point framework, task difficulty is not explicitly defined. Alternately, two broad categories can encompass these elements: Performance of a task  with low nominal difficulty will be expected to be high in all groups of performers (i.e. all skill levels). However, beginner performance will be expected to decline rapidly as nominal difficulty increases, whereas intermediate and skilled performance will decline less rapidly, and expert performance is expected to decline only at the highest nominal difficulty levels. Although the "Expert" skill level is useful to explain this framework, one may argue that experts should have a high level of predicted success for all nominal task difficulties. It is possible that once expertise is attained, these individuals are able to predict the outcome  of the ongoing task and modify ongoing processes in order to reach a suitable outcome (e.g. surgeons). Optimal challenge points. The optimal challenge point represents the degree of functional task difficulty an individual of a specific skill level would need to optimize learning (Guadagnoli and Lee 2004). However, this learning depends on the amount of interpretable information. Therefore, although increases in task difficulty may increase learning potential, only so much is interpretable, and task performance is expected to decrease. Thus, an optimal challenge point exists when learning is maximized and detriment to performance in practice is minimized. With increased practice  it is assumed that one's information-processing capabilities will increase (Marteniuk 1976). Therefore, the optimal challenge point will change as the individual's ability to use information changes, requiring further changes in functional difficulties in task to facilitate learning (Guadagnoli and Lee 2004). Practice variables and framework predictions. Contextual interference (CI) and action planning. Predictions from the challenge point framework with respect to CI (refer to motor learning; Guadagnoli and Lee 2004, p 219): Knowledge of results (KR) and feedback information. Predictions from the challenge point framework with respect to KR (refer to motor learning; Guadagnoli and Lee 2004 , p221):