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Many tasks, such as typing a password, are decomposed into a sequence of subtasks that can be accomplished in many ways. Behavior that accomplishes subtasks in ways that are influenced by the overall task is often described as "skilled" and exhibits coarticulation. Many accounts of coarticulation use search methods that are informed by representations of objectives that define skilled. While they aid in describing the strategies the nervous system may follow, they are computationally complex and may be difficult to attribute to brain structures. Here, the authors present a biologically- inspired account whereby skilled behavior is developed through 2 simple processes: (a) a corrective process that ensures that each subtask is accomplished, but does not do so skillfully and (b) a reinforcement learning process that finds better movements using trial and error search that is not informed by representations of any objectives. We implement our account as a computational model controlling a simulated two-armed kinematic "robot" that must hit a sequence of goals with its hands. Behavior displays coarticulation in terms of which hand was chosen, how the corresponding arm was used, and how the other arm was used, suggesting that the account can participate in the development of skilled behavior.
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Shah et al. (2013) studied this question.
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