| Sumario: | The plasticity of the living matter of our nervous system, in short, is the reason why we do a thing with difficulty the first time, but soon do it more and more easily, and finally, with sufficient practice, do it semi-mechanically, or with hardly any consciousness at all.” --William James, 1899. It is over 100 years since James described the acquisition of skill. How much, or how little, have recent advances in science changed the way we think about skill learning? What theories and ideas do we still hold dear and which have we discarded? Advances in neuroimaging over the past 20 years have provided insight into the dynamic neural processes underlying human motor skill acquisition, focusing primarily on brain networks that are engaged during early versus late stages of learning. What has been challenging for the field is to tightly link these shifting neural processes with what is known about measurable behavioural changes and strategic processes that occur during learning. The complex nature of behaviour and strategy in motor learning often result in a trade-off between experimental control and external validity. Researchers in different disciplines have employed varying approaches to understand motor learning but with relatively little crosstalk. For example, those working from the psychological approach have been focused on topics such as whether implicit or explicit memory systems are engaged during learning; those working from the computational neuroscience approach have modelled fast and slow processes of learning; and those working from the cognitive neuroscience approach have identified large scale shifts in brain networks that are engaged during early versus late learning. The purpose of this Research Topic is to publish papers across these domains, in an effort to facilitate an integrative view of motor learning, to foster discussion across disciplines, and to stimulate collaboration. A cross disciplinary focus will help to elucidate the neural and cognitive processes underlying skill learning, and may serve to further accelerate translational paradigms that are grounded in skill learning theory. We welcome scientists from a variety of disciplines to submit their work.
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