Review
Motor skill learning between selection and execution

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Highlights

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    Skill learning involves changes in selection and execution-related representations.

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    Even highly-skilled movements are represented hierarchically.

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    Chunking and modularity allow flexible production of skilled movements.

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    Skill learning leads to reduced neural variability and specialized activity patterns.

Learning motor skills evolves from the effortful selection of single movement elements to their combined fast and accurate production. We review recent trends in the study of skill learning which suggest a hierarchical organization of the representations that underlie such expert performance, with premotor areas encoding short sequential movement elements (chunks) or particular component features (timing/spatial organization). This hierarchical representation allows the system to utilize elements of well-learned skills in a flexible manner. One neural correlate of skill development is the emergence of specialized neural circuits that can produce the required elements in a stable and invariant fashion. We discuss the challenges in detecting these changes with fMRI.

Section snippets

What is skill learning?

Motor skill learning generally refers to neuronal changes that allow an organism to accomplish a motor task better, faster, or more accurately than before. Beyond this accepted understanding of the common use of the word, there is little agreement in the literature about a more precise, scientific definition. Most researchers, however, agree on what skill learning is not. In other words, skill learning is currently mainly defined by its demarcation from other forms of learning.

First, skill

Selection versus execution

A first division in skill learning can be made between the levels of action selection and action execution [10]. The output of the execution level causes muscle activity – in other words, it includes motor cortical neurons that project to the spinal cord. Recent stimulation and recording studies in primary motor cortex (M1) suggest that small movement elements, so-called motor primitives, are encoded in the dynamics of sub-networks of neurons which produce replicable spatio-temporal patterns of

Neuronal correlates: recruitment versus efficiency

What are the neural correlates of skill learning? Investigation of this question is complicated by the fact that plasticity may involve multiple overlapping processes. Learning leads to neuronal recruitment – in other words, neurons not previously activated by the task become engaged 22, 23. This process may explain why the activity observed in fMRI studies often increases with learning 8, 24, 25, 26.

Equally commonly, however, studies find that activity decreases with learning, especially after

Neuronal correlates: stabilization and specialization

An important alternative idea in the search for neural correlates of skill learning is that training leads to the stabilization of the underlying neural network [37]. Reductions in neural variability during the production of the skill with learning have been observed in several different systems 22, 38, 39, 40. Concomitant with these neuronal changes, the skilled behavior itself also becomes more invariant 41, 42, 43. As a result, it is often unclear whether the reduced neural variability

Chunking

Motor chunking is one of the key arguments for a hierarchical representation of motor skill. Proposed by Lashley in 1951 [48], the concept of motor chunking has come again to prominence over the last years. With learning, in addition to sequence completion becoming faster and more accurate, performance starts to show idiosyncratic temporal groupings or chunks [49]. Elementary movements that are bound into one chunk (Figure 2A) are retrieved faster and more accurately than when the selection

Modularity of skill features

The dynamical systems view holds that the spatio-temporal evolution of an action is encoded in the intrinsic dynamics of a pattern generator at the execution level [15]. For simple movements, such as reaching, the required muscle commands seem to be inseparably represented from their timing [59]. Recent computational work further shows that this principle could scale up to more complex sequential movements such as writing a full word [60]. Thus, in this low-level view of motor skill encoding

Concluding remarks

The next important challenge is to understand the neuronal underpinnings of hierarchical skill encoding (outstanding questions are listed in Box 3). Note that our current model is mainly representational and that we have resisted the temptation to provide a direct mapping between the different levels and specific neural regions because this relationship is likely to be complex. For example, different subregions of both cerebellum and basal ganglia form partially parallel loops with multiple

Acknowledgments

We would like to thank Nobuhiro Hagura, Jing Xu, and the Motor Control Lab for comments on the earlier versions of the manuscript. The paper was supported by a grant from the Wellcome trust (094874/Z/10/Z) and James McDonnell foundation, both to J.D., and a Sir Henry Wellcome Fellowship (098881/Z/12/Z) to K.K.

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