From movement to skill : neural and behavioral mechanisms of motor sequence learning

Abstract

How do we learn new movements? The simple answer to this question is Through practice! Yet, a better response might be What aspect of motor learning are we talking about? Our capacity for learning new skills and for combining movements into new sequences is virtually unlimited. In contrast, our understanding of the mechanisms behind motor skill learning is still rather sparse. One part of the problem is that the topic of motor skill learning can be approached from many different angles. An athlete might ask How should I practice? or Which strategies work best?, the neuroscientist might wonder How does the brain form new memories for a movement and where are they stored?, while the child might ask Why does it take grandpa so long to swipe the unlock pattern on his smartphone? This thesis will explore some of the behavioral and neural mechanisms of motor skill learning.

One form of motor skill learning is sequence learning, i.e., learning to produce the right movements at the right time and order. Motor sequences have been studied extensively, since response times for individual movement elements (usually key-presses) can be precisely measured and different sequence properties, such as complexity and familiarity, can be easily manipulated. In this thesis, I made use of different sequence learning paradigms to explore 1) how different practice formats affect how flexibly we can use the acquired knowledge in related tasks (Study I), 2) how the information for different motor sequences is represented in the brain (Study II), and 3) how a specific brain area, the pre-supplementary motor area (preSMA), is involved in chunking, i.e., grouping individual key-presses into movement units (Study III).

In Study I we assigned participants to two different groups that practiced an implicit sequence learning task either via constant practice (i.e., constantly repeating the same sequence) or via variable practice (i.e., alternating between two different sequences). We found that variable practice led to better performance on a subsequent transfer test, where participants had to perform an entirely different sequence.

In Study II we found that familiar (trained) and novel motor sequences are represented by different patterns of neural activity, even in brain areas that did not change their mean level of activity for either sequence type. Moreover, we observed that the patterns of neural activity were related to the patterns of behavioral performance; sequences that were performed at similar speeds also evoked similar patterns of brain activity.

In Study III we demonstrated that transcranial magnetic stimulation of the preSMA increased response times for the next sequence element, but only under the demanding condition where the next response required both the initiation of a new chunk and a switch between hands. Together, these studies show how different practice strategies affect skill generalizability and how task difficulty and proficiency shape the neural implementation of motor skills.