http://www.mdlinx.com/neurology/top-medical-news/article/2016/07/15/1
Northwestern Medicine News
Consider
an everyday action such as tying shoelaces. It consists of discrete
halts in movement between continuous elemental actions, such as making a
loop, or tugging at the lace. As people repeat movements, these
elemental actions are merged into “chunks.” A new study, led by
researchers at the Rehabilitation Institute of Chicago (RIC), makes
significant advances in explaining the phenomenon of movement chunking
and has important implications for the early diagnosis, treatment and
rehabilitation therapy for patients with neurological disorders. The
field of computational motor control focuses on how the brain ought to
control movements, given its goals and resource constraints (i.e., how
the brain ought to optimize the efficiency of movement). In this
context, researchers have had difficulty explaining how people learn to
transition from computationally simple (but inefficient) movements to
those that are computationally demanding (but efficient). This study
resolves the issue by demonstrating that chunking is the natural
by–product of a physiologically clever strategy that minimizes learning
costs. The research, published in the journal Nature Communications,
presents two main findings. First, it develops a theory to explain why
chunking occurs. By measuring how the nervous system in monkeys produces
movement sequences over several days of practice, the authors found
empirical evidence that chunks occur because of a tradeoff between
efficiency and computational cost. On the one hand, the nervous system
aims to produce movements as efficiently as possible. On the other,
there is a computational cost to calculating efficient trajectories.
Chunks are the sweet spot between these goals. Second, the study
demonstrates that there are certain stages during the learning of
complex movements at which it is optimally cost–effective to merge small
chunks. The data show that monkeys are indeed cost–effective learners
whose nervous system decides when to merge chunks in an intelligent way.
Specifically, the movement sequence is divided into chunks, optimizing
for efficiency within chunks, and then merging chunks only when further
gains in efficiency are required.
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