Maurice Smith
Thomas D. Cabot
Associate Professor of Bioengineering, SEAS, Harvard University
The
dissection of motor learning using an engineering systems framework
The
ability to control movement is perhaps *the* central function of the nervous
system, and the ability to optimize this control through learning can be
absolutely essential for not only successful movement, but survival. The
human motor system, in particular, has a remarkable capacity for adaptive
control during volitional movement. I will present some recent insights
into the mechanisms by which we, as humans, achieve this adaptive control based
on analysis of the formation and maintenance of the motor memories that support
it in an engineering systems framework. I will begin by showing how the
contributions of fast and slow adaptive processes interact during
motor learning. We find that these processes display very different
learning rates and contribute to distinct memory stores that display different
modes of forgetting, that they compete against one another in adaptive
strength, and that a computational model of these competitive interactions
provides an intriguing quantitatively-accurate account of the well-known
but poorly understood advantage of trial spacing during learning. Recent
work has suggested that fast and slow learning rely on distinct neuroanatomical
underpinnings, giving new insight into neurologic diseases that affect
motor learning. We also examine the relationship between motor
variability and learning, showing how internal estimates of motor variability
dynamically regulate motor execution allowing for adaptive control that
maintains a fixed statistical confidence in the face of varying levels of
environmental variability.
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