Care and Feeding of the Internal Model
Abstract
When considering how to build a machine that learns, a reasonable
starting point is to consider how the human brain and body learns,
especially learns to solve motor tasks.
We have been conducting human subject studies using haptic interface
to virtual environments to understand how the central nervous system
uses sensory input and past experience to solve manipulation tasks and
whether internal models might be involved. We ask subjects to drive
resonant systems, to balance underactuated and unstable systems, to
anticipate changes in load while objects are lifted, and to throw
virtual balls at targets. We meter the visual and haptic feedback, use
covert condition changes that check dependence on expectations, and
occasionally back-drive the human hand and arm to determine driving
point impedance. We have found evidence that internal models are used
for integration of visual and haptic feedback and for rapid tuning of
parameters within a feedforward controller. We have shown that
training in one task can lead to performance improvements in
parametrically related tasks even without specific practice. The most
reasonable explanation for such an outcome is a mental model that is
similarly parameterized. A memory map within the brain now seems less
likely. Interestingly, the concept of a model computing somewhere
inside the brain strikes most neuroscientists as ludicrous;
nevertheless, the idea is gaining hold in motor control. In addition
to reviewing results from our lab in this talk, I will survey the
field of human motor learning, and attempt to extract implications for
the field of machine learning.
Maintained by:
Fei Sha